Validation of HOMA-IR in a model of insulin-resistance induced by a high-fat diet in Wistar rats.
Antunes, Luciana C; Elkfury, Jessica L; Jornada, Manoela N; Foletto, Kelly C; Bertoluci, Marcello C
2016-04-01
Objective The present study aimed to validate homeostasis model assessment of insulin resistance (HOMA-IR) in relation to the insulin tolerance test (ITT) in a model of insulin-resistance in Wistar rats induced by a 19-week high-fat diet. Materials and methods A total of 30 male Wistar rats weighing 200-300 g were allocated into a high-fat diet group (HFD) (55% fat-enriched chow, ad lib, n = 15) and a standard-diet group (CD) standard chow, ad lib, n = 15), for 19 weeks. ITT was determined at baseline and in the 19th week. HOMA-IR was determined between the 18-19th week in three different days and the mean was considered for analysis. Area under the curve (AUC-ITT) of the blood glucose excursion along 120 minutes after intra-peritoneal insulin injection was determined and correlated with the corresponding fasting values for HOMA-IR. Results AUC-ITT and HOMA-IR were significantly greater after 19th week in HFD compared to CD (p HOMA-IR was strongly correlated (Pearson's) with AUC-ITT r = 0.637; p HOMA-IR and AUC-ITT showed similar sensitivity and specificity. Conclusion HOMA-IR is a valid measure to determine insulin-resistance in Wistar rats. Arch Endocrinol Metab. 2016;60(2):138-42.
Lee, Da Eun; Park, Soo Yeon; Park, So Yun; Lee, Sa Ra; Chung, Hye Won; Jeong, Kyungah
2014-12-01
The aim of this study was to investigate the clinical and biochemical profiles according to homeostasis model assessment of insulin resistance (HOMA-IR) in Korean polycystic ovary syndrome (PCOS) patients. In 458 PCOS patients diagnosed by the Rotterdam European Society for Human Reproduction and Embryology (ESHRE) criteria, measurements of somatometry, blood test of hormones, glucose metabolic and lipid profiles, and transvaginal or transrectal ultrasonogram were carried out. HOMA-IR was then calculated and compared with the clinical and biochemical profiles related to PCOS. The patients were divided into 4 groups by quartiles of HOMA-IR. The mean level of HOMA-IR was 2.18 ± 1.73. Among the four groups separated according to HOMA-IR, body weight, body mass index (BMI), waist-to-hip ratio (WHR), triglyceride (TG), total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, lipid accumulation product (LAP) index, high-sensitivity C-reactive protein (hs-CRP), Apoprotein B, free testosterone, and sex hormone binding globulin (SHBG) were found to be significantly different. TG, LAP index, glucose metabolic profiles, and hs-CRP were positively correlated with HOMA-IR after adjustment for BMI. Our results suggest that the clinical and biochemical profiles which are applicable as cardiovascular risk factors are highly correlated with HOMA-IR in Korean women with PCOS.
Vogeser, Michael; König, Daniel; Frey, Ingrid; Predel, Hans-Georg; Parhofer, Klaus Georg; Berg, Aloys
2007-09-01
Lifestyle changes with increased physical activity and balanced energy intake are recognized as the principal interventions in obesity and insulin resistance. Only few prospective studies, however, have so far addressed the potential role of routine biochemical markers of insulin sensitivity in the monitoring of respective interventions. Fasting insulin and glucose was measured in 33 obese individuals undergoing a lifestyle modification program (MOBILIS) at baseline and after 1 year. The HOMA-IR index (homeostasis model of insulin resistance) was calculated as [fasting serum glucose*fasting serum insulin/22.5], with lower values indicating a higher degree of insulin sensitivity. While the median body mass index (BMI) and waist circumference decreased by 10% and 11%, respectively, the HOMA-IR index decreased in an over-proportional manner by 45% within 1 year (BMI baseline, median 35.7, interquartile range (IQR) 33.7-37.7; after 1 year, median 32.2, IQR 29.6-35.1. HOMA-IR baseline, median 2.9, IQR 1.5-4.6; after 1 year 1.6, IQR 0.9-2.7). In contrast to HOMA-IR and fasting serum insulin, no significant changes in fasting serum glucose were observed. Baseline and post-intervention HOMA-IR showed a high degree of inter-individual variation with eight individuals maintaining high HOMA-IR values despite weight loss after 1 year of intervention. Individual changes in the carbohydrate metabolism achieved by a lifestyle intervention program were displayed by fasting serum insulin concentrations and the HOMA-IR but not by fasting glucose measurement alone. Therefore, assessment of the HOMA-IR may help to individualize lifestyle interventions in obesity and to objectify improvements in insulin sensitivity after therapeutic lifestyle changes.
Sengupta, Shreejita; Jaseem, T; Ambalavanan, Jayachidambaram; Hegde, Anupama
2018-04-01
Despite various studies with conflicting results, the effect of thyroid hormones on lipids and insulin levels in dysthyroidism is of great interest. This case control study was aimed to perceive the existence of IR and dyslipidemia in mild subclinical hypothyroid subjects (TSH ≤ 9.9 µIU/ml) as compared to their age and gender matched euthyroid controls. Basic demographic information like height, weight was recorded. Serum samples of all the subjects were assayed for thyroid profile, lipid profile, blood glucose, HbA1C and insulin. BMI and insulin resistance was calculated. Compared to controls patients with mild subclinical hypothyroidism demonstrated hyperinsulinemia and dyslipidemia observed by the higher LDL cholesterol. A significantly positive correlation was observed for HOMA-IR with TSH and LDL cholesterol. Hence, even in the mild subclinical hypothyroid state assessment of thyroid function should be combined with estimation of plasma glucose, insulin and serum lipids to monitor and prevent its associated effects.
Abbasi, Fahim; Okeke, QueenDenise; Reaven, Gerald M
2014-04-01
Insulin-mediated glucose disposal varies severalfold in apparently healthy individuals, and approximately one-third of the most insulin resistant of these individuals is at increased risk to develop various adverse clinical syndromes. Since direct measurements of insulin sensitivity are not practical in a clinical setting, several surrogate estimates of insulin action have been proposed, including fasting plasma insulin (FPI) concentration and the homeostasis model assessment of insulin resistance (HOMA-IR) calculated by a formula employing fasting plasma glucose (FPG) and FPI concentrations. The objective of this study was to compare FPI as an estimate of insulin-mediated glucose disposal with values generated by HOMA-IR in 758 apparently healthy nondiabetic individuals. Measurements were made of FPG, FPI, triglyceride (TG), and high-density lipoprotein cholesterol (HDL-C) concentrations, and insulin-mediated glucose uptake was quantified by determining steady-state plasma glucose (SSPG) concentration during the insulin suppression test. FPI and HOMA-IR were highly correlated (r = 0.98, P HOMA-IR (r = 0.64). Furthermore, the relationship between FPI and TG (r = 0.35) and HDL-C (r = -0.40) was comparable to that between HOMA-IR and TG (r = 0.39) and HDL-C (r = -0.41). In conclusion, FPI and HOMA-IR are highly correlated in nondiabetic individuals, with each estimate accounting for ~40% of the variability (variance) in a direct measure of insulin-mediated glucose disposal. Calculation of HOMA-IR does not provide a better surrogate estimate of insulin action, or of its associated dyslipidemia, than measurement of FPI.
Shaban, N; Kenno, K A; Milne, K J
2014-04-01
High intensity interval training (HIIT) induces similar metabolic adaptations to traditional steady state aerobic exercise training. Until recently, most HIIT studies have examined maximum efforts in healthy populations. The current study aimed to examine the effects of a 2 week modified HIIT program on the homeostatic model of insulin resistance (HOMA-IR) in individuals with type 2 diabetes (T2D). It was hypothesized that HIIT would improve HOMA-IR. Nine individuals with T2D (age=40.2±9.7 y; BMI=33.9±5.3; fasting plasma glucose [FPG]=8.7±2.9 mmol/L; HbA1C=7.3±1.2%; [mean±SD]) performed 6 individualized training sessions of HIIT (4x30 seconds at 100% of estimated maximum workload followed by 4 minutes of active rest) over 2 weeks. HOMA-IR was calculated from FPG and serum insulin and compared against a prior 2 week baseline period. Blood glucose was reduced immediately after each HIIT session (PHOMA-IR were unchanged after training. However, 6 of the 9 individuals exhibited reduced HOMA-IR values after the training period and there was a significant negative correlation between HOMA-IR value prior to training and change in HOMA-IR after HIIT. These observations tend to support the positive health benefits of HITT for individuals with T2D reported in recently published data using a modified HIIT protocol. However, they suggest that the magnitude of the disease should be assessed when examining the effects of exercise interventions in individuals with T2D.
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Majid, H.; Khan, A.H.; Masood, Q.
2017-01-01
To assess the utility of HOMA-IR in assessing insulin resistance in patients with polycystic ovary syndrome (PCOS) and compare it with fasting insulin for assessing insulin resistance (IR). Study Design: Observational study. Place and Duration of Study: Section of Clinical Chemistry, Department of Pathology and Laboratory Medicine, The Aga Khan University Hospital, Karachi, from January 2009 to September 2012. Methodology: Medical chart review of all women diagnosed with PCOS was performed. Of the 400 PCOS women reviewed, 91 met the inclusion criteria. Insulin resistance was assessed by calculating HOMA-IR using the formula (fasting glucose x fasting insulin)/405, taking normal value =12 micro IU/ml. Results: A total of 91 premenopausal women diagnosed with PCOS were included. Mean age was 30 +-5.5 years. Mean HOMA-IR of women was 3.1 +-1.7, respectively with IR in 69% (n=63) women, while hyperinsulinemia was present in 60% (n=55) women (fasting Insulin 18.5 +-5.8 micro IU/ml). Hyperandrogenism was present in 53.8% (n=49), whereas 38.5% (n=35) women had primary infertility or subfertility, while 65.9% (n=60) had menstrual irregularities; and higher frequencies were observed in women with IR. Eight subjects with IR and endocrine abnormalities were missed by fasting insulin. Conclusion: Insulin resistance is common in PCOS and it is likely a pathogenic factor for development of PCOS. HOMA-IR model performed better than hyperinsulinemia alone for diagnosing IR. (author)
Ying, X; Song, Zh; Zhao, Ch; Jiang, Y
2011-01-01
To investigate the prevalence of metabolic syndrome (MetS) in young Chinese population and assess the association between HOMA-IR and different components of MetS in young Chinese men. Overall 5576 young Chinese subjects (age range [19-44 yr], 3636 men) were enrolled in, who visited our Health Care Center for a related health checkup from March to December 2008. The international diabetes federation (IDF) definition for MetS was used. The SPSS statistical package, version 11.5 was used for the statistical analysis. The prevalence of MetS was 21.81% in young men and 5.62% in young women. According to suffering from different numbers of MetS components, the male subjects were divided into four groups. Numbers of MetS components were more and HOMA-IR values were significantly higher. In this male population, the quartile of HOMA-IR was higher, values of triglyceride (TG), fasting plasma glucose (FBG), systolic blood pressure(SBP), diastolic blood pressure(DBP) and waist circumference (WC) were all significantly higher, as well as high density lipoprotein cholesterol (HDL-C) value was significantly lower (P= 0.000). In Spearman's correlation analysis, HOMA-IR was positively correlated with TG, FBG, SBP, DBP and WC, and negatively correlated with HDL-C (r= 0.460, 0.464, 0.362, 0.346, 0.586, -0.357, respectively, all P value= 0.000). The prevalence of MetS in these young Chinese men was obviously high. Insulin resistance played an important role in occurrence and development of MetS. Waist circumference was the best correlation with HOMA-IR among all components of MetS.
Burrows, Raquel A; Leiva, Laura B; Weisstaub, Gerardo; Lera, Lydia M; Albala, Cecilia B; Blanco, Estela; Gahagan, Sheila
2011-05-01
To determine how the homeostasis model assessment of insulin resistance (HOMA-IR) is related to metabolic risk in a sample of overweight and obese Chilean youths accounting for Tanner stage. A cross-sectional study assessing 486 overweight and obese youths (aged 5-15 years) recruited from the University of Chile, Pediatric Obesity Clinic. We measured anthropometry, Tanner stage, HOMA-IR, and laboratory tests related to metabolic risk. HOMA-IR was categorized by quartile for children (Tanner stages I and II) and adolescents (Tanner stage III and above) from a normative Chilean sample. Children and adolescents with HOMA-IR in the highest quartile were likely to have higher body mass index (BMI) Z-scores, elevated waist circumference, systolic and diastolic blood pressure, and triglycerides and low high-density lipoprotein. HOMA-IR had good negative predictive value for characteristics of the metabolic syndrome (MetS; 0.82). In a multivariate regression model, BMI Z-score [odds ratio (OR) 1.5] and HOMA-IR (OR 3.3) predicted 22% of the variance for the MetS, with 36% of the explained variance attributed to HOMA-IR. In a large clinical sample of overweight and obese Chilean youths, HOMA-IR ≥ 75th percentile was significantly associated with the cluster of factors referred to as the MetS. We emphasize the importance of establishing percentiles for HOMA-IR based on a normative sample and taking Tanner stage into account. Although BMI is easy to assess and interpret with minimal costs in a clinical setting, adding HOMA-IR explains more of the variance in the MetS than BMI Z-score alone. © 2011 John Wiley & Sons A/S.
Sarafidis, P A; Lasaridis, A N; Nilsson, P M; Pikilidou, M I; Stafilas, P C; Kanaki, A; Kazakos, K; Yovos, J; Bakris, G L
2007-09-01
The aim of this study was to evaluate the validity and reliability of homeostasis model assessment-insulin resistance (HOMA-IR) index, its reciprocal (1/HOMA-IR), quantitative insulin sensitivity check index (QUICKI) and McAuley's index in hypertensive diabetic patients. In 78 patients with hypertension and type II diabetes glucose, insulin and triglyceride levels were determined after a 12-h fast to calculate these indices, and insulin sensitivity (IS) was measured with the hyperinsulinemic euglycemic clamp technique. Two weeks later, subjects had again their glucose, insulin and triglycerides measured. Simple and multiple linear regression analysis were applied to assess the validity of these indices compared to clamp IS and coefficients of variation between the two visits were estimated to assess their reproducibility. HOMA-IR index was strongly and inversely correlated with the basic IS clamp index, the M-value (r=-0.572, PHOMA-IR and QUICKI indices were positively correlated with the M-value (r=0.342, PHOMA-IR was the best fit of clamp-derived IS. Coefficients of variation between the two visits were 23.5% for HOMA-IR, 19.2% for 1/HOMA-IR, 7.8% for QUICKI and 15.1% for McAuley's index. In conclusion, HOMA-IR, 1/HOMA-IR and QUICKI are valid estimates of clamp-derived IS in patients with hypertension and type II diabetes, whereas the validity of McAuley's index needs further evaluation. QUICKI displayed better reproducibility than the other indices.
Majid, Hafsa; Masood, Qamar; Khan, Aysha Habib
2017-03-01
To assess the utility of HOMA-IR in assessing insulin resistance in patients with polycystic ovary syndrome (PCOS) and compare it with fasting insulin for assessing insulin resistance (IR). Observational study. Section of Clinical Chemistry, Department of Pathology and Laboratory Medicine, The Aga Khan University Hospital, Karachi, from January 2009 to September 2012. Medical chart review of all women diagnosed with PCOS was performed. Of the 400 PCOS women reviewed, 91 met the inclusion criteria. Insulin resistance was assessed by calculating HOMA-IR using the formula (fasting glucose x fasting insulin)/405, taking normal value HOMA-IR of women was 3.1 ±1.7, respectively with IR in 69% (n=63) women, while hyperinsulinemia was present in 60% (n=55) women (fasting Insulin 18.5 ±5.8 µIU/ml). Hyperandrogenism was present in 53.8% (n=49), whereas 38.5% (n=35) women had primary infertility or subfertility, while 65.9% (n=60) had menstrual irregularities; and higher frequencies were observed in women with IR. Eight subjects with IR and endocrine abnormalities were missed by fasting insulin. Insulin resistance is common in PCOS and it is likely a pathogenic factor for development of PCOS. HOMAIR model performed better than hyperinsulinemia alone for diagnosing IR.
Morimoto, Akiko; Tatsumi, Yukako; Soyano, Fumie; Miyamatsu, Naomi; Sonoda, Nao; Godai, Kayo; Ohno, Yuko; Noda, Mitsuhiko; Deura, Kijyo
2014-01-01
Our aim was to assess the impact of increase in homeostasis model assessment of insulin resistance (HOMA-IR) on the development of type 2 diabetes in Japanese individuals with impaired insulin secretion (IIS). This study included 2,209 participants aged 30-69 without diabetes at baseline who underwent comprehensive medical check-ups between April 2006 and March 2007 at Saku Central Hospital. Participants were classified into eight groups according to the combination of baseline IIS status (non-IIS and IIS) and category of HOMA-IR change between the baseline and follow-up examinations (decrease, no change/small increase, moderate increase, and large increase). Type 2 diabetes was determined from fasting and 2 h post-load plasma glucose concentrations at the follow-up examination between April 2009 and March 2011. At baseline, 669 individuals (30.3%) were classified as having IIS. At follow-up, 74 individuals developed type 2 diabetes. After adjusting for confounding factors including baseline HOMA-IR values, the multivariable-adjusted odds ratios (95% confidence intervals) for type 2 diabetes in the non-IIS with a decrease (mean change in HOMA-IR: -0.47), non-IIS with a moderate increase (mean change in HOMA-IR: 0.28), non-IIS with a large increase (mean change in HOMA-IR: 0.83), IIS with a decrease (mean change in HOMA-IR: -0.36), IIS with no change/small increase (mean change in HOMA-IR: 0.08), IIS with a moderate increase (mean change in HOMA-IR: 0.27), and IIS with a large increase (mean change in HOMA-IR: 0.73) groups, relative to the non-IIS with no change/small increase (mean change in HOMA-IR: 0.08) group were 0.23 (0.04, 1.11), 1.22 (0.26, 5.72), 2.01 (0.70, 6.46), 1.37 (0.32, 4.28), 3.60 (0.83, 15.57), 5.24 (1.34, 20.52), and 7.01 (1.75, 24.18), respectively. Moderate and large increases in HOMA-IR had a strong impact on the development of type 2 diabetes among individuals with IIS in this Japanese population.
Alptekin, Hüsnü; Çizmecioğlu, Ahmet; Işık, Hatice; Cengiz, Türkan; Yildiz, Murat; Iyisoy, Mehmet Sinan
2016-05-01
To determine the predictability of gestational diabetes mellitus (GDM) during the first trimester using the degree of insulin resistance and anthropometric measurements and to assign the risk of developing GDM by weight gained during pregnancy (WGDP). A total of 250 singleton pregnancies at 7-12 gestational weeks were studied. Body mass index (BMI), waist/hip ratio (WHR), quantitative insulin sensitivity check index (QUICKI), homeostasis model assessment-insulin resistance (HOMA-IR) scores and WGDP were determined. The backward stepwise method was applied to estimate possible associations with GDM. Cutoff points were estimated using receiver operating characteristic curve analysis. GDM was found in 20 of 227 singleton pregnancies (8.8 %). The calculated HOMA-IR, QUICKI, BMI, WHR, WGDP, and parity were significantly associated with GDM. Logistic regression analyses showed that three covariates (HOMA-IR, BMI, WGDP) remained independently associated with GDM. It was calculated as OR 1.254 (95 % CI 1.006-1.563), AUC 0.809, sensitivity 90 %, specificity 61 % with cutoff = 2.08 for HOMA-IR; OR 1.157 (CI 1.045-1.281), AUC 0.723, sensitivity 80 %, specificity 58 % with cutoff = 25.95 for BMI; OR 1.221, (CI 1.085-1.374), AUC 0.654, sensitivity 80 %, specificity 46 % with cutoff = 4.7 for WGDP. Despite a HOMA-IR score of >3.1 in pregnant women, GDM was detected in only three of 29 patients (10.3 %) if WGDP was HOMA-IR. In particular, if BMI is >25.95 kg/m(2) and the HOMA-IR score >2.08, controlling weight gain may protect against GDM.
Gayoso-Diz, Pilar; Otero-Gonzalez, Alfonso; Rodriguez-Alvarez, María Xosé; Gude, Francisco; Cadarso-Suarez, Carmen; García, Fernando; De Francisco, Angel
2011-10-01
To describe the distribution of HOMA-IR levels in a general nondiabetic population and its relationships with metabolic and lifestyles characteristics. Cross-sectional study. Data from 2246 nondiabetic adults in a random Spanish population sample, stratified by age and gender, were analyzed. Assessments included a structured interview, physical examination, and blood sampling. Generalized additive models (GAMs) were used to assess the effect of lifestyle habits and clinical and demographic measurements on HOMA-IR. Multivariate GAMs and quantile regression analyses of HOMA-IR were carried out separately in men and women. This study shows refined estimations of HOMA-IR levels by age, body mass index, and waist circumference in men and women. HOMA-IR levels were higher in men (2.06) than women (1.95) (P=0.047). In women, but not men, HOMA-IR and age showed a significant nonlinear association (P=0.006), with increased levels above fifty years of age. We estimated HOMA-IR curves percentile in men and women. Age- and gender-adjusted HOMA-IR levels are reported in a representative Spanish adult non-diabetic population. There are gender-specific differences, with increased levels in women over fifty years of age that may be related with changes in body fat distribution after menopause. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.
Qu, Hui-Qi; Li, Quan; Rentfro, Anne R; Fisher-Hoch, Susan P; McCormick, Joseph B
2011-01-01
The lack of standardized reference range for the homeostasis model assessment-estimated insulin resistance (HOMA-IR) index has limited its clinical application. This study defines the reference range of HOMA-IR index in an adult Hispanic population based with machine learning methods. This study investigated a Hispanic population of 1854 adults, randomly selected on the basis of 2000 Census tract data in the city of Brownsville, Cameron County. Machine learning methods, support vector machine (SVM) and Bayesian Logistic Regression (BLR), were used to automatically identify measureable variables using standardized values that correlate with HOMA-IR; K-means clustering was then used to classify the individuals by insulin resistance. Our study showed that the best cutoff of HOMA-IR for identifying those with insulin resistance is 3.80. There are 39.1% individuals in this Hispanic population with HOMA-IR>3.80. Our results are dramatically different using the popular clinical cutoff of 2.60. The high sensitivity and specificity of HOMA-IR>3.80 for insulin resistance provide a critical fundamental for our further efforts to improve the public health of this Hispanic population.
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. Sunarti
2017-01-01
Full Text Available Low expression of insulin receptor substrate 1 (Irs1 is associated with insulin resistance and type 2 diabetes mellitus (type 2 DM. This study was performed to evaluate the effects of Dioscorea esculenta and Eubacterium rectale on the Irs1 expression in the skeletal muscle and the homeostatic model assessment-insulin resistance (HOMA-IR of diabetic rats. Twenty-five male Wistar rats were divided into five groups i.e. non diabetic rats Group 1; diabetic rats as Group 2; diabetic rats + D. esculenta as Group 3; diabetic rats + E.rectale as Group 4 and diabetic rats + both E. rectale and D. esculenta as Group 5. Rats were made diabetic with induction of intraperitoneally injection of nicotinamide and streptozotocin. After four weeks of the interventions, the blood and skeletal muscles were taken. The Irs1 expression was analyzed with immunohistochemical staining, plasma glucose levels was analyzed using a spectrophotometer, and insulin was analyzed using ELISA methods. All intervention groups reduced plasma glucose levels and HOMA-IRs (p<0.001 and increased Irs1 expression. The greatest reduction of plasma glucose levels and increase of Irs1 expression in the skeletal muscle were found in Group 4, however, the lowest of HOMA-IR was seen in Group 5. These results suggested that D.esculenta, E.rectale, and the combination reduced plasma glucose levels and HOMA-IR by increasing Irs1 expression in skeletal muscle.
Relationship between HOMA-IR and serum vitamin D in Chinese children and adolescents.
Wang, Lingli; Wang, Huiyan; Wen, Huaikai; Tao, Hongqun; Zhao, Xiaowei
2016-07-01
The objective of this study was to examine the cross-sectional relationship between homeostasis model assessment for insulin resistance (HOMA-IR) and serum 25-hydroxyvitamin D (25-OHD) level in Chinese children and adolescents. Anthropometric indices, lipid metabolic profile, and serum levels of glucose, insulin and 25-OHD were determined among 278 healthy prepubertal and pubertal, normal and overweight/obese children and adolescents aged 8-18 years between March 2014 and February 2015. HOMA-IR was significantly different across vitamin D statuses (pHOMA-IR negatively correlated with serum 25-OHD level for all subjects (R2=0.148, pHOMA-IR and BMI and serum 25-OHD level (R2=0.654, pHOMA-IR. Our findings supported that lower vitamin D status is strongly associated with worse HOMA-IR.
Lee, C H; Shih, A Z L; Woo, Y C; Fong, C H Y; Leung, O Y; Janus, E; Cheung, B M Y; Lam, K S L
The optimal reference range of homeostasis model assessment of insulin resistance (HOMA-IR) in normal Chinese population has not been clearly defined. Here we address this issue using the Hong Kong Cardiovascular Risk Factor Prevalence Study (CRISPS), a prospective population-based cohort study with long-term follow-up. In this study, normal glucose tolerance (NGT), impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and type 2 diabetes mellitus (T2DM) were defined according to the 1998 World Health Organization criteria. Dysglycemia referred to IFG, IGT or T2DM. This study comprised two parts. Part one was a cross-sectional study involving 2,649 Hong Kong Chinese subjects, aged 25-74 years, at baseline CRISPS-1 (1995-1996). The optimal HOMA-IR cut-offs for dysglycemia and T2DM were determined by the receiver-operating characteristic (ROC) curve. Part two was a prospective study involving 872 subjects who had persistent NGT at CRISPS-4 (2010-2012) after 15 years of follow-up. At baseline, the optimal HOMA-IR cut-offs to identify dysglyceia and T2DM were 1.37 (AUC = 0.735; 95% confidence interval [CI] = 0.713-0.758; Sensitivity [Se] = 65.6%, Specificity [Sp] = 71.3%] and 1.97 (AUC = 0.807; 95% CI = 0.777-0.886; Se = 65.5%, Sp = 82.9%) respectively. These cut-offs, derived from the cross-sectional study at baseline, corresponded closely to the 75th (1.44) and 90th (2.03) percentiles, respectively, of the HOMA-IR reference range derived from the prospective study of subjects with persistent NGT. HOMA-IR cut-offs, of 1.4 and 2.0, which discriminated dysglycemia and T2DM respectively from NGT in Southern Chinese, can be usefully employed as references in clinical research involving the assessment of insulin resistance.
Tang, Qi; Li, Xueqin; Song, Peipei; Xu, Lingzhong
2015-12-01
Diabetes mellitus (DM) appears to be increasing rapidly, threatening to reduce life expectancy for humans around the globe. The International Diabetes Federation (IDF) has estimated that there will be 642 million people living with the disease by 2040 and half as many again who will be not diagnosed. This means that pre-DM screening is a critical issue. Insulin resistance (IR) has emerged as a major pathophysiological factor in the development and progression of DM since it is evident in susceptible individuals at the early stages of DM, and particularly type 2 DM (T2DM). Therefore, assessment of IR via the homeostasis model assessment of IR (HOMA-IR) is a key index for the primary prevention of DM and is thus found in guidelines for screening of high-risk groups. However, the cut-off values of HOMA-IR differ for different races, ages, genders, diseases, complications, etc. due to the complexity of IR. This hampers the determination of specific cut-off values of HOMA-IR in different places and in different situations. China has not published an official index to gauge IR for primary prevention of T2DM in the diabetic and non-diabetic population except for children and adolescents ages 6-12 years. Hence, this article summarizes developments in research on IR, HOMA-IR, and pre-DM screening in order to provide a reference for optimal cut-off values of HOMA-IR for the diagnosis of DM in the Chinese population.
Higher HOMA-IR index and correlated factors of insulin resistance in patients with IgA nephropathy.
Yang, Yue; Wei, Ri-Bao; Wang, Yuan-da; Zhang, Xue-Guang; Rong, Na; Tang, Li; Chen, Xiang-Mei
2012-11-01
To investigate the index of homeostasis model of insulin resistance (HOMA-IR) in IgA nephropathy (IgAN) patients, and to explore the possible correlated factors contributing to insulin resistance (IR) within these patients. There were 255 IgAN patients and 45 membranous nephropathy (MN) patients in our database. We identified 89 IgAN subjects and 21 MN subjects without diabetes and undergoing glucocorticoid therapy for at least 6 months. Data regarding physical examination, blood chemistry and renal pathology were collected from 89 IgAN subjects and 21 MN subjects. Then 62 IgAN patients and 19 MN patients with chronic kidney disease (CKD) Stage 1 - 2 were selected for the comparison of HOMA-IR index, 89 IgAN patients were selected for multiple regression analysis to test for correlated factors of HOMA-IR index with IgAN patients. Comparison between IgAN and MN show that HOMA-IR index was significantly higher in IgAN patients with CKD Stage 1 - 2. After logarithmic transformation with urine protein (UPr), Ln(UPr) (b = 0.186, p = 0.008), eGFR (b = -0.005, p = 0.014), > 50% of glomeruli with mesangial hypercellularity (b = 0.285, p = 0.027) and body mass index (BMI) (b = 0.039, p = 0.008) were correlated factors of HOMA-IR index in the multiple regression analysis. IgAN patients had higher HOMA-IR index compared with MN in the stages of CKD 1 - 2. For IgAN patients, more UPr, lower eGFR, > 50% of glomeruli with mesangial hypercellularity and higher BMI were correlated with IR.
Alebić, Miro Šimun; Bulum, Tomislav; Stojanović, Nataša; Duvnjak, Lea
2014-11-01
Polycystic ovary syndrome (PCOS) women are more insulin resistant than general population. Prevalence data on insulin resistance (IR) in PCOS vary depending on population characteristics and methodology used. The objectives of this study were to investigate whether IR in PCOS is exclusively associated with body mass and to assess the prevalence of IR in lean and overweight/obese PCOS. Study included 250 consecutive women who attended a Department of Human Reproduction diagnosed as having PCOS according to the Rotterdam criteria. Control group comprised 500 healthy women referred for male factor infertility evaluation during the same period as the PCOS women. PCOS women (n = 250) were more insulin resistant than controls (n = 500) even after adjustment for age and body mass index (BMI) (P = 0.03). Using logistic regression analysis, BMI ≥ 25 kg/m(2) (OR 6.0; 95 % CI 3.3-11.0), PCOS (OR 2.2; 95 % CI 1.4-3.5) and waist circumference ≥ 80 cm (OR 2.0; 95 % CI 1.1-3.8) were identified as independent determinants of IR (P IR was more prevalent in overweight/obese controls (n = 100) than in lean PCOS women (n = 150), 31 versus 9.3 %, but less prevalent than in overweight/obese PCOS (n = 100), 31 versus 57 %. The prevalence of IR between lean controls (5 %) and lean PCOS (9.3 %) did not significantly differ. Both PCOS-specific and obesity-related IR independently contribute to IR in PCOS. Using HOMA-IR cutoff value of 3.15 specific for Croatian women in our clinical setting, the assessed prevalence of IR in lean and overweight/obese PCOS women was 9.3 and 57 %, respectively.
Boyer, William R; Johnson, Tammie M; Fitzhugh, Eugene C; Richardson, Michael R; Churilla, James R
2016-03-01
The purpose of this study was to examine the associations between increasing degrees of insulin resistance (using the homeostatic model assessment of insulin resistance [HOMA-IR]) and two measures of adiposity in a nationally representative sample of euglycemic U.S. adults. Sample included adult participants (≥ 20 years of age) [N = 1586 (body mass index, BMI model), N = 1577 (waist circumference, WC model)] from the 1999-2004 National Health and Nutrition Examination Survey (NHANES). HOMA-IR was categorized into quartiles. BMI and WC were examined continuously as the dependent variables. Following adjustment for covariates, those with HOMA-IR values in the second, third, and fourth quartiles had significantly higher BMIs (P HOMA-IR (P HOMA-IR and BMI (R(2) = 0.4171, P HOMA-IR and WC (R(2) = 0.4826, P HOMA-IR value is associated with higher BMI and WC values in euglycemic subjects.
Birth-weight, insulin levels, and HOMA-IR in newborns at term
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Simental-Mendía Luis E
2012-07-01
Full Text Available Abstract Background Recent studies have demonstrated that low and high birth-weight at birth are risk factors of developing diabetes. The aim of this study was to determine if the abnormal birth-weight is related with hyperinsulinemia and elevated index of the Homeostasis Model assessment for Insulin Resistance (HOMA-IR at birth, in at term newborns. Methods Newborns with gestational age between 38 and 41 weeks, products of normal pregnancies of healthy mothers aged 18 to 39 years, were eligible to participate. Small-for-gestational age (SGA and large-for-gestational age (LGA newborns were compared with appropriate-for-gestational (AGA age newborns. Incomplete or unclear data about mother’s health status, diabetes, gestational diabetes, history of gestational diabetes, hypertension, pre-eclampsia, eclampsia, and other conditions that affect glucose metabolism were exclusion criteria. Hyperinsulinemia was defined by serum insulin levels ≥13.0 μU/mL and IR by HOMA-IR ≥2.60. Multiple logistic regression analysis was used to determine the odds ratio (OR that computes the association between birth-weight (independent variable with hyperinsulinemia and HOMA-IR index (dependent variables. Results A total of 107 newborns were enrolled; 13, 22, and 72 with SGA, LGA, and AGA, respectively. Hyperinsulinemia was identified in 2 (15.4%, 6 (27.3%, and 5 (6.9% with SGA, LGA, and AGA (p=0.03, whereas IR in 3 (23.1%, 8 (36.4%, and 10 (13.9% newborns with SGA, LGA and AGA (p=0.06. The LGA showed a strong association with hyperinsulinemia (OR 5.02; CI 95%, 1.15-22.3; p=0.01 and HOMA-IR (OR 3.54; CI 95%, 1.03-12.16; p=0.02; although without statistical significance, the SGA showed a tendency of association with hyperinsulinemia (OR 2.43; CI 95%, 0.43-17.3 p=0.29 and HOMA-IR (OR 1.86; CI 95%, 0.33-9.37; p=0.41. Conclusions Our results suggest that LGA is associated with hyperinsulinemia and elevated HOMA-IR at birth whereas the SGA show a tendency of
Birth-weight, insulin levels, and HOMA-IR in newborns at term.
Simental-Mendía, Luis E; Castañeda-Chacón, Argelia; Rodríguez-Morán, Martha; Guerrero-Romero, Fernando
2012-07-07
Recent studies have demonstrated that low and high birth-weight at birth are risk factors of developing diabetes. The aim of this study was to determine if the abnormal birth-weight is related with hyperinsulinemia and elevated index of the Homeostasis Model assessment for Insulin Resistance (HOMA-IR) at birth, in at term newborns. Newborns with gestational age between 38 and 41 weeks, products of normal pregnancies of healthy mothers aged 18 to 39 years, were eligible to participate. Small-for-gestational age (SGA) and large-for-gestational age (LGA) newborns were compared with appropriate-for-gestational (AGA) age newborns. Incomplete or unclear data about mother's health status, diabetes, gestational diabetes, history of gestational diabetes, hypertension, pre-eclampsia, eclampsia, and other conditions that affect glucose metabolism were exclusion criteria. Hyperinsulinemia was defined by serum insulin levels ≥13.0 μU/mL and IR by HOMA-IR ≥2.60. Multiple logistic regression analysis was used to determine the odds ratio (OR) that computes the association between birth-weight (independent variable) with hyperinsulinemia and HOMA-IR index (dependent variables). A total of 107 newborns were enrolled; 13, 22, and 72 with SGA, LGA, and AGA, respectively. Hyperinsulinemia was identified in 2 (15.4%), 6 (27.3%), and 5 (6.9%) with SGA, LGA, and AGA (p=0.03), whereas IR in 3 (23.1%), 8 (36.4%), and 10 (13.9%) newborns with SGA, LGA and AGA (p=0.06). The LGA showed a strong association with hyperinsulinemia (OR 5.02; CI 95%, 1.15-22.3; p=0.01) and HOMA-IR (OR 3.54; CI 95%, 1.03-12.16; p=0.02); although without statistical significance, the SGA showed a tendency of association with hyperinsulinemia (OR 2.43; CI 95%, 0.43-17.3 p=0.29) and HOMA-IR (OR 1.86; CI 95%, 0.33-9.37; p=0.41). Our results suggest that LGA is associated with hyperinsulinemia and elevated HOMA-IR at birth whereas the SGA show a tendency of association.
Gayoso-Diz, Pilar; Otero-González, Alfonso; Rodriguez-Alvarez, María Xosé; Gude, Francisco; García, Fernando; De Francisco, Angel; Quintela, Arturo González
2013-10-16
Insulin resistance has been associated with metabolic and hemodynamic alterations and higher cardio metabolic risk. There is great variability in the threshold homeostasis model assessment of insulin resistance (HOMA-IR) levels to define insulin resistance. The purpose of this study was to describe the influence of age and gender in the estimation of HOMA-IR optimal cut-off values to identify subjects with higher cardio metabolic risk in a general adult population. It included 2459 adults (range 20-92 years, 58.4% women) in a random Spanish population sample. As an accurate indicator of cardio metabolic risk, Metabolic Syndrome (MetS), both by International Diabetes Federation criteria and by Adult Treatment Panel III criteria, were used. The effect of age was analyzed in individuals with and without diabetes mellitus separately. ROC regression methodology was used to evaluate the effect of age on HOMA-IR performance in classifying cardio metabolic risk. In Spanish population the threshold value of HOMA-IR drops from 3.46 using 90th percentile criteria to 2.05 taking into account of MetS components. In non-diabetic women, but no in men, we found a significant non-linear effect of age on the accuracy of HOMA-IR. In non-diabetic men, the cut-off values were 1.85. All values are between 70th-75th percentiles of HOMA-IR levels in adult Spanish population. The consideration of the cardio metabolic risk to establish the cut-off points of HOMA-IR, to define insulin resistance instead of using a percentile of the population distribution, would increase its clinical utility in identifying those patients in whom the presence of multiple metabolic risk factors imparts an increased metabolic and cardiovascular risk. The threshold levels must be modified by age in non-diabetic women.
Hirata, Takumi; Higashiyama, Aya; Kubota, Yoshimi; Nishimura, Kunihiro; Sugiyama, Daisuke; Kadota, Aya; Nishida, Yoko; Imano, Hironori; Nishikawa, Tomofumi; Miyamatsu, Naomi; Miyamoto, Yoshihiro; Okamura, Tomonori
2015-01-01
Several studies have reported that insulin resistance was a major risk factor for the onset of type 2 diabetes mellitus in individuals without diabetes or obesity. We aimed to clarify the association between insulin resistance and glycemic control in Japanese subjects without diabetes or obesity. We conducted a community-based cross-sectional study including 1083 healthy subjects (323 men and 760 women) in an urban area. We performed multivariate regression analyses to estimate the association between the homeostasis model assessment of insulin resistance (HOMA-IR) values and markers of glycemic control, including glycated haemoglobin (HbA1c), 1,5-anhydroglucitol (1,5-AG), and fasting plasma glucose (FPG) levels, after adjustment for potential confounders. Compared with the lowest tertile of HOMA-IR values, the highest tertile was significantly associated with HbA1c and FPG levels after adjustment for potential confounders, both in men (HbA1c: β = 1.83, P = 0.001; FPG: β = 0.49, P HOMA-IR values was inversely associated with 1,5-AG levels compared with the lowest tertile (β = -18.42, P = 0.009) only in men. HOMA-IR values were associated with markers of glycemic control in Japanese subjects without diabetes or obesity. Insulin resistance may influence glycemic control even in a lean, non-diabetic Asian population.
Furugen, M; Saitoh, S; Ohnishi, H; Akasaka, H; Mitsumata, K; Chiba, M; Furukawa, T; Miyazaki, Y; Shimamoto, K; Miura, T
2012-05-01
Here we examined whether the Matsuda-DeFronzo insulin sensitivity index (ISI-M) is more efficient than the homeostasis model assessment of insulin resistance (HOMA-IR) for assessing risk of hypertension. Cross-sectional and longitudinal analyses were conducted using normotensive subjects who were selected among 1399 subjects in the Tanno-Sobetsu cohort. In the cross-sectional analysis (n=740), blood pressure (BP) level was correlated with HOMA-IR and with ISI-M, but correlation coefficients indicate a tighter correlation with ISI-M. Multiple linear regression analysis adjusted by age, sex, body mass index (BMI) and serum triglyceride level (TG) showed contribution of ISI-M and fasting plasma glucose, but not of HOMA-IR. In the longitudinal analysis (n=607), 241 subjects (39.7%) developed hypertension during a 10-year follow-up period, and multiple logistic regression indicated that age, TG, systolic BP and ISI-M, but not HOMA-IR, were associated with development of hypertension. In subjects HOMA-IR. Non-hepatic IR may be a determinant, which is independent of TG, BP level and BMI, of the development of hypertension.
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R. Burrows
2015-01-01
Full Text Available Objective. To determine the optimal cutoff of the homeostasis model assessment-insulin resistance (HOMA-IR for diagnosis of the metabolic syndrome (MetS in adolescents and examine whether insulin resistance (IR, determined by this method, was related to genetic, biological, and environmental factors. Methods. In 667 adolescents (16.8 ± 0.3 y, BMI, waist circumference, glucose, insulin, adiponectin, diet, and physical activity were measured. Fat and fat-free mass were assessed by dual-energy X-ray absorptiometry. Family history of type 2 diabetes (FHDM was reported. We determined the optimal cutoff of HOMA-IR to diagnose MetS (IDF criteria using ROC analysis. IR was defined as HOMA-IR values above the cutoff. We tested the influence of genetic, biological, and environmental factors on IR using logistic regression analyses. Results. Of the participants, 16% were obese and 9.4 % met criteria for MetS. The optimal cutoff for MetS diagnosis was a HOMA-IR value of 2.6. Based on this value, 16.3% of participants had IR. Adolescents with IR had a significantly higher prevalence of obesity, abdominal obesity, fasting hyperglycemia, and MetS compared to those who were not IR. FHDM, sarcopenia, obesity, and low adiponectin significantly increased the risk of IR. Conclusions. In adolescents, HOMA-IR ≥ 2.6 was associated with greater cardiometabolic risk.
Burrows, R; Correa-Burrows, P; Reyes, M; Blanco, E; Albala, C; Gahagan, S
2015-01-01
To determine the optimal cutoff of the homeostasis model assessment-insulin resistance (HOMA-IR) for diagnosis of the metabolic syndrome (MetS) in adolescents and examine whether insulin resistance (IR), determined by this method, was related to genetic, biological, and environmental factors. In 667 adolescents (16.8 ± 0.3 y), BMI, waist circumference, glucose, insulin, adiponectin, diet, and physical activity were measured. Fat and fat-free mass were assessed by dual-energy X-ray absorptiometry. Family history of type 2 diabetes (FHDM) was reported. We determined the optimal cutoff of HOMA-IR to diagnose MetS (IDF criteria) using ROC analysis. IR was defined as HOMA-IR values above the cutoff. We tested the influence of genetic, biological, and environmental factors on IR using logistic regression analyses. Of the participants, 16% were obese and 9.4 % met criteria for MetS. The optimal cutoff for MetS diagnosis was a HOMA-IR value of 2.6. Based on this value, 16.3% of participants had IR. Adolescents with IR had a significantly higher prevalence of obesity, abdominal obesity, fasting hyperglycemia, and MetS compared to those who were not IR. FHDM, sarcopenia, obesity, and low adiponectin significantly increased the risk of IR. In adolescents, HOMA-IR ≥ 2.6 was associated with greater cardiometabolic risk.
Simental-Mendía, Luis E; Castañeda-Chacón, Argelia; Rodriguez-Morán, Martha; Aradillas-García, Celia; Guerrero-Romero, Fernando
2015-05-01
To test the hypothesis that mildly elevated triglyceride levels are associated with the increase of homeostasis model assessment of insulin resistance (HOMA-IR) and β-cell function (HOMA-β) indices in healthy children and adolescents with normal weight, we conducted a cross-sectional population study. Based on fasting triglyceride levels, participants were allocated into groups with and without triglyceride levels ≥1.2 mmol/L. Normal weight was defined by body mass index between the 15th and 85th percentiles, for age and gender. Insulin resistance and insulin secretion were estimated using HOMA-IR and HOMA-β indices. A total of 1660 children and adolescents were enrolled, of them 327 (19.7%) with mildly elevated triglycerides. The multivariate linear regression analysis showed that mildly elevated triglyceride levels in children were associated with HOMA-IR (β = 0.214, p HOMA-β (β = 0.139, p = 0.001), systolic (β = 0.094, p = 0.01), and diastolic blood pressure (β = 0.102, p = 0.007), whereas in adolescents, HOMA-IR (β = 0.267, p HOMA-β (β = 0.154, p HOMA-IR and HOMA-β indices in healthy children and adolescents with normal weight.
Esteghamati, Alireza; Ashraf, Haleh; Khalilzadeh, Omid; Zandieh, Ali; Nakhjavani, Manouchehr; Rashidi, Armin; Haghazali, Mehrdad; Asgari, Fereshteh
2010-04-07
We have recently determined the optimal cut-off of the homeostatic model assessment of insulin resistance for the diagnosis of insulin resistance (IR) and metabolic syndrome (MetS) in non-diabetic residents of Tehran, the capital of Iran. The aim of the present study is to establish the optimal cut-off at the national level in the Iranian population with and without diabetes. Data of the third National Surveillance of Risk Factors of Non-Communicable Diseases, available for 3,071 adult Iranian individuals aging 25-64 years were analyzed. MetS was defined according to the Adult Treatment Panel III (ATPIII) and International Diabetes Federation (IDF) criteria. HOMA-IR cut-offs from the 50th to the 95th percentile were calculated and sensitivity, specificity, and positive likelihood ratio for MetS diagnosis were determined. The receiver operating characteristic (ROC) curves of HOMA-IR for MetS diagnosis were depicted, and the optimal cut-offs were determined by two different methods: Youden index, and the shortest distance from the top left corner of the curve. The area under the curve (AUC) (95%CI) was 0.650 (0.631-0.670) for IDF-defined MetS and 0.683 (0.664-0.703) with the ATPIII definition. The optimal HOMA-IR cut-off for the diagnosis of IDF- and ATPIII-defined MetS in non-diabetic individuals was 1.775 (sensitivity: 57.3%, specificity: 65.3%, with ATPIII; sensitivity: 55.9%, specificity: 64.7%, with IDF). The optimal cut-offs in diabetic individuals were 3.875 (sensitivity: 49.7%, specificity: 69.6%) and 4.325 (sensitivity: 45.4%, specificity: 69.0%) for ATPIII- and IDF-defined MetS, respectively. We determined the optimal HOMA-IR cut-off points for the diagnosis of MetS in the Iranian population with and without diabetes.
de Lima Sanches, Priscila; de Mello, Marco Túlio; Elias, Natália; Fonseca, Francisco Antonio Helfenstein; de Piano, Aline; Carnier, June; Oyama, Lila Missae; Tock, Lian; Tufik, Sergio; Dâmaso, Ana Raimunda
2011-02-01
The aim of this study was to verify whether a 1-year interdisciplinary weight-loss program improved common carotid artery intima-media thickness (IMT) and whether insulin resistance and/or inflammation (as measured by the markers plasminogen activator inhibitor type-1 and adiponectin) might underlie obesity in adolescents. A group of 29 post-pubescent obese adolescents were submitted to an interdisciplinary intervention over the course of 1 year. Common carotid artery IMT was determined ultrasonographically. Body composition, blood pressure (BP), glycemia, insulinemia, homeostasis model assessment of insulin resistance (HOMA-IR), lipid profile and adipokine concentrations were analyzed before and after the intervention. The interdisciplinary weight-loss program promoted a significant improvement in body composition, insulin concentration, HOMA-IR, lipid profile, BP and inflammatory state, in addition to significantly decreasing the common carotid artery IMT. Furthermore, this study demonstrated that the difference between baseline and final values of HOMA-IR (ΔHOMA-IR) was negatively correlated with concomitant changes in the adiponectin concentration (Δadiponectin; r=-0.42; P=0.02) and positively correlated with changes in common carotid artery IMT (Δcarotid IMT; r=0.41; P=0.03). Multiple regression analysis adjusted by age, cardiovascular risk factors and inflammatory markers showed that ΔHOMA-IR was an independent predictor of significant changes in common carotid artery IMT. This investigation demonstrated that an interdisciplinary weight-loss program promoted a reduction of the common carotid artery IMT in obese Brazilian adolescents, and the improvement of HOMA-IR was an independent predictor of carotid IMT changes in this population.
Variability in HOMA-IR, lipoprotein profile and selected hormones in young active men.
Keska, Anna; Lutoslawska, Grazyna; Czajkowska, Anna; Tkaczyk, Joanna; Mazurek, Krzysztof
2013-01-01
Resistance to insulin actions is contributing to many metabolic disturbances. Such factors as age, sex, nutrition, body fat, and physical activity determine body insulin resistance. Present study attempted to asses insulin resistance and its metabolic effects with respect to energy intake in young, lean, and active men. A total of 87 men aged 18-23 participated in the study. Plasma levels of glucose, insulin, lipoproteins, cortisol, and TSH were determined. Insulin resistance was expressed as Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) and calculated using homeostatic model. The median value of HOMA-IR (1.344) was used to divide subjects into two groups. Men did not differ in anthropometric parameters, daily physical activity, and plasma TSH and cortisol levels. However, in men with higher HOMA-IR significantly lower daily energy intake was observed concomitantly with higher TG, TC, and HDL-C concentrations in plasma versus their counterparts with lower HOMA-IR. Exclusively in subjects with higher HOMA-IR significant and positive correlation was noted between HOMA-IR and TC and LDL-C. We concluded that despite a normal body weight and physical activity, a subset of young men displayed unfavorable changes in insulin sensitivity and lipid profile, probably due to insufficient energy intake.
Li, Xue; Pang, Xiuyu; Zhang, Qiao; Qu, Qiannuo; Hou, Zhigang; Liu, Zhipeng; Lv, Lin; Na, Guanqiong; Zhang, Wei; Sun, Changhao; Li, Ying
2016-01-01
Abstract This prospective cohort study was conducted to assess the duration of daytime napping and its effect combined with night sleep deprivation on the risk of developing high HOMA-IR (homeostasis model assessment of insulin resistance) index and disadvantageous changes in glycosylated hemoglobin (HbA1c) levels. A total of 5845 diabetes-free subjects (2736 women and 3109 men), 30 to 65 years of age, were targeted for this cohort study since 2008. Multiple adjusted Cox regression models were performed to evaluate the single and joint effects of daytime napping on the risk of an elevated HbA1c level and high HOMA-IR index. After an average of 4.5 years of follow-up, >30 minutes of daytime napping was significantly associated with an increased risk of an elevated HbA1c level (>6.5%) in men and women (all P trend HOMA-IR index in the entire cohort, men, and women were 1.33 (1.10–1.62), 1.46 (1.08–1.98), and 1.47 (1.12–1.91), respectively. The combination of sleep deprivation with no naps or >30 minutes napping and the combination of no sleep deprivation with >30 minutes daytime napping were all associated with an HbA1c level >6.5% (HR = 2.08, 95% CI = 1.24–3.51; HR = 4.00, 95% CI = 2.03–7.90; and HR = 2.05, 95% CI = 1.29–3.27, respectively). No sleep deprivation combined with >30 minutes daytime napping correlated with a high risk of an HbA1c level between 5.7% and 6.4% and high HOMA-IR index (HR = 2.12, 95% CI = 1.48–3.02; and HR = 1.35, 95% CI = 1.10–1.65, respectively). Daytime napping >30 minutes was associated with a high risk of an elevated HbA1c level and high HOMA-IR index. No sleep deprivation combined with napping >30 minutes carries a risk of abnormal glucose metabolism. Sleep deprivation combined with brief daytime napping HOMA-IR index. PMID:26844520
[Reliability of HOMA-IR for evaluation of insulin resistance during perioperative period].
Fujino, Hiroko; Itoda, Shoko; Sako, Saori; Matsuo, Kazuki; Sakamoto, Eiji; Yokoyama, Takeshi
2013-02-01
Hyperglycemia due to increase in insulin resistance (IR) is often observed after surgery in spite of normal insulin secretion. To evaluate the degree of IR, the golden standard method is the normoglycemic hyperinsulinemic clamp technique (glucose clamp: GC). The GC using the artificial pancreas, STG-22 (Nikkiso, Tokyo, Japan), was established as a more reliable method, since it was evaluated during steady-state period under constant insulin infusion. Homeostasis model assessment insulin resistance (HOMA-IR), however, is frequently employed in daily practice because of its convenience. We, therefore, investigated the reliability of HOMA-IR in comparison with the glucose clamp using the STG-22. Eight healthy patients undergoing maxillofacial surgery were employed in this study after obtaining written informed consent. Their insulin resistance was evaluated by HOMA-IR and the GC using the STG-22 before and after surgery. HOMA-IR increased from 0.81 +/- 0.48 to 1.17 +/- 0.50, although there were no significant differences between before and after surgery. On the other hand, M-value by GC significantly decreased after surgery from 8.82 +/- 2.49 mg x kg(-1) x min(-1) to 3.84 +/- 0.79 mg x kg(-1) x min(-1) (P = 0.0003). In addition, no significant correlation was found between the values of HOMA-IR and the M-value by GC. HOMA-IR may not be reliable to evaluate IR for perioperative period.
Genetics of variation in HOMA-IR and cardiovascular risk factors in Mexican-Americans.
Voruganti, V Saroja; Lopez-Alvarenga, Juan C; Nath, Subrata D; Rainwater, David L; Bauer, Richard; Cole, Shelley A; Maccluer, Jean W; Blangero, John; Comuzzie, Anthony G
2008-03-01
Insulin resistance is a major biochemical defect underlying the pathogenesis of cardiovascular disease (CVD). Mexican-Americans are known to have an unfavorable cardiovascular profile. Thus, the aim of this study was to investigate the genetic effect on variation in HOMA-IR and to evaluate its genetic correlations with other phenotypes related to risk of CVD in Mexican-Americans. The homeostatic model assessment method (HOMA-IR) is one of several approaches that are used to measure insulin resistance and was used here to generate a quantitative phenotype for genetic analysis. For 644 adults who had participated in the San Antonio Family Heart Study (SAFHS), estimates of genetic contribution were computed using a variance components method implemented in SOLAR. Traits that exhibited significant heritabilities were body mass index (BMI) (h (2) = 0.43), waist circumference (h (2) = 0.48), systolic blood pressure (h (2) = 0.30), diastolic blood pressure (h (2) = 0.21), pulse pressure (h (2) = 0.32), triglycerides (h (2) = 0.51), LDL cholesterol (h (2) = 0.31), HDL cholesterol (h (2) = 0.24), C-reactive protein (h (2) = 0.17), and HOMA-IR (h (2) = 0.33). A genome-wide scan for HOMA-IR revealed significant evidence of linkage on chromosome 12q24 (close to PAH (phenylalanine hydroxylase), LOD = 3.01, p HOMA-IR with BMI (rho (G) = 0.36), waist circumference (rho (G) = 0.47), pulse pressure (rho (G) = 0.39), and HDL cholesterol (rho (G) = -0.18). Identification of significant linkage for HOMA-IR on chromosome 12q replicates previous family-based studies reporting linkage of phenotypes associated with type 2 diabetes in the same chromosomal region. Significant genetic correlations between HOMA-IR and phenotypes related to CVD risk factors suggest that a common set of gene(s) influence the regulation of these phenotypes.
Sharma, Sushma; Lustig, Robert H; Fleming, Sharon E
2011-05-01
Metabolic syndrome (MetS) is increasing among young people. We compared the use of homeostasis model assessment of insulin resistance (HOMA-IR) with the use of fasting blood glucose to identify MetS in African American children. We performed a cross-sectional analysis of data from a sample of 105 children (45 boys, 60 girls) aged 9 to 13 years with body mass indexes at or above the 85th percentile for age and sex. Waist circumference, blood pressure, and fasting levels of blood glucose, insulin, triglycerides, and high-density lipoprotein cholesterol were measured. We found that HOMA-IR is a stronger indicator of MetS in children than blood glucose. Using HOMA-IR as 1 of the 5 components, we found a 38% prevalence of MetS in this sample of African American children and the proportion of false negatives decreased from 94% with blood glucose alone to 13% with HOMA-IR. The prevalence of MetS was higher in obese than overweight children and higher among girls than boys. Using HOMA-IR was preferred to fasting blood glucose because insulin resistance was more significantly interrelated with the other 4 MetS components.
Iftikhar, Imran H; Hoyos, Camilla M; Phillips, Craig L; Magalang, Ulysses J
2015-04-15
We sought to conduct an updated meta-analysis of randomized controlled trials (RCTs) on the effect of continuous positive airway pressure (CPAP) on insulin resistance, as measured by homeostasis model assessment of insulin resistance (HOMA-IR), visceral abdominal fat (VAF), and adiponectin. Additionally, we performed a separate meta-analysis and meta-regression of studies on the association of insulin resistance and obstructive sleep apnea (OSA). All included studies were searched from PubMed (from conception to March 15, 2014). Data were pooled across all included RCTs as the mean difference in HOMA-IR and VAF, and as the standardized mean difference in the case of adiponectin analysis. From the included case-control studies, data on the difference of HOMA-IR between cases and controls were pooled across all studies, as the standardized mean difference (SMD). There was a significant difference in HOMA-IR (-0.43 [95% CIs: -0.75 to -0.11], p = 0.008) between CPAP treated and non CPAP treated participants. However, there was no significant difference in VAF or adiponectin; (-47.93 [95% CI: -112.58 to 16.72], p = 0.14) and (-0.06 [95% CI: -0.28 to 0.15], p = 0.56), respectively. Meta-analysis of 16 case-control studies showed a pooled SMD in HOMA-IR of 0.51 (95% CI: 0.28 to 0.75), p ≤ 0.001, between cases and controls. The results of our meta-analyses show that CPAP has a favorable effect on insulin resistance. This effect is not associated with any significant changes in total adiponectin levels or amount of VAF. Our findings also confirm a significant association between OSA and insulin resistance. © 2015 American Academy of Sleep Medicine.
HOMA-IR and the risk of hyperuricemia: a prospective study in non-diabetic Japanese men.
Nakamura, Koshi; Sakurai, Masaru; Miura, Katsuyuki; Morikawa, Yuko; Nagasawa, Shin-Ya; Ishizaki, Masao; Kido, Teruhiko; Naruse, Yuchi; Nakashima, Motoko; Nogawa, Kazuhiro; Suwazono, Yasushi; Nakagawa, Hideaki
2014-10-01
To examine the relation of insulin resistant status determined by homeostasis model assessment of insulin resistance (HOMA-IR) with the risk of incident hyperuricemia. The study participants included 2071 Japanese men without hyperuricemia and diabetes, aged 35-54 years. The participants had undergone annual heath examinations for 6 years to compare incident hyperuricemia (serum uric acid >416.4μmol/L (7.0mg/dL) and/or taking medication for hyperuricemia) in four groups based on quartiles of baseline HOMA-IR. During follow-up there were 331 incident cases of hyperuricemia. The hazard ratios for hyperuricemia, compared with HOMA-IR ≤0.66, were 1.42 (95% confidence interval 1.02-1.98) for HOMA-IR 0.67-0.98, 1.20 (0.86-1.68) for HOMA-IR 0.99-1.49 and 1.44 (1.04-1.98) for HOMA-IR ≥1.50 after adjustment for baseline serum uric acid, creatinine, hypercholesterolemia and hypertension status, age, alcohol intake, and smoking and exercise habits. The hazard ratio associated with an increase of one standard deviation in lnHOMA-IR (1.85 as one geometric standard deviation of HOMA-IR) was 1.14 (1.03-1.28) (p for trend=0.02). Increased HOMA-IR independently predicted the subsequent development of hyperuricemia. Insulin resistance itself or compensatory hyperinsulinemia may contribute to the development of hyperuricemia. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Reference ranges of HOMA-IR in normal-weight and obese young Caucasians.
Shashaj, Blegina; Luciano, Rosa; Contoli, Benedetta; Morino, Giuseppe Stefano; Spreghini, Maria Rita; Rustico, Carmela; Sforza, Rita Wietrzycowska; Dallapiccola, Bruno; Manco, Melania
2016-04-01
Insulin resistance (IR) may develop very early in life being associated with occurrence of cardiometabolic risk factors (CMRFs). Aim of the present study was to identify in young Caucasians normative values of IR as estimated by the homeostasis model assessment (HOMA-IR) and cutoffs diagnostic of CMRFs. Anthropometrics and biochemical parameters were assessed in 2753 Caucasians (age 2-17.8 years; 1204 F). Reference ranges of HOMA-IR were defined for the whole population and for samples of normal-weight and overweight/obese individuals. The receiver operator characteristic analysis was used to find cutoffs of HOMA-IR accurately identifying individuals with any CMRF among total cholesterol and/or triglycerides higher than the 95th percentile and/or HDL cholesterol lower than the 5th for age and sex, impaired glucose tolerance, and alanine aminotransferase levels ≥40 U/l. Overweight/obese individuals had higher HOMA-IR levels compared with normal-weight peers (p HOMA-IR index rose progressively with age, plateaued between age 13 and 15 years and started decreasing afterward. HOMA-IR peaked at age 13 years in girls and at 15 years in boys. The 75th percentile of HOMA-IR in the whole population (3.02; AUROC = 0.73, 95 % CI = 0.70-0.75), in normal-weight (1.68; AUROC = 0.76, 95 % CI = 0.74-0.79), and obese (3.42; AUROC = 0.71, 95 % CI = 0.69-0.72) individuals identified the cutoffs best classifying individuals with any CMRF. Percentiles of HOMA-IR varied significantly in young Caucasians depending on sex, age, and BMI category. The 75th percentile may represent an accurate cutoff point to suspect the occurrence of one or more CMRFs among high total cholesterol and triglycerides, low HDL cholesterol, and ALT ≥ 40 UI/l.
Hossain, Israt Ara; Rahman Shah, Md Mijanur; Rahman, Mohammad Khalilur; Ali, Liaquat
2016-01-01
Nonalcoholic fatty liver disease (NAFLD) is a major cause of liver-related morbidity and is frequently associated with insulin resistance (HOMA-IR) syndrome. Recently serum gamma glutamyl transferase (GGT) has been considered as surrogate marker of NAFLD leading to oxidative stress and hepatocellular damage. In the present study we examined the association of serum GGT and HOMA-IR with NAFLD in Bangladeshi adult subjects. Under a cross-sectional analytical design a total of 110 subjects were recruited who came for their routine health check up in the BIHS Hospital, Darussalam, Dhaka, Bangladesh. After whole abdomen ultrasonography, 62 were diagnosed as non-NAFLD and 48 were NAFLD subjects. Serum glucose was measured by glucose-oxidase method, lipid profile and liver enzymes by enzymatic colorimetric method, glycosylated hemoglobin (HbA1c) was measured by high performance liquid chromatography (HPLC), serum insulin were measured by enzyme-linked immunosorbent assay. HOMA-IR was calculated by homeostasis model assessment (HOMA). NAFLD subjects had significantly higher levels of GGT and HOMA-IR as compared to their non-NAFLD counterparts. Multiple linear regression analysis showed a significant positive association of HOMA-IR with GGT after adjusting the effects of waist circumference (WC) and HbA1c. In binary logistic regression analysis, HOMA-IR and GGT were found to be significant determinants of NAFLD after adjusting the effects of WC and HbA1c. These results suggest that elevated levels of GGT and insulin resistance are more likely to develop NAFLD and thus support a role of these determinants in the pathogenesis of NAFLD in Bangladeshi adult subjects. Copyright © 2015 Diabetes India. Published by Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Yani Lina
2009-12-01
Full Text Available BACKGROUND: Fibroblast growth factor-21 (FGF21 is known as an important endocrine and paracrine regulator of metabolic homeostasis. Recent studies have shown that FGF21 attenuates lipolysis in human adipocytes, which is suggested as a FGF21's mechanism as anti-hyperlipidemia, anti-hyperglycemia and anti-obesity. The aim of this study was to measure the correlation between FGF21, FFA, hsCRP and HOMA-IR among Indonesian obese non diabetic males. METHODS: The study was observational with cross sectional design. The analysis was done in 137 subjects aged 30-60 years with non diabetic abdominal obesity. We measured the biochemical markers FGF21, FFA, hsCRP, fasting insulin and fasting glucose. We also measured weight, height, waist circumrefence (WC, creatinine, serum glutamin oxaloacetic transaminase (SGOT, and serum glutamic pyruvic transaminase (SGPT, systolic blood pressure (SBP and diastolic blood pressure (DBP. Correlation between markers was measured using Pearson and Spearman's analysis. RESULTS: There were significant positive correlations between FGF21-HOMA-IR (r=0.314, p=0.000; FGF21-WC (r=0.173, p=0.043; FFA=hsCRP r=0.270, p=0.001; and WC-HOMA-IR (r=0.279, p=0.001. There was significant negative correlation between FGF21-FFA (r=-0.038, p=0.657 and FGF21-hsCRP (r=-0.061, p=0.482. CONCLUSIONS: In this study we found that although there was no significant correlation, FGF21 might act as an anti-lipolytic and anti-inflammation agent among Indonesian obese non-diabetic males. Our findings agree with results of previous studies that the positive correlation between FGF21-WC and FGF21-HOMA-IR might occur as a compensatory mechanism or resistance to FGF21 in obesity. KEYWORDS: obesity, FGF21, FFA, hsCRP, HOMA-IR.
A cohort study of incident microalbuminuria in relation to HOMA-IR in Korean men.
Park, Sung Keun; Chun, Hyejin; Ryoo, Jae-Hong; Lee, Sang Wha; Lee, Hong Soo; Shim, Kyung Won; Cho, Choo Yon; Ryu, Dong-Ryeol; Ko, Taeg Su; Kim, Eugene; Park, Se-Jin; Park, Jai Hyung; Hong, Seok Jin; Hong, Hyun Pyo
2015-06-15
Despite the previous studies showing the relationship between microalbuminuria and insulin resistance, longitudinal effect of insulin resistance on development of microalbuminuria is not clearly identified in non-diabetic population. One thousand six hundred three non-diabetic Korean men without microalbuminuria in 2005 had been followed up for the development of microalbuminuria until 2010. Microalbuminuria was evaluated by urine-albumin creatinine ration, and insulin resistance was evaluated by homeostasis model assessment of insulin resistance (HOMA-IR). Cox proportional hazards model was used to estimate the risk for microalbuminuria according to the tertile of HOMA-IR. During 5465.8 person-y of average follow-up, microalbuminuria developed in 76 (4.7%) participants. Incidence of microalbuminuria increased in proportion to the level of HOMA-IR (tertile 1: 3.0%, tertile 2: 4.1%, tertile 3: 7.1%, PHOMA-IR was set as reference, hazard ratios and 95% confidence interval were 1.15 (0.56-2.35) and 2.07 (1.05-4.09) for those in the 2nd and 3rd tertiles of HOMA-IR, even after adjusting multiple covariates, respectively (P for linear trend=0.054). Increased insulin resistance was a predictor of microalbuminuria in Korean men. Copyright © 2015 Elsevier B.V. All rights reserved.
Li, Xue; Pang, Xiuyu; Zhang, Qiao; Qu, Qiannuo; Hou, Zhigang; Liu, Zhipeng; Lv, Lin; Na, Guanqiong; Zhang, Wei; Sun, Changhao; Li, Ying
2016-02-01
This prospective cohort study was conducted to assess the duration of daytime napping and its effect combined with night sleep deprivation on the risk of developing high HOMA-IR (homeostasis model assessment of insulin resistance) index and disadvantageous changes in glycosylated hemoglobin (HbA1c) levels.A total of 5845 diabetes-free subjects (2736 women and 3109 men), 30 to 65 years of age, were targeted for this cohort study since 2008. Multiple adjusted Cox regression models were performed to evaluate the single and joint effects of daytime napping on the risk of an elevated HbA1c level and high HOMA-IR index.After an average of 4.5 years of follow-up, >30 minutes of daytime napping was significantly associated with an increased risk of an elevated HbA1c level (>6.5%) in men and women (all P trend HOMA-IR index in the entire cohort, men, and women were 1.33 (1.10-1.62), 1.46 (1.08-1.98), and 1.47 (1.12-1.91), respectively. The combination of sleep deprivation with no naps or >30 minutes napping and the combination of no sleep deprivation with >30 minutes daytime napping were all associated with an HbA1c level >6.5% (HR = 2.08, 95% CI = 1.24-3.51; HR = 4.00, 95% CI = 2.03-7.90; and HR = 2.05, 95% CI = 1.29-3.27, respectively). No sleep deprivation combined with >30 minutes daytime napping correlated with a high risk of an HbA1c level between 5.7% and 6.4% and high HOMA-IR index (HR = 2.12, 95% CI = 1.48-3.02; and HR = 1.35, 95% CI = 1.10-1.65, respectively).Daytime napping >30 minutes was associated with a high risk of an elevated HbA1c level and high HOMA-IR index. No sleep deprivation combined with napping >30 minutes carries a risk of abnormal glucose metabolism. Sleep deprivation combined with brief daytime napping HOMA-IR index.
[Distribution of HOMA-IR among children and adolescent in Zhangzhou and Zhongshan cities].
He, Jinshui; Lin, Guomo; Zhang, Yugui; Ye, Xiaoling; Liu, Fuxing; Liu, Linyong
2015-07-01
To investigate the distribution of the homeostasis model assessment of insulin resistance (HOMA-IR) among children and adolescent in Zhangzhou city and Zhongshan city. Total of 3102 children and adolescent aged 6 to 18-year-old were recruited, which were enrolled in a population-based cross-sectional study. Anthropometric and biochemical parameters were measured. A total of 1528 (49.26%) girls and 1574 (50.74%) boys were included in this study. The concentrations of insulin and fasting glucose gradually increased from 6 to 18 years of age, there was no statistical difference between boys ang girls. The mean values for the BMI were similar in age-matched boys and girls from 6 to 18-year-old ,but for 12 to 15-year-old children was significantly higher in the girls compared with the boys and conversely for 16 to 18-year-old (P HOMA-IR gradually increased with age and reached a plateau at 12 years of age and there was no markedly differential in gender. The glucose levels, insulin concentrations and HOMA-IR exhibited a gradual increase with age. It was suggested that the evaluation of IR in children should be based on percentiles of the HOMA-IR rather than a dichotomous value derived from a single cutoff point.
de Abreu, Virgínia Genelhu; Martins, Cyro José de Moraes; de Oliveira, Patricia Aguiar Cardoso; Francischetti, Emilio Antonio
2017-01-01
Metabolic syndrome (MetS) has an important epidemiological relevance due to its increasing prevalence and association with type 2 diabetes and cardiovascular disease. Insulin resistance is a core feature of the MetS. HOMA-IR is a robust clinical and epidemiological marker of MetS. Adiponectin is an adipokine with insulin-sensitizing and anti-inflammatory functions; its levels decrease as number of components of MetS increases. High-molecular weight adiponectin (HMWA) is the multimer responsible for the relationship of adiponectin with insulin sensitivity. HOMA-IR and HMWA are suitable candidates for MetS biomarkers. The ratio of adiponectin to HOMA-IR has been validated as a powerful index of MetS and considered a better marker of its presence, than either HOMA-IR or adiponectin alone, in selected homogeneous populations. We compared the strength of association between HMWA, HOMA-IR and HMWA/HOMA-IR ratio with MetS and its key components. Our data have shown that the median (25th, 75th percentile) of HMWA/HOMA-IR ratio was lower in subjects with MetS [0.51 (0.33, 1.31)] as compared to those without it [2.19 (1.13, 4.71)]. The correlation coefficient (r) was significantly higher for HMWA/HOMA-IR ratio as compared to HMWA for waist circumference (-0.65; -0.40, respectively); mean blood pressure (-0.27; -0.14, respectively); fasting glucose (-0.38; -0.19, respectively); HDL-cholesterol (0.44; 0.40, respectively); and triglycerides (-0.35; -0.18, respectively). In a multivariable logistic regression analysis, the HMWA/HOMA-IR ratio was a sensitive predictor for MetS, being the only marker that was significantly associated with each and all the individual components of the syndrome. These results expand on previous studies in that we used the active circulating form of adiponectin, i.e. HMWA, and represent a typical Brazilian cohort characterized by intense interethnic admixture. Thus, the HMWA/HOMA-IR ratio is a minimally invasive biomarker for MetS that could be
Serum Interleukin-6, insulin, and HOMA-IR in male individuals with colorectal adenoma.
Sasaki, Yu; Takeda, Hiroaki; Sato, Takeshi; Orii, Tomohiko; Nishise, Shoichi; Nagino, Ko; Iwano, Daisuke; Yaoita, Takao; Yoshizawa, Kazuya; Saito, Hideki; Tanaka, Yasuhisa; Kawata, Sumio
2012-01-15
It is widely acknowledged that chronic low-grade inflammation plays a key role in the development of obesity-related insulin resistance and type 2 diabetes. The level of circulating interleukin-6 (IL-6), one of the major proinflammatory adipokines, is correlated with obesity and insulin resistance, which are known to be risk factors for colorectal adenoma. We examined the association between the circulating level of IL-6 and the presence of colorectal adenoma. In a total colonoscopy-based cross-sectional study conducted between January and December 2008, serum levels of IL-6 were measured in samples of venous blood obtained from 336 male participants attending health checkups (118 individuals with colorectal adenoma and 218 age-matched controls) after an overnight fast. In the colorectal adenoma group, the median levels of serum IL-6 (1.24 vs. 1.04 pg/mL; P = 0.01), triglyceride, insulin, and homeostasis model assessment of insulin resistance (HOMA-IR) were to be significantly higher than those in the control group. When restricted to individuals with adenoma, levels of IL-6 were positively correlated with body mass index, insulin, and HOMA-IR. Multiple logistic analyses adjusted to include insulin or HOMA-IR showed that high levels of IL-6 were associated with the presence of colorectal adenoma. There was no significant interaction of IL-6 with HOMA-IR to modify this association. Our findings suggest that increased serum levels of IL-6 are positively associated with the presence of colorectal adenoma in men, independently of insulin and HOMA-IR. ©2011 AACR.
Piyathilake, Chandrika J; Badiga, Suguna; Alvarez, Ronald D; Partridge, Edward E; Johanning, Gary L
2013-01-01
Identification of associations between global DNA methylation and excess body weight (EBW) and related diseases and their modifying factors are an unmet research need that may lead to decreasing DNA methylation-associated disease risks in humans. The purpose of the current study was to evaluate the following; 1) Association between the degree of peripheral blood mononuclear cell (PBMC) L1 methylation and folate, and indicators of EBW, 2) Association between the degree of PBMC L1 methylation and folate, and insulin resistance (IR) as indicated by a higher homeostasis model assessment (HOMA-IR). The study population consisted of 470 child-bearing age women diagnosed with abnormal pap. The degree of PBMC L1 methylation was assessed by pyrosequencing. Logistic regression models specified indicators of EBW (body mass index-BMI, body fat-BF and waist circumference-WC) or HOMA-IR as dependent variables and the degree of PBMC L1 methylation and circulating concentrations of folate as the independent predictor of primary interest. Women with a lower degree of PBMC L1 methylation and lower plasma folate concentrations were significantly more likely to have higher BMI, % BF or WC (OR = 2.49, 95% CI:1.41-4.47, P = 0.002; OR = 2.49, 95% CI:1.40-4.51, P = 0.002 and OR = 1.98, 95% = 1.14-3.48 P = 0.0145, respectively) and higher HOMA-IR (OR = 1.78, 95% CI:1.02-3.13, P = 0.041). Our results demonstrated that a lower degree of PBMC L1 methylation is associated with excess body weight and higher HOMA-IR, especially in the presence of lower concentrations of plasma folate.
Use of HOMA-IR in hepatitis C.
Eslam, M; Kawaguchi, T; Del Campo, J A; Sata, M; Khattab, M Abo-Elneen; Romero-Gomez, M
2011-10-01
Chronic infection with hepatitis C virus (HCV) can induce insulin resistance (IR) in a genotype-dependent manner and contributes to steatosis, progression of fibrosis and resistance to interferon plus ribavirin therapy. Our understanding of HCV-induced IR has improved considerably over the years, but certain aspects concerning its evaluation still remain elusive to clinical researchers. One of the most important issues is elucidating the ideal method for assessment of IR in the setting of hepatitis C. The hyperinsulinaemic euglycaemic clamp is the gold standard method for determining insulin sensitivity, but is impractical as it is labour intensive and time-consuming. To date, all human studies except for four where IR was evaluated in the HCV setting, an estimation of IR has been used rather than direct measurements of insulin-mediated glucose uptake. The most commonly used estimation in the HCV population is the homeostasis model assessment of insulin resistance (HOMA-IR) which is calculated from a single measurement of fasting insulin and glucose. In this article, we review the use and reporting of HOMA in the literature and provide guidance on its appropriate as well as inappropriate use in the hepatitis setting. © 2011 Blackwell Publishing Ltd.
Mossmann, Márcio; Wainstein, Marco V; Gonçalves, Sandro C; Wainstein, Rodrigo V; Gravina, Gabriela L; Sangalli, Marlei; Veadrigo, Francine; Matte, Roselene; Reich, Rejane; Costa, Fernanda G; Bertoluci, Marcello C
2015-01-01
Insulin resistance is a major component of metabolic syndrome, type 2 Diabetes Mellitus (T2DM) and coronary artery disease (CAD). Although important in T2DM, its role as a predictor of CAD in non-diabetic patients is less studied. In the present study, we aimed to evaluate the association of HOMA-IR with significant CAD, determined by coronary angiography in non-obese, non-T2DM patients. We also evaluate the association between 3 oral glucose tolerance test (OGTT) based insulin sensitivity indexes (Matsuda, STUMVOLL-ISI and OGIS) and CAD. We conducted a cross-sectional study with 54 non-obese, non-diabetic individuals referred for coronary angiography due to suspected CAD. CAD was classified as the "anatomic burden score" corresponding to any stenosis equal or larger than 50 % in diameter on the coronary distribution. Patients without lesions were included in No-CAD group. Patients with at least 1 lesion were included in the CAD group. A 75 g oral glucose tolerance test (OGTT) with measurements of plasma glucose and serum insulin at 0, 30, 60, 90 and 120 min was obtained to calculate insulin sensitivity parameters. HOMA-IR results were ranked and patients were also categorized into insulin resistant (IR) or non-insulin resistant (NIR) if they were respectively above or below the 75th percentile (HOMA-IR > 4.21). The insulin sensitivity tests results were also divided into IR and NIR, respectively below and above each 25th percentile. Chi square was used to study association. Poisson Regression Model was used to compare prevalence ratios between categorized CAD and IR groups. Fifty-four patients were included in the study. There were 26 patients (48 %) with significant CAD. The presence of clinically significant CAD was significant associated with HOMA-IR above p75 (Chi square 4.103, p = 0.0428) and 71 % of patients with HOMA-IR above p75 had significant CAD. Subjects with CAD had increased prevalence ratio of HOMA-IR above p75 compared to subjects without
[HOMA-IR in patients with chronic hepatitis C].
Botshorishvili, T; Vashakidze, E
2012-02-01
The aim of investigation was to study the frequency of IR in type of viral hepatitis C, correlation with the degree of hepatic lesion and liver cirrhosis. 130 patients were investigated: 20 with acute hepatitis C; 38 with chronic hepatitis C; 72 with cirrhosis: among them 10 with Stage A, 14 with Stage B and 48 with Stage C. Also we used 30 healthy people as the controls. The study demonstrates significant changes of insulin, glucose, HOMA-IR type of viral hepatitis C, correlation with the degree of hepatic lesion and liver cirrhosis. In patients with liver cirrhosis levels of HOMA-IR is higher than in patients with chronic hepatitis C. In patients with acute hepatitis C levels of HOMA-IR was normal as in the control group. The results showed that various types of chronic viral hepatitis C and stages of cirrhosis set to increase HOMA-IR versus the controls., which were the most prominent in cases of severe hepatic lesion, which indicates that insulin resistance is a frequent companion of CHC.
Ha, Chang Ho; Swearingin, Brenda; Jeon, Yong Kyun
2015-09-01
[Purpose] This study aimed to examine the correlation of visfatin level to pancreatic endocrine hormone level, homeostasis model assessment of insulin resistance (HOMA-IR) index, and HOMA β-cell index in hydraulic resistance exercise. Furthermore, it investigated the relationship between visfatin level and other variables affected by exercise in overweight women. [Subjects and Methods] The exercise group trained for 12 weeks, 70 minutes/day, 5 days/week. Visfatin level, pancreatic endocrine hormone level, HOMA-IR index, and HOMA β-cell index were measured before and after the intervention. Based on the blood insulin and glucose concentrations, HOMA-IR index, the indicator of insulin resistance, and HOMA β-cell index, the indicator of insulin secretion level, were assessed. [Results] Interaction effects on visfatin level, insulin level, HOMA-IR index, and HOMA β-cell index were observed. Interaction effects on glucagon and glucose levels were not observed between the intervention groups. The correlations of visfatin level to insulin, glucagon, and glucose levels, and HOMA-IR and HOMA β-cell indexes were not significant for any of the subjects. [Conclusion] Therefore, the 12-week resistance exercise affected body composition, visfatin level, insulin level, HOMA-IR index, and HOMA β-cell index. Finally, visfatin was not related to insulin, glucagon, and glucose levels, and HOMA-IR and HOMA β-cell indexes.
Singh, Yashpal; Garg, M K; Tandon, Nikhil; Marwaha, Raman Kumar
2013-01-01
Insulin resistance (IR) and associated metabolic abnormalities are increasingly being reported in the adolescent population. Cut-off value of homeostasis model of assessment IR (HOMA-IR) as an indicator of metabolic syndrome (MS) in adolescents has not been established. This study aimed to investigate IR by HOMA-IR in urban Indian adolescents and to establish cut-off values of HOMA-IR for defining MS. A total of 691 apparently healthy adolescents (295 with normal body mass index (BMI), 205 overweight, and 199 obese) were included in this cross-sectional study. MS in adolescents was defined by International Diabetes Federation (IDF) and Adult Treatment Panel III (ATP III) criteria. IR was calculated using the HOMA model. Mean height, waist circumference (WC), waist/hip ratio (WHR), waist/height ratio (WHtR), and blood pressure were significantly higher in boys as compared to girls. The HOMA-IR values increased progressively from normal weight to obese adolescents in both sexes. Mean HOMA-IR values increased progressively according to sexual maturity rating in both sexes. HOMA-IR value of 2.5 had a sensitivity of >70% and specificity of >60% for MS. This cut-off identified larger number of adolescents with MS in different BMI categories (19.7% in normal weight, 51.7% in overweight, and 77.0% in obese subjects) as compared to the use of IDF or ATP III criteria for diagnosing MS. Odds ratio for having IR (HOMA-IR of >2.5) was highest with WHtR (4.9, p pHOMA-IR increased with sexual maturity and with progression from normal to obese. A HOMA-IR cut-off of 2.5 provided the maximum sensitivity and specificity in diagnosing MS in both genders as per ATP III and IDF criteria.
HOMA-IR and QUICKI: decide on a general standard instead of making further comparisons.
Rössner, Sophia M; Neovius, Martin; Mattsson, Anna; Marcus, Claude; Norgren, Svante
2010-11-01
To limit further comparisons between the two fasting indices Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) and Quantitative Insulin Sensitivity Check Index (QUICKI), and to examine their robustness in assessing insulin sensitivity. A total of 191 obese children and adolescents (age 13.9 ± 2.9 years, BMI SDS 6.1 ± 1.6), who had undergone a Frequently Sampled Intravenous Glucose Tolerance Test (FSIVGTT), were included. Receiver operating characteristic curve (ROC) analysis was used to compare indices in detecting insulin resistance and Bland-Altman plots to investigate agreement between three consecutive fasting samples when compared to using single samples. ROC analysis showed that the diagnostic accuracy was identical for QUICKI and HOMA-IR [area under the curve (AUC) boys 0.80, 95%CI 0.70-0.89; girls 0.80, 0.71-0.88], while insulin had a nonsignificantly lower AUC (boys 0.76, 0.66-0.87; girls 0.75, 0.66-0.84). Glucose did not perform better than chance as a diagnostic test (boys 0.47, 0.34-0.60; girls 0.57, 0.46-0.68). Indices varied with consecutive sampling, mainly attributable to fasting insulin variations (mean maximum difference in HOMA-IR -0.8; -0.9 to -0.7). Using both HOMA-IR and QUICKI in further studies is superfluous as these indices function equally well as predictors of the FSIVGTT sensitivity index. Focus should be on establishing a general standard for research and clinical purposes. © 2010 The Author(s)/Journal Compilation © 2010 Foundation Acta Paediatrica.
Ancestral effect on HOMA-IR levels quantitated in an American population of Mexican origin.
Qu, Hui-Qi; Li, Quan; Lu, Yang; Hanis, Craig L; Fisher-Hoch, Susan P; McCormick, Joseph B
2012-12-01
An elevated insulin resistance index (homeostasis model assessment of insulin resistance [HOMA-IR]) is more commonly seen in the Mexican American population than in European populations. We report quantitative ancestral effects within a Mexican American population, and we correlate ancestral components with HOMA-IR. We performed ancestral analysis in 1,551 participants of the Cameron County Hispanic Cohort by genotyping 103 ancestry-informative markers (AIMs). These AIMs allow determination of the percentage (0-100%) ancestry from three major continental populations, i.e., European, African, and Amerindian. We observed that predominantly Amerindian ancestral components were associated with increased HOMA-IR (β = 0.124, P = 1.64 × 10(-7)). The correlation was more significant in males (Amerindian β = 0.165, P = 5.08 × 10(-7)) than in females (Amerindian β = 0.079, P = 0.019). This unique study design demonstrates how genomic markers for quantitative ancestral information can be used in admixed populations to predict phenotypic traits such as insulin resistance.
OGTT results in obese adolescents with normal HOMA-IR values.
Sahin, Nursel Muratoglu; Kinik, Sibel Tulgar; Tekindal, Mustafa Agah
2013-01-01
To investigate insulin resistance (IR) with OGTT in obese adolescents who have normal fasting insulin and homeostasis model assessment for insulin resistance (HOMA-IR). A total of 97 obese adolescents who had values of HOMA-IR IR using an insulin peak of ≥150 μU/mL (1041.8 pmol/L) and/or ≥75 μU/mL (520.9 pmol/L) 120 min after glucose charge and the sum of insulin levels >2083.5 pmol/L (300 μU/mL) in OGTT. IR risk factors were defined as family history of diabetes mellitus, acanthosis nigricans (AN), and hepatic steatosis. IR was detected in 61 (62.9%) patients. The IR group had significantly more frequent AN (p=0.0001). As the number of risk factors increased, the frequency of IR also increased (p=0.01). We advise to perform OGTT in obese adolescents with normal HOMA-IR, if they have risk factors for IR.
van Dielen, Francois M H; Nijhuis, Jeroen; Rensen, Sander S M; Schaper, Nicolaas C; Wiebolt, Janneke; Koks, Afra; Prakken, Fred J; Buurman, Wim A; Greve, Jan Willem M
2010-01-01
The low-grade inflammatory condition present in morbid obesity is thought to play a causative role in the pathophysiology of insulin resistance (IR). Bariatric surgery fails to improve this inflammatory condition during the first months after surgery. Considering the close relation between inflammation and IR, we conducted a study in which insulin sensitivity was measured during the first months after bariatric surgery. Different methods to measure IR shortly after bariatric surgery have given inconsistent data. For example, the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) levels have been reported to decrease rapidly after bariatric surgery, although clamp techniques have shown sustained insulin resistance. In the present study, we evaluated the use of steady-state plasma glucose (SSPG) levels to assess insulin sensitivity 2 months after bariatric surgery. Insulin sensitivity was measured using HOMA-IR and SSPG levels in 11 subjects before surgery and at 26% excess weight loss (approximately 2 months after restrictive bariatric surgery). The SSPG levels after 26% excess weight loss did not differ from the SSPG levels before surgery (14.3 +/- 5.4 versus 14.4 +/- 2.7 mmol/L). In contrast, the HOMA-IR values had decreased significantly (3.59 +/- 1.99 versus 2.09 +/- 1.02). During the first months after restrictive bariatric surgery, we observed a discrepancy between the HOMA-IR and SSPG levels. In contrast to the HOMA-IR values, the SSPG levels had not improved, which could be explained by the ongoing inflammatory state after bariatric surgery. These results suggest that during the first months after restrictive bariatric surgery, HOMA-IR might not be an adequate marker of insulin sensitivity. Copyright 2010 American Society for Metabolic and Bariatric Surgery. Published by Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Seo Hee Lee
2011-08-01
Full Text Available BackgroundRecent studies indicate postprandial triglyceride (TG had a better association with cardiovascular events and metabolic syndrome than fasting TG. The authors of the present study investigated the metabolic and clinical relevance of postprandial TG.MethodsIn a cross-sectional retrospective study, the authors of the present study compared fasting and postprandial TG and analyzed the relationship between postprandial TG and various demographic and metabolic parameters in 639 Korean subjects with type 2 diabetes (T2D, group I, n=539 and impaired fasting glucose (IFG, group II, n=100 after ingestion of a standardized liquid meal (total 500 kcal, 17.5 g fat, 68.5 g carbohydrate, and 17.5 g protein.ResultsFasting and postprandial TG were significantly correlated (r=0.973, r=0.937, P<0.001 in group I and II, respectively. Of the variables, total cholesterol, waist circumference and body mass index were significantly correlated with fasting and postprandial TG in both groups. Only postprandial TG showed a significant correlation with glucose metabolic parameters (e.g., postprandial glucose, homeostatic model assessment of insulin resistance [HOMA-IR], and fasting C-peptide in subjects with T2D. Multiple regression analysis showed fasting TG and HOMA-IR could be predictable variables for postprandial TG in subjects with T2D.ConclusionPostprandial TG was very strongly correlated with fasting TG. The authors of the present study suggest insulin resistance may be more associated with postprandial TG than fasting TG in Korean T2D patients on a low-fat diet.
PEDIATRIC VISCERAL ADIPOSITY INDEX ADAPTATION CORRELATES WITH HOMA-IR, MATSUDA, AND TRANSAMINASES.
Hernández, María José Garcés; Klünder, Miguel; Nieto, Nayely Garibay; Alvarenga, Juan Carlos López; Gil, Jenny Vilchis; Huerta, Samuel Flores; Siccha, Rosa Quispe; Hernandez, Joselin
2018-03-01
Visceral adiposity index (VAI) is a mathematical model associated with cardiometabolic risk in adults, but studies on children failed to support this association. Our group has proposed a pediatric VAI model using pediatric ranges, but it has not yet been evaluated and needs further adjustments. The objective of this study was to further adjust the proposed pediatric VAI by age, creating a new pediatric metabolic index (PMI), and assess the correlation of the PMI with insulin resistance indexes and hepatic enzymes. A cross-sectional design with data from 396 children (age 5 to 17 years) was analyzed with a generalized linear model to find the coefficients for triglycerides, high-density-lipoprotein cholesterol, and waist circumference-body mass index quotient. The model was constructed according to sex and age and designated PMI. A cross-validation analysis was performed and a receiver operating characteristic curve was used to determine cut-off points. Significant moderate correlation was found between PMI and homeostatic model assessment of insulin resistance (HOMA-IR) ( r = 0.452; P = .003), Matsuda ( r = -0.366; P = .019), alanine aminotransferase ( r = 0.315, P = .045), and γ-glutamyltransferase ( r = 0.397; P = .010). A PMI score >1.7 was considered as risk. PMI correlates with HOMA-IR, Matsuda, and hepatic enzymes. It could be helpful for identifying children at risk for cardiometabolic diseases. ALT = alanine transaminase BMI = body mass index GGT = γ-glutamyltransferase HDL-C = high-density-lipoprotein cholesterol HOMA-IR = homeostatic model assessment of insulin resistance hs-CRP = high sensitivity C-reactive protein ISI = insulin sensitivity index NAFLD = nonalcoholic fatty liver disease PMI = pediatric metabolic index QUICKI = quantitative insulin sensitivity check index ROC = receiver operating characteristic TG = triglyceride TNF-α = tumor necrosis factor-alpha VAI = visceral adiposity index VAT = visceral adipose tissue WC = waist circumference.
Ruijgrok, Carolien; Dekker, Jacqueline M; Beulens, Joline W; Brouwer, Ingeborg A; Coupé, Veerle M H; Heymans, Martijn W; Sijtsma, Femke P C; Mela, David J; Zock, Peter L; Olthof, Margreet R; Alssema, Marjan
2018-01-01
Glycaemic markers and fasting insulin are frequently measured outcomes of intervention studies. To extrapolate accurately the impact of interventions on the risk of diabetes incidence, we investigated the size and shape of the associations of fasting plasma glucose (FPG), 2 h post-load glucose (2hPG), HbA 1c , fasting insulin and HOMA-IR with incident type 2 diabetes mellitus. The study population included 1349 participants aged 50-75 years without diabetes at baseline (1989) from a population-based cohort in Hoorn, the Netherlands. Incident type 2 diabetes was defined by the WHO 2011 criteria or known diabetes at follow-up. Logistic regression models were used to determine the associations of the glycaemic markers, fasting insulin and HOMA-IR with incident type 2 diabetes. Restricted cubic spline logistic regressions were conducted to investigate the shape of the associations. After a mean follow-up duration of 6.4 (SD 0.5) years, 152 participants developed diabetes (11.3%); the majority were screen detected by high FPG. In multivariate adjusted models, ORs (95% CI) for incident type 2 diabetes for the highest quintile in comparison with the lowest quintile were 9.0 (4.4, 18.5) for FPG, 6.1 (2.9, 12.7) for 2hPG, 3.8 (2.0, 7.2) for HbA 1c , 1.9 (0.9, 3.6) for fasting insulin and 2.8 (1.4, 5.6) for HOMA-IR. The associations of FPG and HbA 1c with incident diabetes were non-linear, rising more steeply at higher values. FPG was most strongly associated with incident diabetes, followed by 2hPG, HbA 1c , HOMA-IR and fasting insulin. The strong association with FPG is probably because FPG is the most frequent marker for diabetes diagnosis. Non-linearity of associations between glycaemic markers and incident type 2 diabetes should be taken into account when estimating future risk of type 2 diabetes based on glycaemic markers.
Park, J-M; Lee, D-C; Lee, Y-J
2017-05-01
Increasing evidence has indicated that insulin resistance is associated with inflammation. However, few studies have investigated the association between white blood cell (WBC) count and insulin resistance, as measured by a homeostasis model assessment of insulin resistance (HOMA-IR) in a general pediatric population. This study aimed to examine the association between WBC count and insulin resistance as measured by HOMA-IR in a nationally representative sample of children and adolescents. In total, 2761 participants (1479 boys and 1282 girls) aged 10-18 years were selected from the 2008-2010 Korean National Health and Nutrition Examination Survey. Insulin resistance was defined as a HOMA-IR value greater than the 90th percentile. The odds ratios and 95% confidence intervals for insulin resistance were determined using multiple logistic regression analysis. The mean values of most cardiometabolic variables tended to increase proportionally with WBC count quartiles. The prevalence of insulin resistance significantly increased in accordance with WBC count quartiles in both boys and girls. Compared to individuals in the lowest WBC count quartile, the odds ratio for insulin resistance for individuals in the highest quartile was 2.84 in boys and 3.20 in girls, after adjusting for age, systolic blood pressure, body mass index, and waist circumference. A higher WBC count was positively associated with an increased risk of insulin resistance in Korean children and adolescents. This study suggests that WBC count could facilitate the identification of children and adolescents with insulin resistance. Copyright © 2017 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.
Kurl, Sudhir; Zaccardi, Francesco; Onaemo, Vivian N; Jae, Sae Young; Kauhanen, Jussi; Ronkainen, Kimmo; Laukkanen, Jari A
2015-02-01
Whether glucose and insulin are differently associated with the risk of coronary heart disease (CHD) mortality is unclear. We aimed to estimate the association between insulin resistance (estimated by the homeostasis model assessment for insulin resistance, HOMA-IR), fasting serum insulin (FI) and fasting plasma glucose (FPG) with incident CHD mortality in a prospective study including middle-aged nondiabetic Finnish men. During an average follow-up of 20 years, 273 (11 %) CHD deaths occurred. In a multivariable Cox regression analysis adjusted for age, body mass index, systolic blood pressure, serum LDL-cholesterol, cigarette smoking, history of CHD, alcohol consumption, blood leukocytes and plasma fibrinogen, the hazard ratios (HRs) for CHD mortality comparing top versus bottom quartiles were as follows: 1.69 (95 % CI: 1.15-2.48; p = 0.008) for HOMA-IR; 1.59 (1.09-2.32; p = 0.016) for FI; and 1.26 (0.90-1.76; p = 0.173) for FPG. These findings suggest that IR and FI, but not FPG, are independent risk factors for CHD mortality. Further studies could help clarify these results in terms of screening and risk stratification, causality of the associations, and therapeutical implications.
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José Arturo Hernández Yero
2011-08-01
. Conclusiones: una sola determinación de insulinemia para estudiar el HOMA-IR es de utilidad en la práctica diaria, aunque resulta de mucha mayor precisión aplicar la fórmula original para una mejor reproducibilidad.Introduction: HOMA-IR index is a simple and not much invasive procedure allowing by a validated and well established formula to specify exactly a value expression of insulin resistance. To estimate the HOMA-IR index with an only numerical value of insulinemia could to present a greater variability, something that could be solved with at least the mean of three insulinemias, according to original formula. En some studies it is habitual to perform it with a single insulinemia determination. Thus, we conducted a study in patients presenting type 2 diabetes and to compare the results as regards the sensitivity and specificity with a single blood determination for insulinemia and glycemia. Objectives: to assess the sensitivity and specificity of each of insulinemia determinations performed each 5 min versus the mean of these determinations during the application of formula for HOMA-IR index. Methods: sixty patients diagnosed with type 2 diabetes attended in the Center for Diabetic Care of La Habana were studied. They had a diabetes course time lower than 5 years as average with predominance of body excess weight recruited during 6 months in a consecutive way and the carrying out of fasting insulinemias and glycemias determinations by trocar and venous blood extraction at 0,5 and 10 min for a calculation of Mathews's hemostatic model known as HOMA-IR. Results: a 88,3 % had a HOMA-IR greater than 3,2. The sensitivity of a single sample of insulinemia, although high to confirm the insulin resistance diagnosis, it is variable and specificity of one of samples was low with a 14%. There is an appropriate concordance among the positive predictor values with sensitivity and the negative predictive values with the test specificity. Conclusions: a single insulinemia
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Jin-Ho Kim
2017-12-01
Full Text Available Background : Vitamin D and calcium are important factors involved in the regulation of blood glucose and insulin secretion. The Homeostatic Model Assessment of Insulin Resistance (HOMA-IR score is a useful variable for evaluating insulin resistance, and therefore we cross-sectionally compared HOMA-IR scores according to serum vitamin D levels and dietary calcium intake. Methods : We selected data from healthy males (n=5,163 and females (n=7,506 analyzed over 5 years (2008–2012 via the Korea National Health and Nutrition Examination Survey (KNHANES. We calculated HOMA-IR scores and compared them according to serum 25-hydroxyvitamin D (25(OHD concentration classification (30 ng/mL and dietary calcium quintile after adjustment for relevant variables using complex sample analysis. Comparisons were done after data weighting. Results : The mean dietary calcium intake in males and females was 558.1 mg/day and 445.9 mg/day, respectively. The mean serum 25(OHD concentration in males and females was 19.4 ng/mL and 16.8 ng/mL, respectively. After adjustment for relevant variables, HOMA-IR score was significantly correlated with serum 25(OHD concentration and dietary calcium intake in females, whereas it was only correlated with serum 25(OHD concentration in males. HOMA-IR was significantly lower in the top quintile of dietary calcium intake (mean, 866 mg/day within females with vitamin D deficiency (P=0.047. Conclusion : Adequate dietary calcium intake may be important for normal HOMA-IR in females with vitamin D deficiency.
Gutierrez-Buey, Gala; Núñez-Córdoba, Jorge M; Llavero-Valero, María; Gargallo, Javier; Salvador, Javier; Escalada, Javier
2017-06-01
Non-alcoholic fatty liver disease (NAFLD) is the commonest hepatic disease in many parts of the World, with particularly high prevalence in patients with type 2 diabetes (T2DM). However, a good screening test for NAFLD in T2DM has not been established. Insulin resistance (IR) has been associated with NAFLD, and homeostatic model assessment of insulin resistance (HOMA-IR), a good proxy for IR, may represent an affordable predictive test which could be easily applied in routine clinical practice. We aimed to evaluate the diagnostic accuracy of HOMA-IR for NAFLD in T2DM and sought to estimate an optimal cut-off value for discriminating NAFLD from non-NAFLD cases. We conducted a retrospective analysis of 56 well-controlled patients with T2DM (HbAc1HOMA-IR and NAFLD was found (OR 1.5; 95% CI: 1.03-2.1; p=0.033), independently of transaminases, fat percentage, BMI and triglyceride levels. The AUROC curve of HOMA-IR for identifying NAFLD was 80.7% (95% CI: 68.9-92.5). A value of HOMA-IR of 4.5 was estimated to be an optimal threshold for discriminating NAFLD from non-NAFLD cases. HOMA-IR is independently associated with the presence of NAFLD in adults with T2DM, and might potentially be applied in clinical practice as a screen for this condition. Copyright © 2017 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.
Nutritional status, lipid profile and HOMA-IR in post-liver transplant patients.
Da Silva Alves, Vanessa; Hack Mendes, Roberta; Pinto Kruel, Cleber Dario
2014-05-01
A high prevalence of overweight, obesity, diabetes and dyslipidemia has been reported following liver transplantation (LT). Although these conditions are known to induce an increased risk for cardiovascular events, which are among the major causes of death in post-LT patients, much debate remains in the literature regarding the applicability of different nutritional assessments methods to this population. To assess the nutritional status, lipid profile, homeostatic model assessment of insulin resistance (HOMA-IR) and dietary intake adequacy in the post-LT period. Cross-sectional study of patients after a maximum of 2 years post-LT, involving the assessment of body mass index (BMI), percent weight loss, arm (AC) and arm muscle circumference (AMC), triceps skinfold (TSF), neck (NC) and waist (WC) circumference, lipid profile, HOMA-IR and percent adequacy of dietary intake. In the group of 36 patients, 61.1% were male, mean age 53.2 years (± 10.6). Severe weight loss was noted in 66.7% of patients. Most individuals were eutrophic according to BMI, AC and AMC, while TSF showed malnutrition, NC demonstrated overweight and WC showed metabolic risk. Dyslipidemia was diagnosed in 87.5% of patients, and insulin resistance in 57% of the patients. Most patients had adequate dietary intake, although the time since transplant was positively correlated with AC (r = 0.353; p = 0.035) and negatively correlated with vitamin A intake (r = - 0.382; p = 0.022), with the caloric adequacy (r = -0.338; p = 0.044) and vitamin A adequacy (r = -0.382; p = 0.021). Although anthropometry provided somewhat variable nutritional diagnoses, when combined with biochemical tests, findings showed the prevalence of cardiovascular risk. As such, patients should be provided with transdisciplinary assistance, and strategies should be developed so as to reduce the risk factors recorded in this population. Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.
Silva, Maria Inês Barreto; Lemos, Carla Cavalheiro da Silva; Torres, Márcia Regina Simas Gonçalves; Bregman, Rachel
2014-03-01
Chronic kidney disease (CKD) is associated with metabolic disorders, including insulin resistance (IR), mainly when associated with obesity and characterized by high abdominal adiposity (AbAd). Anthropometric measures are recommended for assessing AbAd in clinical settings, but their accuracies need to be evaluated. The aim of this study was to evaluate the precision of different anthropometric measures of AbAd in patients with CKD. We also sought to determine the AbAd association with high homeostasis model assessment index of insulin resistance (HOMA-IR) values and the cutoff point for AbAd index to predict high HOMA-IR values. A subset of clinically stable nondialyzed patients with CKD followed at a multidisciplinary outpatient clinic was enrolled in this cross-sectional study. The accuracy of the following anthropometric indices: waist circumference, waist-to-hip ratio, conicity index and waist-to-height ratio (WheiR) to assess AbAd, was evaluated using trunk fat, by dual x-ray absorptiometry (DXA), as a reference method. HOMA-IR was estimated to stratify patients in high and low HOMA-IR groups. The total area under the receiver-operating characteristic curves (AUC-ROC; sensitivity/specificity) was calculated: AbAd with high HOMA-IR values (95% confidence interval [CI]). We studied 134 patients (55% males; 54% overweight/obese, body mass index ≥ 25 kg/m(2), age 64.9 ± 12.5 y, estimated glomerular filtration rate 29.0 ± 12.7 mL/min). Among studied AbAd indices, WheiR was the only one to show correlation with DXA trunk fat after adjusting for confounders (P HOMA-IR values (r = 0.47; P HOMA-IR values was 0.55 (AUC-ROC = 0.69 ± 0.05; 95% CI, 0.60-0.77; sensitivity/specificity, 68.9/61.9). WheiR is recommended as an effective and precise anthropometric index to assess AbAd and to predict high HOMA-IR values in nondialyzed patients with CKD. Copyright © 2014 Elsevier Inc. All rights reserved.
The association of osteopenia with levels of serum 25-hydroxyvitamin D and HOMA-IR values.
Yoldemir, T; Yavuz, D G
2014-06-01
To determine the association of osteopenia with levels of serum 25-hydroxyvitamin D and HOMA-IR values in postmenopausal women. Methods One hundred healthy postmenopausal women were included in a cross-sectional study. Venous blood was collected after an overnight fast and 25-hydroxyvitamin D, glucose and insulin levels were measured. HOMA-IR was calculated. Bone mineral density was measured with a dual X-ray absorptiometer. There was no difference in serum 25-hydroxyvitamin D levels and HOMA-IR values between the two groups. A weak positive correlation between serum 25-hydroxyvitamin D levels and osteopenia was detected. Insulin resistance had a weak negative association with osteopenia. The correlations between osteopenia and serum 25-hydroxyvitamin D levels and HOMA-IR values were weak among early postmenopausal women.
Davis, Susan R; Robinson, Penelope J; Moufarege, Alain; Bell, Robin J
2012-10-01
Sex hormone-binding globulin (SHBG) is a robust predictor of insulin resistance. Whether this is independent of circulating sex steroid levels remains uncertain. The aim of this study was to investigate the determinants of SHBG in postmenopausal women and whether the relationship between SHBG and insulin resistance is independent of oestrogen and androgen levels. A cross-sectional study of naturally and surgically menopausal women. Seven hundred and sixty three postmenopausal women not using any systemic hormone therapy, mean age 54·4 ± 5·8 years, recruited in the US, Canada, Australia, UK and Sweden between July 2004 and February 2005. Relationships between log-transformed (ln) SHBG and ln homoeostasis model assessment for insulin resistance (HOMA-IR) were explored, taking into account age, body mass index (BMI), blood pressure (BP) and circulating oestradiol, oestrone, testosterone and dihydrotestosterone. Taking into account age, race, years since menopause, menopause type, BMI, BP, prior postmenopausal hormone use and the sex steroids measured, 34·4% of the variation in SHBG could be explained by the model that included negative contributions by HOMA-IR, BMI and diastolic BP, and a positive contribution by total testosterone (P HOMA-IR, which was best explained by the model that included BMI, SHBG, systolic BP and surgical menopause, with each variable being positively related to HOMA-IR (r(2) = 0·3152, P = 0·03). The relationship between SHBG and HOMA-IR, as an estimate of insulin resistance, is not explained by endogenous oestrogen and androgen levels and is, at least in part, independent of BMI in postmenopausal women. © 2011 Blackwell Publishing Ltd.
The correlation between serum AMH and HOMA-IR among PCOS phenotypes.
Wiweko, Budi; Indra, Indra; Susanto, Cynthia; Natadisastra, Muharam; Hestiantoro, Andon
2018-02-09
Polycystic ovarian syndrome (PCOS) is known to be one of the most prevalent endocrine disorders affecting reproductive age women. One of the endocrine disorder is hyperinsulinemia, which corresponds with the severity of PCOS. However, the pathogenesis of PCOS is not fully understood, but one theory of anti-mullerian hormone (AMH) has been proposed as one of the factor related to the degree of severity of PCOS. However, there are no clear correlation between levels of AMH with the incidence of insulin resistance in PCOS patients especially in Indonesia. This is a cross-sectional study involving reproductive age women aged 18-35 years. Subjects were recruited consecutively at Dr. Cipto Mangunkusumo General Hospital between 2011 until 2014. PCOS women diagnosed using 2003 Rotterdam criteria were categorized into four different PCOS phenotypes. Subsequently, serum level of AMH and HOMA-IR was measured and evaluated with correlation tests performed using SPSS 11.0 RESULTS: A total of 125 PCOS patients were included in a study conducted within a 3-year period. Phenotype 1 (anovulation, hyperandrogenism, and polycystic ovaries) shows the highest levels of AMH and HOMA-IR, which decreases in accordance to severity level (p HOMA-IR persisted even after adjusting for BMI in multivariate analysis. There was a positive correlation between serum AMH and HOMA IR levels. Serum AMH and HOMA IR levels were significantly different across the four PCOS phenotypes; with the highest values were present with phenotype 1.
Kurtoğlu, Selim; Hatipoğlu, Nihal; Mazıcıoğlu, Mümtaz; Kendirici, Mustafa; Keskin, Mehmet; Kondolot, Meda
2010-01-01
Childhood obesity is associated with an increased risk for insulin resistance. The underlying mechanism for the physiological increase in insulin levels in puberty is not clearly understood. The aim of the present study was to determine the cut-off values for homeostasis model assessment for insulin resistance (HOMA-IR) in obese children and adolescents according to gender and pubertal status. Two hundred and eight obese children and adolescents (141 girls, 127 boys) aged between 5 and 18 years were included in the study. The children were divided into prepubertal and pubertal groups. A standard oral glucose tolerance test (OGTT) was carried out in all children. A total insulin level exceeding 300 μU/mL in the blood samples, collected during the test period, was taken as the insulin resistance criterion. Cut-off values for HOMA-IR were calculated by receiver operating characteristic (ROC) analysis. In the prepubertal period, the rate of insulin resistance was found to be 37% in boys and 27.8% in girls,while in the pubertal period, this rate was 61.7% in boys and 66.7% in girls. HOMA-IR cut-off values for insulin resistance in the prepubertal period were calculated to be 2.67 (sensitivity 88.2%, specificity 65.5%) in boys and 2.22 (sensitivity 100%, specificity 42.3%) in girls, and in the pubertal period, they were 5.22 (sensitivity 56%, specificity 93.3%) in boys and 3.82 (sensitivity 77.1%, specificity 71.4%) in girls. Since gender, obesity and pubertal status are factors affecting insulin resistance, cut-off values which depend on gender and pubertal status, should be used in evaluation of insulin resistance.
Motamed, Nima; Miresmail, Seyed Javad Haji; Rabiee, Behnam; Keyvani, Hossein; Farahani, Behzad; Maadi, Mansooreh; Zamani, Farhad
2016-03-01
The present study was carried out to determine the optimal cutoff points for homeostatic model assessment (HOMA-IR) and quantitative insulin sensitivity check index (QUICKI) in the diagnosis of metabolic syndrome (MetS) and non-alcoholic fatty liver disease (NAFLD). The baseline data of 5511 subjects aged ≥18years of a cohort study in northern Iran were utilized to analyze. Receiver operating characteristic (ROC) analysis was conducted to determine the discriminatory capability of HOMA-IR and QUICKI in the diagnosis of MetS and NAFLD. Youden index was utilized to determine the optimal cutoff points of HOMA-IR and QUICKI in the diagnosis of MetS and NAFLD. The optimal cutoff points for HOMA-IR in the diagnosis of MetS and NAFLD were 2.0 [sensitivity=64.4%, specificity=66.8%] and 1.79 [sensitivity=66.2%, specificity=62.2%] in men and were 2.5 [sensitivity=57.6%, specificity=67.9%] and 1.95 [sensitivity=65.1%, specificity=54.7%] in women respectively. Furthermore, the optimal cutoff points for QUICKI in the diagnosis of MetS and NAFLD were 0.343 [sensitivity=63.7%, specificity=67.8%] and 0.347 [sensitivity=62.9%, specificity=65.0%] in men and were 0.331 [sensitivity=55.7%, specificity=70.7%] and 0.333 [sensitivity=53.2%, specificity=67.7%] in women respectively. Not only the optimal cutoff points of HOMA-IR and QUICKI were different for MetS and NAFLD, but also different cutoff points were obtained for men and women for each of these two conditions. Copyright © 2016 Elsevier Inc. All rights reserved.
Ha, Chang Ho; Swearingin, Brenda; Jeon, Yong Kyun
2015-01-01
[Purpose] This study aimed to examine the correlation of visfatin level to pancreatic endocrine hormone level, homeostasis model assessment of insulin resistance (HOMA-IR) index, and HOMA β-cell index in hydraulic resistance exercise. Furthermore, it investigated the relationship between visfatin level and other variables affected by exercise in overweight women. [Subjects and Methods] The exercise group trained for 12 weeks, 70 minutes/day, 5 days/week. Visfatin level, pancreatic endocrine h...
Peplies, Jenny; Börnhorst, Claudia; Günther, Kathrin; Fraterman, Arno; Russo, Paola; Veidebaum, Toomas; Tornaritis, Michael; De Henauw, Stefaan; Marild, Staffan; Molnar, Dénes; Moreno, Luis A; Ahrens, Wolfgang
2016-09-02
This study investigates prospective associations of anthropometrical and lifestyle indices with insulin resistance (IR) in European children from the IDEFICS cohort. Insulin resistance (IR) is a growing concern in childhood obesity and a central aspect of the metabolic syndrome (MS). It most likely represents the link between obesity and type 2 diabetes. This longitudinal study included 3348 preadolescent children aged 3 to 10.9 years from 8 European countries who were observed from 2007/2008 to 2009/2010. The main outcome measure in the present analysis is HOMA-IR (homeostasis model assessment as a common proxy indicator to quantify IR) at follow-up and in its longitudinal development. Anthropometrical measures and lifestyle indices, including objectively determined physical activity, were considered, among others factors, as determinants of IR. Prospective associations between IR at follow-up and anthropometrical and lifestyle indices were estimated by logistic regression models. Country-specific prevalence rates of IR in the IDEFICS cohort of European children showed a positive trend with weight category. Prospective multivariate analyses showed the strongest positive associations of IR with BMI z-score (OR = 2.6 for unit change from the mean, 95 % CI 2.1-3.1) and z-score of waist circumference (OR = 2.2 for unit change from the mean, 95 % CI 1.9-2.6), which were analysed in separate models, but also for sex (OR = 2.2 for girls vs. boys, 95 % CI 1.5-3.1 up to OR 2.5, 95 % CI 1.8-3.6 depending on the model), audio-visual media time (OR = 1.2 for an additional hour per day, 95 % CI 1.0-1.4 in both models) and an inverse association of objectively determined physical activity (OR = 0.5 for 3(rd) compared to 1(st) quartile, 95 % CI 0.3-0.9 in both models). A longitudinal reduction of HOMA-IR was accompanied with a parallel decline in BMI. This study is, to our knowledge, the first prospective study on IR in a preadolescent children
Relation with HOMA-IR and thyroid hormones in obese Turkish women with metabolic syndrome.
Topsakal, S; Yerlikaya, E; Akin, F; Kaptanoglu, B; Erürker, T
2012-03-01
The aim of this study was to investigate the relationship between insulin resistance and thyroid function in obese pre- and postmenopausal women with or without metabolic syndrome (MetS). 141 obese women were divided into two groups, HOMA-IRHOMA-IR>2.7, to evaluate relation with HOMA-IR and fatness, hormone and blood parameters. They were then divided into four groups as pre- and postmenopausal with or without MetS. Various fatness, hormone and blood parameters were examined. Statistically significant difference was found in weight, body mass index (BMI), waist circumference, fat%, fasting insulin, TSH, FT3, FT4, FSH, Anti-microsomal antibody (ANTIM) and triglycerides levels in HOMA-IRHOMA-IR>2.7 obese Turkish women. This study showed that age, weight, BMI, waist circumference, fat%, fasting insulin, FT3, ANTIM, FSH, LH, total cholesterol, triglycerides, HDL, HOMA-IR, systolic and diastolic blood pressure levels were related in preand post menopausal status in obese women with or without MetS. Obesity may influence the levels of thyroid hormones and increases the risk of MetS in women. Postmenopausal status with MetS is associated with an increased TSH, FT3 and FT4 levels and HOMA-IR in obese women. Strong relation was observed with MetS and TSH and FT3 levels.
Moore, Amy; Hochner, Hagit; Sitlani, Colleen M; Williams, Michelle A; Hoofnagle, Andrew N; de Boer, Ian H; Kestenbaum, Bryan; Siscovick, David S; Friedlander, Yechiel; Enquobahrie, Daniel A
2015-01-01
Objective To examine cross-sectional relationships between plasma vitamin D and Cardiometabolic Risk Factors in young adults. Design Data were collected from interviews, physical examinations, and biomarker measurements. Total plasma 25-hydroxyvitamin D (25[OH]D) was measured using liquid chromatography-tandem mass spectrometry. Associations between 25[OH]D and CMR were modeled using weighted linear regression with robust standard error estimates. Setting Individuals born in Jerusalem during 1974-1976. Subjects Participants of the Jerusalem Perinatal Study (n = 1,204) interviewed and examined at age 32 years. Participants were oversampled for low and high birthweight and for maternal pre-pregnancy obesity. Results Mean total 25[OH]D concentration among participants was 21.7 ng/mL (SD 8.9). Among males, 25[OH]D was associated with Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) (natural log-transformed, β = -0.011, p = 0.004) after adjustment for body mass index. However, these associations were not present among females (p for sex interaction = 0.005). Conclusions We found evidence for inverse associations of 25[OH]D with markers of insulin resistance among males, but not females, in a health, young adult Caucasian population. Prospective studies and studies conducted on other populations investigating sex specific effects of vitamin D on CMR are warranted. PMID:25145881
Peplies, J; Jiménez-Pavón, D; Savva, S C; Buck, C; Günther, K; Fraterman, A; Russo, P; Iacoviello, L; Veidebaum, T; Tornaritis, M; De Henauw, S; Mårild, S; Molnár, D; Moreno, L A; Ahrens, W
2014-09-01
The aim of this study is to present age- and sex-specific reference values of insulin, glucose, glycosylated haemoglobin (HbA1c) and the homeostasis model assessment to quantify insulin resistance (HOMA-IR) for pre-pubertal children. The reference population consists of 7074 normal weight 3- to 10.9-year-old pre-pubertal children from eight European countries who participated in at least one wave of the IDEFICS ('identification and prevention of dietary- and lifestyle-induced health effects in children and infants') surveys (2007-2010) and for whom standardised laboratory measurements were obtained. Percentile curves of insulin (measured by an electrochemiluminescence immunoassay), glucose, HbA1c and HOMA-IR were calculated as a function of age stratified by sex using the general additive model for location scale and shape (GAMLSS) method. Levels of insulin, fasting glucose and HOMA-IR continuously show an increasing trend with age, whereas HbA1c shows an upward trend only beyond the age of 8 years. Insulin and HOMA-IR values are higher in girls of all age groups, whereas glucose values are slightly higher in boys. Median serum levels of insulin range from 17.4 and 13.2 pmol l(-1) in 3-HOMA-IR, median values range from 0.5 and 0.4 in 3-<3.5-year-old girls and boys to 1.7 and 1.4 in 10.5-<11-year-old girls and boys, respectively. Our study provides the first standardised reference values for an international European children's population and provides the, up to now, largest data set of healthy pre-pubertal children to model reference percentiles for markers of insulin resistance. Our cohort shows higher values of Hb1Ac as compared with a single Swedish study while our percentiles for the other glucose metabolic markers are in good accordance with previous studies.
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Farideh Rezaei Abhari
2014-01-01
Full Text Available Women with preeclampsia, independent of obesity and glucose intolerance, exhibit insulin resistance during pregnancy. The purpose of the present study is to determine whether early diagnosis of insulin resistance during pregnancy can predict preeclampsia. Through a case-control study, 675 pregnant women were selected and their first trimester blood was taken. Their fasting blood glucose and insulin were also measured after diagnosis of preeclampsia by 20 weeks of pregnancy. Based on the experiments conducted on 675 women who were 20 weeks past their pregnancy, 375 cases with preeclampsia were selected and assigned to the case group. 35 other pregnant women were put in the control group. Diagnosis criteria for the participants included blood pressure above 140/90 and proteinuria above 300 mg or above +1. Both groups were matched according to age, parity, gestational age, and BMI. Homa-Irand rate of insulin resistance was calculated by HOMA-IR and patients were followed up. Homeostatic model assessments (HOMA-IR revealed that the average insulin resistance increased during pregnancy among both the case and control groups. There was a significant difference between insulin resistance of these two groups in both first trimester and third trimester and after developing preeclampsia (P < 0.001, P = 0.021. Insulin-resistance of the group with preeclampsia was higher in first trimester prior to diagnosis as well as the third trimester after diagnosis compared to natural pregnancy under similar conditions. Measurement of insulin resistance in first trimester may be useful in predicting the risk of preeclampsia.
Abhari, Farideh Rezaei; Ghanbari Andarieh, Maryam; Farokhfar, Asadollah; Ahmady, Soleiman
2014-01-01
Women with preeclampsia, independent of obesity and glucose intolerance, exhibit insulin resistance during pregnancy. The purpose of the present study is to determine whether early diagnosis of insulin resistance during pregnancy can predict preeclampsia. Through a case-control study, 675 pregnant women were selected and their first trimester blood was taken. Their fasting blood glucose and insulin were also measured after diagnosis of preeclampsia by 20 weeks of pregnancy. Based on the experiments conducted on 675 women who were 20 weeks past their pregnancy, 375 cases with preeclampsia were selected and assigned to the case group. 35 other pregnant women were put in the control group. Diagnosis criteria for the participants included blood pressure above 140/90 and proteinuria above 300 mg or above +1. Both groups were matched according to age, parity, gestational age, and BMI. Homa-Irand rate of insulin resistance was calculated by HOMA-IR and patients were followed up. Homeostatic model assessments (HOMA-IR) revealed that the average insulin resistance increased during pregnancy among both the case and control groups. There was a significant difference between insulin resistance of these two groups in both first trimester and third trimester and after developing preeclampsia (P < 0.001, P = 0.021). Insulin-resistance of the group with preeclampsia was higher in first trimester prior to diagnosis as well as the third trimester after diagnosis compared to natural pregnancy under similar conditions. Measurement of insulin resistance in first trimester may be useful in predicting the risk of preeclampsia.
Isokuortti, Elina; Zhou, You; Peltonen, Markku; Bugianesi, Elisabetta; Clement, Karine; Bonnefont-Rousselot, Dominique; Lacorte, Jean-Marc; Gastaldelli, Amalia; Schuppan, Detlef; Schattenberg, Jörn M.; Hakkarainen, Antti; Lundbom, Nina; Jousilahti, Pekka; Männistö, Satu; Keinänen-Kiukaanniemi, Sirkka
2017-01-01
Aims/hypothesis\\ud \\ud Recent European guidelines for non-alcoholic fatty liver disease (NAFLD) call for reference values for HOMA-IR. In this study, we aimed to determine: (1) the upper limit of normal HOMA-IR in two population-based cohorts; (2) the HOMA-IR corresponding to NAFLD; (3) the effect of sex and PNPLA3 genotype at rs738409 on HOMA-IR; and (4) inter-laboratory variations in HOMA-IR.\\ud \\ud Methods\\ud \\ud We identified healthy individuals in two population-based cohorts (FINRISK 20...
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Ana Carolina Junqueira Vasques
2009-11-01
umbilical, menor cintura, inmediatamente superior a la cresta ilíaca y punto promedio entre la cresta ilíaca y la última costilla se calcularon en cuatro locales diferentes. La resistencia a la insulina se evaluó por el índice HOMA-IR. RESULTADOS: Todas las mediciones presentaron correlación intraclase de 0,986-0,999. Tanto el diámetro abdominal sagital calculado en la menor cintura (r=0,482 y AUC=0,739±0,049 como el perímetro de la cintura calculado en el punto promedio entre la última costilla y la cresta ilíaca (r=0,464 e AUC=0,746±0,05 presentaron mayores correlaciones con el HOMA-IR, así como un mejor poder discriminante para el HOMA-IR según el análisis ROC (pBACKGROUND: The correlation between the increase in visceral fat and insulin resistance makes the sagittal abdominal diameter and the waist perimeter as potential tools for the prediction of insulin resistance. OBJECTIVE: To assess the reproducibility of different measurements of the sagittal abdominal diameter and the waist perimeter and analyze the discriminating power of the measurements when predicting insulin resistance. METHODS: A total of 190 adult males were studied. The sagittal abdominal diameter (smallest girth, larger abdominal diameter, umbilical level and midpoint between the iliac crests and the waist perimeter (umbilical level, smallest girth, immediately above the iliac crest and midpoint between the iliac crest and the last rib were measured at four different sites. Insulin resistance was assessed by the homeostasis model of assessment-insulin resistance (HOMA-IR index. RESULTS: All measurements presented an intraclass correlation of 0.986-0.999. The sagittal abdominal diameter measured at the smallest girth (r=0.482 and AUC=0.739±0.049 and the waist perimeter measured at the midpoint between the last rib and the iliac crest (r=0.464 and AUC=0.746±0.05 presented the highest correlations with the HOMA-IR and the best discriminating power for HOMA-IR according to the ROC analysis
Lan, Jianjun; Chen, Xiaoni; Chen, Xiaoping; Wang, Si; Zhang, Xin; Wu, Kai; He, Sen; Peng, Yong; Jiang, Lingyun; Li, Longxin; Wan, Liyan
2011-10-01
To investigate the correlation between serum visfatin and insulin resistance (IR) in non-diabetic essential hypertensive (EH) patients with and without IR, and to evaluate the effect of antihypertensive treatment on serum visfatin and IR in these patients. A total of 81 non-diabetic EH patients, including 54 with IR and 27 without IR, were enrolled. After two weeks wash-out, patients with IR were randomly assigned to telmisartan (group T) or amlodipine (group A) for 6 months. Blood samples were taken before and after treatment for measurement of routine biochemical parameters, visfatin and insulin resistance (measured by HOMA-IR). Visfatin was independently correlated with HOMA-IR (r=0.845, P=0.000). After 6 months of treatment, both drugs lowered HOMA-IR, more significantly so in group T than group A (P=0.010). Serum visfatin levels increased in group T but decreased in group A. Serum visfatin levels were higher in non-diabetic EH patients with IR compared with those without IR. Visfatin is independently correlated with HOMA-IR. Telmisartan lowers HOMA-IR to a greater extent than amlodipine. Interestingly, serum visfatin increased with telmisartan yet decreased with amlodipine treatment. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.
Yang, Yue; Wei, Ri-bao; Xing, Yue; Tang, Lu; Zheng, Xiao-yong; Wang, Zi-cheng; Gao, Yu-wei; Li, Min-xia; Chen, Xiang-mei
2013-12-01
This study compared the efficacy of angiotensin receptor blockers (ARBs) and calcium channel blockers (CCBs) in the effect of insulin resistance (IR) as assessed using the homeostasis model assessment of insulin resistance (HOMA-IR) in non-diabetic patients. The MEDLINE, EMBASE, and Cochrane Library databases were searched to identify studies published before December 2012 that investigated the use of ARBs and CCBs to determine the effect on the HOMA-IR index in non-diabetics. Parameters on IR and blood pressure were collected. Review Manager 5.2 and Stata 12.0 were used to perform the meta-analysis. Fixed and random effects models were applied to various aspects of the meta-analysis, which assessed the therapeutic effects of the two types of drug using the HOMA-IR index in non-diabetic patients. The meta-analysis included five clinical trials. Patient comparisons before and after treatment with ARBs and CCBs revealed that ARBs reduced the HOMA-IR index (weighted mean difference (WMD) -0.65, 95% confidence interval (CI) -0.93 to -0.38) and fasting plasma insulin (FPI) (WMD -2.01, 95% CI -3.27 to -0.74) significantly more than CCBs. No significant differences in the therapeutic effects of these two types of drug on blood pressure were observed. Given that there are no significant differences in the therapeutic effects of ARBs and CCBs on blood pressure, as ARBs are superior to CCBs in their effect on the HOMA-IR index in non-diabetics, they might be a better choice in hypertension patients without diabetes. © 2013.
Hidalgo, Bertha; Irvin, M Ryan; Sha, Jin; Zhi, Degui; Aslibekyan, Stella; Absher, Devin; Tiwari, Hemant K; Kabagambe, Edmond K; Ordovas, Jose M; Arnett, Donna K
2014-02-01
Known genetic susceptibility loci for type 2 diabetes (T2D) explain only a small proportion of heritable T2D risk. We hypothesize that DNA methylation patterns may contribute to variation in diabetes-related risk factors, and this epigenetic variation across the genome can contribute to the missing heritability in T2D and related metabolic traits. We conducted an epigenome-wide association study for fasting glucose, insulin, and homeostasis model assessment of insulin resistance (HOMA-IR) among 837 nondiabetic participants in the Genetics of Lipid Lowering Drugs and Diet Network study, divided into discovery (N = 544) and replication (N = 293) stages. Cytosine guanine dinucleotide (CpG) methylation at ∼470,000 CpG sites was assayed in CD4(+) T cells using the Illumina Infinium HumanMethylation 450 Beadchip. We fit a mixed model with the methylation status of each CpG as the dependent variable, adjusting for age, sex, study site, and T-cell purity as fixed-effects and family structure as a random-effect. A Bonferroni corrected P value of 1.1 × 10(-7) was considered significant in the discovery stage. Significant associations were tested in the replication stage using identical models. Methylation of a CpG site in ABCG1 on chromosome 21 was significantly associated with insulin (P = 1.83 × 10(-7)) and HOMA-IR (P = 1.60 × 10(-9)). Another site in the same gene was significant for HOMA-IR and of borderline significance for insulin (P = 1.29 × 10(-7) and P = 3.36 × 10(-6), respectively). Associations with the top two signals replicated for insulin and HOMA-IR (P = 5.75 × 10(-3) and P = 3.35 × 10(-2), respectively). Our findings suggest that methylation of a CpG site within ABCG1 is associated with fasting insulin and merits further evaluation as a novel disease risk marker.
Bahijri, Suhad M; Alissa, Eman M; Akbar, Daad H; Ghabrah, Tawfik M
2010-01-01
Identification of insulin resistance (IR) in the general population is important for developing strategies to reduce the prevalence of non-insulin-dependent diabetes mellitus (NIDDM). We used the original and a modified version of the Quantitative Insulin Sensitivity Check Index (QUICKI, M-QUICKI), and the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) to divide non-diabetic normotensive adults into high- (HIR) and low-insulin-resistant (LIR) subgroups to investigate similarities and differences in their characteristics. Three hundred fifty-seven healthy adults aged 18-50 years were recruited randomly from health centers in Jeddah in a cross-sectional study design. Anthropometric and demographic information was taken. Insulin, glucose, lipid profile and free fatty acid were determined in fasting blood samples. M-QUICKI, HOMA-IR and QUICKI were calculated. Reported cut-off points were used to identify HIR subjects, who were then matched for age and sex to others in the study population, resulting in 3 HIR and 3 LIR subgroups. Two hundred nine subjects satisfied the selection criteria. M-QUICKI correlated significantly (P=.01) with HOMA-IR and QUICKI values. Increased adiposity was the common characteristic of the three HIR subgroups. HIR subgroups identified using M-QUICKI (97 subjects) and HOMA (25 subjects), but not QUICKI (135 subjects), had statistically different biochemical characteristics compared to corresponding LIR sub-groups. Adiposity, but not sex, is a risk factor for IR in the studied population. Further studies are needed to choose the most appropriate index for detecting IR in community-based surveys.
Zhang, Kun; Chen, Yi; Liu, Lijie; Lu, Meng; Cheng, Jing; Gao, Fengbin; Wang, Ningjian; Shen, Zhoujun; Lu, Yingli
2017-11-20
Previous studies have reported that insulin resistant and low testosterone are related. The triglyceride and glucose index (TyG) well mirrors insulin sensitivity. No study investigated the application of TyG in male hypogonadism. We aimed to explore whether TyG was associated with hypogonadism, and also evaluate the ability of TyG compared to HOMA-IR as a possible hypogonadism predictor. A total of 4299 male subjects were enrolled from 22 sites in East China. Hypogonadism was defined as total testosterone HOMA-IR (0.68, 95% CI 0.66,0.70). Thus, the TyG was significantly associated with a higher prevalence of hypogonadism in Chinese men. TyG had a better predictive power for hypogonadism than HOMA-IR.
Otake, Toshie; Fukumoto, Jin; Abe, Masao; Takemura, Shigeki; Mihn, Pham Ngoc; Mizoue, Tetsuya; Kiyohara, Chikako
2014-09-01
Insulin resistance (IR) is regarded as one of the earliest features of many metabolic diseases, and major efforts are aimed at improving insulin function to confront this issue. The aim of this study was to investigate the relationship of body mass index (BMI), cigarette smoking, alcohol intake, physical activity, green tea and coffee consumption to IR. We performed a cross-sectional study of 1542 male self defense officials. IR was defined as the highest quartile of the fasting plasma insulin (≥ 50 pmol/L) or the homeostasis model assessment-estimated IR (HOMA-IR ≥ 1.81). An unconditional logistic model was used to estimate the odds ratio (OR) and 95% confidence interval (CI) for the association between IR and influential factors. Stratified analysis by obesity status (BMI IR was significantly positively related to BMI and glucose tolerance, negatively related to alcohol use. Independent of obesity status, significant trends were observed between IR and alcohol use. Drinking 30 mL or more of ethanol per day reduced IR by less than 40%. Strong physical activity was associated with decreased risk of IR based on fasting plasma insulin only in the obese. Coffee consumption was inversely associated with the risk of IR based on HOMA-IR in the non-obese group. Higher coffee consumption may be protective against IR among only the non-obese. Further studies are warranted to examine the effect modification of the obesity status on the coffee-IR association.
Isokuortti, Elina; Zhou, You; Peltonen, Markku; Bugianesi, Elisabetta; Clement, Karine; Bonnefont-Rousselot, Dominique; Lacorte, Jean-Marc; Gastaldelli, Amalia; Schuppan, Detlef; Schattenberg, Jörn M; Hakkarainen, Antti; Lundbom, Nina; Jousilahti, Pekka; Männistö, Satu; Keinänen-Kiukaanniemi, Sirkka; Saltevo, Juha; Anstee, Quentin M; Yki-Järvinen, Hannele
2017-10-01
Recent European guidelines for non-alcoholic fatty liver disease (NAFLD) call for reference values for HOMA-IR. In this study, we aimed to determine: (1) the upper limit of normal HOMA-IR in two population-based cohorts; (2) the HOMA-IR corresponding to NAFLD; (3) the effect of sex and PNPLA3 genotype at rs738409 on HOMA-IR; and (4) inter-laboratory variations in HOMA-IR. We identified healthy individuals in two population-based cohorts (FINRISK 2007 [n = 5024] and the Programme for Prevention of Type 2 Diabetes in Finland [FIN-D2D; n = 2849]) to define the upper 95th percentile of HOMA-IR. Non-obese individuals with normal fasting glucose levels, no excessive alcohol use, no known diseases and no use of any drugs were considered healthy. The optimal HOMA-IR cut-off for NAFLD (liver fat ≥5.56%, based on the Dallas Heart Study) was determined in 368 non-diabetic individuals (35% with NAFLD), whose liver fat was measured using proton magnetic resonance spectroscopy ( 1 H-MRS). Samples from ten individuals were simultaneously analysed for HOMA-IR in seven European laboratories. The upper 95th percentiles of HOMA-IR were 1.9 and 2.0 in healthy individuals in the FINRISK (n = 1167) and FIN-D2D (n = 459) cohorts. Sex or PNPLA3 genotype did not influence these values. The optimal HOMA-IR cut-off for NAFLD was 1.9 (sensitivity 87%, specificity 79%). A HOMA-IR of 2.0 corresponded to normal liver fat (HOMA-IR measured in Helsinki corresponded to 1.3, 1.6, 1.8, 1.8, 2.0 and 2.1 in six other laboratories. The inter-laboratory CV% of HOMA-IR was 25% due to inter-assay variation in insulin (25%) rather than glucose (5%) measurements. The upper limit of HOMA-IR in population-based cohorts closely corresponds to that of normal liver fat. Standardisation of insulin assays would be the first step towards definition of normal values for HOMA-IR.
Sawathiparnich, Pairunyar; Weerakulwattana, Linda; Santiprabhob, Jeerunda; Likitmaskul, Supawadee
2005-11-01
The prevalence of obesity in Thai children is increasing. These individuals are at increased risks of metabolic syndrome that includes insulin resistance, type 2 diabetes mellitus (T2DM), polycystic ovary syndrome (PCOS), dyslipidemia and hypertension. PCOS has been known to be associated with insulin resistance. To compare the insulin sensitivity between obese adolescent girls with PCOS and those without PCOS. We reviewed demographic and hormonal data of 6 obese adolescent girls with PCOS and compared with 6 age, weight and BMI-matched non-PCOS controls. Each subject underwent an oral glucose tolerance test. Homeostasis model assessment of insulin resistance score (HOMA-IR score) in obese adolescent girls with PCOS was significantly higher than in girls without PCOS with median and range as follows (16.5 [3.8, 21.8] vs. 4.1 [3.3, 6.9], p = 0.04). Our study demonstrates that obese adolescent girls with PCOS have more severe insulin resistance measured by HOMA-IR score than girls without PCOS independent of the degree of obesity. Since insulin resistance is a metabolic precursor of future cardiovascular diseases, obese adolescent girls with PCOS might be at greater risk of developing cardiovascular disease in later adulthood than their non-PCOS counterparts.
Zhu, Yubing; Sun, Zhipeng; Du, Yanmin; Xu, Guangzhong; Gong, Ke; Zhu, Bin; Zhang, Nengwei
2017-01-01
Our purpose was to explore the remission of insulin resistance after bariatric surgery to discover the mechanism of diabetes remission excluding dietary factors. A retrospective case control study was conducted on patients with type 2 diabetes, who underwent laparoscopic sleeve gastrectomy (LSG) or laparoscopic gastric bypass surgery (LGB) in Beijing Shijitan Hospital from April 1, 2012 to April 1, 2013. The laboratory and anthropometric data was analyzed pre-surgery and during a 2-year follow-up. HOMA-IR was calculated and evaluated. The two surgical procedures were compared. No significant difference in complete remission rate was observed between the two groups (LGB group: 62.1%, LSG group: 60.0%, p = 0.892). HOMA-IR was reduced to a stable level at the 3 rd month after surgery. The cut-off value of HOMA-IR was 2.38 (sensitivity: 0.938, specificity: 0.75) and 2.33 (sensitivity: 0.941, specificity: 0.778) respectively for complete remission after LSG or LGB surgery. Insulin resistance was improved while GLP-1 and Ghrelin was changed significantly in patients with type 2 diabetes prior to weight loss either in the LSG or LGB group. HOMA-IR decreased to less than the cut-off value at the 3 rd month and was closely related to complete remission. The mechanism of bariatric surgery was not due just to simply dietary factors or body weight loss but also the remission of insulin resistance.
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Dewi M Kurniawati
2017-12-01
Conclusion: There are differences on insulin levels and HOMA – IR in 500 and 750 mg/kgBW dosages groups. However, there is no difference on blood glucose before and after black soybean extract treatment. The most decreased levels of blood glucose, insulin and HOMA- IR were in 750mg/kgBW dosage.
Effect of endurance training on plasma levels of AGRP and HOMA-IR in diabetic rats
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Javad Mehrabani
2017-09-01
Full Text Available The hypothalamus is a strong central controller of appetite that secretes different neuropeptides including AGRP. Plasma levels of AGRP are effective in controlling obesity and hunger. Therefore, the current study was performed with the aim of investigating the effect of endurance training on plasma levels of AGRP and HOMA-IR in diabetic rats. The Current study was experimental by posttest and control group. Eighteen male Wistar rats (200-220 grams with 8-10 weeks were randomly divided into the control group and diabetic training. Eight weeks endurance training program included in the group of animal diabetic training for 5 days per week (15-40 minutes at 50 to 65 percent of vo2max. To determine the serum concentrations of AGRP was used by ELISA. A comparison of two groups showed significantly increased plasma concentrations of AGRP (p=0.006 and insulin resistance index, decreased significantly (p=0.002 compared to the control group after eight weeks, endurance training. According to the results, increased plasma concentrations of AGRP can be attributed to the negative balance caused by training. This agent destroys the body's energy balance and hypothalamus for balancing increases the secretion of AGRP. This neuropeptide is likely will cause higher fat metabolism.
Lloyd, Jesse W; Zerfass, Kristy M; Heckstall, Ebony M; Evans, Kristin A
2015-10-01
Chemerin concentrations are elevated in obesity and associated with inflammation and insulin resistance. Exercise improves insulin sensitivity, which may be facilitated by changes in chemerin. We explored the effects of chronic exercise on chemerin levels in diet-induced obese mice. We divided 40 mice into 4 groups: high-fat diet/exercise, high-fat diet/sedentary, normal diet/exercise, and normal diet/sedentary. A 9-week dietary intervention was followed by a 12-week exercise intervention (treadmill run: 11 m/min for 30 min, 3×/week). We analyzed blood samples before and after the exercise intervention. We used t-tests and linear regression to examine changes in chemerin, insulin resistance, and inflammatory markers, and associations between changes in chemerin and all other biomarkers. Chemerin increased significantly across all mice over the 12-week intervention (mean ± SD = 40.7 ± 77.8%, p = 0.01), and this increase was smaller in the exercise versus sedentary mice (27.2 ± 83.9% versus 54.9 ± 70.5%, p = 0.29). The increase among the high-fat diet/exercise mice was ~44% lower than the increase among the high-fat diet/sedentary mice (55.7 ± 54.9% versus 99.8 ± 57.7%, p = 0.12). The high-fat diet mice showed significant increases in insulin (773.5 ± 1286.6%, p HOMA-IR; 846.5 ± 1723.3%, p HOMA-IR. Chronic exercise may attenuate diet-driven increases in circulating chemerin, and the insulin resistance associated with a high-fat diet may be mediated by diet-induced increases in chemerin.
Salgado, Ana Lúcia Farias de Azevedo; Carvalho, Luciana de; Oliveira, Ana Claudia; Santos, Virgínia Nascimento dos; Vieira, Jose Gilberto; Parise, Edison Roberto
2010-01-01
Due to its good correlation to glycemic clamp, HOMA-IR has been widely utilized as insulin resistance index in clinical and epidemiological studies involving non-alcoholic fatty liver disease carriers. However, values used for this parameter have shown large variability. To identify the HOMA-IR cut value that best distinguishes non-diabetic non-alcoholic fatty liver disease patients from a control group. One hundred sixteen non-alcoholic fatty liver disease patients were studied, diagnosed by clinical, biochemical, and liver image or biopsy criteria, and 88 healthy individuals, without any liver disease and testing for oral glucose tolerance within normality. These groups did not differ in age and gender. All were submitted to oral glucose tolerance test and blood samples were collected for glucose and insulin measurements by immunofluorometric method. HOMA-IR was calculated according to the formula: fasting insulin (microU/L) x fasting glucose (nmol/L)/22.5. NAFLD patients showed higher insulin, glycemia, and HOMA-IR values than control group, even when excluding glucose intolerant and diabetes mellitus patients by their glycemic curves. HOMA-IR 75th percentile for control group was 1.78 and the best area under the curve index was obtained for HOMA-IR values of 2.0 [AUC= 0.840 (0.781-0.899 CI 95%), sensitivity (Se): 85%, specificity (Sp): 83%] while value 2.5 showed best specificity without important loss in sensitivity [AUC=0,831 (0.773-0.888) Se = 72%, Sp = 94%]. HOMA-IR values above or equal to 2.0 or 2.5 show enhanced diagnostic value in distinguishing non-alcoholic fatty liver disease carriers from control group individuals.
Eldin Ahmed Abdelsalam, Kamal; Alobeid M Elamin, Abdelsamee
2017-01-01
It is to compare the levels of fasting glucose and insulin as well as insulin resistance in grand multiparas with primiparity and nulliparity. Fasting blood samples were collected from 100 non-pregnant ladies as control group, 100 primiparity pregnant women and 100 grand multiparity pregnant women. Glucose (FBS) and insulin (FSI) concentrations were measured by Hitachi 912 full automated Chemistry Analyzer (Roche Diagnostics, Germany) as manufacturer procedure. Insulin resistance was calculated following the formula: FBG (mg dL-1)×FSI (μU mL-1)/405. This study found a significant reduction in glucose level in primiparity when compared to control group but it was increased significantly in multiparity comparing to primiparity and control. Insulin level showed significant high concentrations in pregnant women and increased significantly in grand multiparas comparing to primiparas and controls. As a result of that, HOMA-IR was increased significantly by increasing of parity. Also, there was a significant increase in fasting insulin and a decrease in insulin sensitivity with parity with association to age and obesity. Grand multiparity is associated with an increased risk of subsequent clinical insulin resistance (HOMA-IR).
Yajnik, Chittaranjan S; Katre, Prachi A; Joshi, Suyog M; Kumaran, Kalyanaraman; Bhat, Dattatray S; Lubree, Himangi G; Memane, Nilam; Kinare, Arun S; Pandit, Anand N; Bhave, Sheila A; Bavdekar, Ashish; Fall, Caroline H D
2015-07-01
The Pune Children's Study aimed to test whether glucose and insulin measurements in childhood predict cardiovascular risk factors in young adulthood. We followed up 357 participants (75% follow-up) at 21 years of age who had undergone detailed measurements at 8 years of age (glucose, insulin, HOMA-IR and other indices). Oral glucose tolerance, anthropometry, plasma lipids, BP, carotid intima-media thickness (IMT) and arterial pulse wave velocity (PWV) were measured at 21 years. Higher fasting glucose, insulin and HOMA-IR at 8 years predicted higher glucose, insulin, HOMA-IR, BP, lipids and IMT at 21 years. A 1 SD change in 8 year variables was associated with a 0.10-0.27 SD change at 21 years independently of obesity/adiposity at 8 years of age. A greater rise in glucose-insulin variables between 8 and 21 years was associated with higher cardiovascular risk factors, including PWV. Participants whose HOMA-IR measurement remained in the highest quartile (n = 31) had a more adverse cardiovascular risk profile compared with those whose HOMA-IR measurement remained in the lowest quartile (n = 28). Prepubertal glucose-insulin metabolism is associated with adult cardiovascular risk and markers of atherosclerosis. Our results support interventions to improve glucose-insulin metabolism in childhood to reduce cardiovascular risk in later life.
Wongwananuruk, Thanyarat; Rattanachaiyanont, Manee; Leerasiri, Pichai; Indhavivadhana, Suchada; Techatraisak, Kitirat; Angsuwathana, Surasak; Tanmahasamut, Prasong; Dangrat, Chongdee
2012-01-01
Objectives. To study the cut-off point of Homeostatic Measurement Assessment-Insulin Resistance (HOMA-IR) as a screening test for detection of glucose intolerance in Thai women with polycystic ovary syndrome (PCOS). Study Design. Cross-sectional study. Setting. Department of Obstetrics and Gynecology, Faculty of Medicine Siriraj Hospital. Subject. Two hundred and fifty Thai PCOS women who attended the Gynecologic Endocrinology Unit, during May 2007 to January 2009. Materials and Methods. The paitents were interviewed and examined for weight, height, waist circumference, and blood pressure. Venous blood samples were drawn twice, one at 12-hour fasting and the other at 2 hours after glucose loading. Results. The prevalence of glucose intolerance in Thai PCOS women was 20.0%. The mean of HOMA-IR was 3.53 ± 7.7. Area under an ROC curve for HOMA-IR for detecting glucose intolerance was 0.82. Using the cut-off value of HOMA-IR >2.0, there was sensitivity at 84.0%, specificity at 61.0%, positive predictive value at 35.0%, negative predictive value at 93.8%, and accuracy at 65.6%. Conclusion. HOMA-IR >2.0 was used for screening test for glucose intolerance in Thai PCOS women. If the result was positive, a specific test should be done to prove the diagnosis.
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Thanyarat Wongwananuruk
2012-01-01
Full Text Available Objectives. To study the cut-off point of Homeostatic Measurement Assessment-Insulin Resistance (HOMA-IR as a screening test for detection of glucose intolerance in Thai women with polycystic ovary syndrome (PCOS. Study Design. Cross-sectional study. Setting. Department of Obstetrics and Gynecology, Faculty of Medicine Siriraj Hospital. Subject. Two hundred and fifty Thai PCOS women who attended the Gynecologic Endocrinology Unit, during May 2007 to January 2009. Materials and Methods. The paitents were interviewed and examined for weight, height, waist circumference, and blood pressure. Venous blood samples were drawn twice, one at 12-hour fasting and the other at 2 hours after glucose loading. Results. The prevalence of glucose intolerance in Thai PCOS women was 20.0%. The mean of HOMA-IR was 3.53 ± 7.7. Area under an ROC curve for HOMA-IR for detecting glucose intolerance was 0.82. Using the cut-off value of HOMA-IR >2.0, there was sensitivity at 84.0%, specificity at 61.0%, positive predictive value at 35.0%, negative predictive value at 93.8%, and accuracy at 65.6%. Conclusion. HOMA-IR >2.0 was used for screening test for glucose intolerance in Thai PCOS women. If the result was positive, a specific test should be done to prove the diagnosis.
Lin, Po-Ju; Borer, Katarina T
2016-01-01
Postprandial hyperinsulinemia, hyperglycemia, and insulin resistance increase the risk of type 2 diabetes (T2D) and cardiovascular disease mortality. Postprandial hyperinsulinemia and hyperglycemia also occur in metabolically healthy subjects consuming high-carbohydrate diets particularly after evening meals and when carbohydrate loads follow acute exercise. We hypothesized the involvement of dietary carbohydrate load, especially when timed after exercise, and mediation by the glucose-dependent insulinotropic peptide (GIP) in this phenomenon, as this incretin promotes insulin secretion after carbohydrate intake in insulin-sensitive, but not in insulin-resistant states. Four groups of eight metabolically healthy weight-matched postmenopausal women were provided with three isocaloric meals (a pre-trial meal and two meals during the trial day) containing either 30% or 60% carbohydrate, with and without two-hours of moderate-intensity exercise before the last two meals. Plasma glucose, insulin, glucagon, GIP, glucagon-like peptide 1 (GLP-1), free fatty acids (FFAs), and D-3-hydroxybutyrate concentrations were measured during 4-h postprandial periods and 3-h exercise periods, and their areas under the curve (AUCs) were analyzed by mixed-model ANOVA, and insulin resistance during fasting and meal tolerance tests within each diet was estimated using homeostasis-model assessment (HOMA-IR). The third low-carbohydrate meal, but not the high-carbohydrate meal, reduced: (1) evening insulin AUC by 39% without exercise and by 31% after exercise; (2) GIP AUC by 48% without exercise and by 45% after exercise, and (3) evening insulin resistance by 37% without exercise and by 24% after exercise. Pre-meal exercise did not alter insulin-, GIP- and HOMA-IR- lowering effects of low-carbohydrate diet, but exacerbated evening hyperglycemia. Evening postprandial insulin and GIP responses and insulin resistance declined by over 30% after three meals that limited daily carbohydrate intake to
Lakhdar, Nadia; Denguezli, Myriam; Zaouali, Monia; Zbidi, Abdelkrim; Tabka, Zouhair; Bouassida, Anissa
2014-01-01
We investigate the effect of 6 months aerobic training alone or in combination with diet on adiponectin in circulation and in adipose abdominal tissue (AT) in obese women. Twenty obese subjects were randomized into a 24 weeks intervention: 1) training (TR) and 2) training and diet (TRD). Blood samples were collected at baseline, after 12 wk and 24 wk. AT biopsies were obtained only at baseline and after 24 wk. In the TRD group the fat loss was after 12 wk -13.74% (pHOMA-IR and HOMA-AD for assessing insulin resistance were strongly affected by protocols. HOMA-IR decreased (pHOMA-AD increased in both groups after 12 (pHOMA-IR.
Ghiasi, Rafigheh; Ghadiri Soufi, Farhad; Somi, Mohammad Hossein; Mohaddes, Gisou; Mirzaie Bavil, Fariba; Naderi, Roya; Alipour, Mohammad Reza
2015-09-01
Insulin resistance plays a key role in the onset and development of type 2 diabetes mellitus (T2DM) and its complications. In this study, we evaluated the effect of swim training on insulin resistance in diabetic rats. Forty male Wistar rats were randomly divided into four groups (n=10): sedentary control (Con), sedentary diabetic (Dia), swim trained control (Exe) and swim trained diabetic (Dia+Exe) rats. Diabetes was induced by high fat diet (HFD) and a low dose of streptozotocin (35 mg/kg, i.p). In trained groups, one week after the induction of diabetes, animals were subjected to swimming (60 min/5 days a week) for 10 weeks. At the end of training, fasting blood sugar (FBS), oral glucose tolerance test (OGTT), fasting/basal insulin, glycosylated hemoglobin (HbA1c) levels, insulin resistance index, homeostasis model assessment method (HOMA-IR), triglycerides (TG,) total cholesterol (TCh), and high density lipoprotein (HDL) levels in blood were measured. Swimming significantly improved OGTT (PHOMA-IR (P<0.01). Swim training also significantly decreased FBS (p<0.01), fasting/basal insulin (P<0.01), HbA1C (p<0.01), TG (P<0.05), and TCh (P<0.05) levels. It also significantly increased HDL (p<0.05) level. Our findings indicate that swim training improved glycemic control and insulin sensitivity in type 2 diabetes caused by high fat diet in male rats.
Gentile, Adriana-Mariel; Lhamyani, Said; Coín-Aragüez, Leticia; Oliva-Olivera, Wilfredo; Zayed, Hatem; Vega-Rioja, Antonio; Monteseirin, Javier; Romero-Zerbo, Silvana-Yanina; Tinahones, Francisco-José; Bermúdez-Silva, Francisco-Javier; El Bekay, Rajaa
2016-01-01
Real-time or quantitative PCR (qPCR) is a useful technique that requires reliable reference genes for data normalization in gene expression analysis. Adipogenesis is among the biological processes suitable for this technique. The selection of adequate reference genes is essential for qPCR gene expression analysis of human Vascular Stromal Cells (hVSCs) during their differentiation into adipocytes. To the best of our knowledge, there are no studies validating reference genes for the analyses of visceral and subcutaneous adipose tissue hVSCs from subjects with different Body Mass Index (BMI) and Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) index. The present study was undertaken to analyze this question. We first analyzed the stability of expression of five potential reference genes: CYC, GAPDH, RPL13A, EEF1A1, and 18S ribosomal RNA, during in vitro adipogenic differentiation, in samples from these types of patients. The expression of RPL13A and EEF1A1 was not affected by differentiation, thus being these genes the most stable candidates, while CYC, GAPDH, and 18S were not suitable for this sort of analysis. This work highlights that RPL13A and EEF1A1 are good candidates as reference genes for qPCR analysis of hVSCs differentiation into adipocytes from subjects with different BMI and HOMA-IR.
Ahuja, Vasudha; Kadowaki, Takashi; Evans, Rhobert W; Kadota, Aya; Okamura, Tomonori; El Khoudary, Samar R; Fujiyoshi, Akira; Barinas-Mitchell, Emma J M; Hisamatsu, Takashi; Vishnu, Abhishek; Miura, Katsuyuki; Maegawa, Hiroshi; El-Saed, Aiman; Kashiwagi, Atsunori; Kuller, Lewis H; Ueshima, Hirotsugu; Sekikawa, Akira
2015-02-01
At the same level of BMI, white people have less visceral adipose tissue (VAT) and are less susceptible to developing type 2 diabetes than Japanese people. No previous population-based studies have compared insulin resistance and insulin secretion between these two races in a standardised manner that accounts for VAT. We compared HOMA-IR, HOMA of beta cell function (HOMA-β%) and disposition index (DI) in US white men and Japanese men in Japan. We conducted a population-based, cross-sectional study, comprising 298 white men and 294 Japanese men aged 40-49 years without diabetes. Insulin, glucose, VAT and other measurements were performed at the University of Pittsburgh. We used ANCOVA to compare geometric means of HOMA-IR, HOMA-β% and DI, adjusting for VAT and other covariates. White men had higher HOMA-IR, HOMA-β% and DI than Japanese men, and the difference remained significant (p HOMA-IR, HOMA-β% and DI were significantly higher in white men even after further adjustment for BMI, impaired fasting glucose and other risk factors. The higher VAT-adjusted DI in white men than Japanese men may partly explain lower susceptibility of white people than Japanese people to developing type 2 diabetes. The results, however, should be interpreted with caution because the assessment of insulin indices was made using fasting samples and adjustment was not made for baseline glucose tolerance. Further studies using formal methods to evaluate insulin indices are warranted.
Lloyd, Jesse W.; Zerfass, Kristy M.; Heckstall, Ebony M.; Evans, Kristin A.
2015-01-01
Objectives: Chemerin concentrations are elevated in obesity and associated with inflammation and insulin resistance. Exercise improves insulin sensitivity, which may be facilitated by changes in chemerin. We explored the effects of chronic exercise on chemerin levels in diet-induced obese mice. Methods: We divided 40 mice into 4 groups: high-fat diet/exercise, high-fat diet/sedentary, normal diet/exercise, and normal diet/sedentary. A 9-week dietary intervention was followed by a 12-week exercise intervention (treadmill run: 11 m/min for 30 min, 3×/week). We analyzed blood samples before and after the exercise intervention. We used t-tests and linear regression to examine changes in chemerin, insulin resistance, and inflammatory markers, and associations between changes in chemerin and all other biomarkers. Results: Chemerin increased significantly across all mice over the 12-week intervention (mean ± SD = 40.7 ± 77.8%, p = 0.01), and this increase was smaller in the exercise versus sedentary mice (27.2 ± 83.9% versus 54.9 ± 70.5%, p = 0.29). The increase among the high-fat diet/exercise mice was ~44% lower than the increase among the high-fat diet/sedentary mice (55.7 ± 54.9% versus 99.8 ± 57.7%, p = 0.12). The high-fat diet mice showed significant increases in insulin (773.5 ± 1286.6%, p diet-induced increases in insulin and HOMA-IR. Conclusion: Chronic exercise may attenuate diet-driven increases in circulating chemerin, and the insulin resistance associated with a high-fat diet may be mediated by diet-induced increases in chemerin. PMID:26445641
Thapa, Lekhjung; Rana, P V S
2016-01-01
Objective. Nondiabetic obese individuals have subclinical involvement of peripheral nerves. We report the factors predicting peripheral nerve function in overweight and obese nondiabetic Nepalese individuals. Methodology. In this cross-sectional study, we included 50 adult overweight and obese nondiabetic volunteers without features of peripheral neuropathy and 50 healthy volunteers to determine the normative nerve conduction data. In cases of abnormal function, the study population was classified on the basis of the number of nerves involved, namely, "HOMA-IR) was the significant predictor (P = 0.019, 96% CI = 1.420-49.322) of sensory nerve dysfunction. Body mass index (BMI) was the significant predictor (P = 0.034, 95% CI = 1.018-1.577) in case of ≥2 mixed nerves' involvement. Conclusion. FBG, HOMA-IR, and BMI were significant predictors of peripheral nerve dysfunction in overweight and obese Nepalese individuals.
Pastucha, D; Filipčíková, R; Horáková, D; Radová, L; Marinov, Z; Malinčíková, J; Kocvrlich, M; Horák, S; Bezdičková, M; Dobiáš, M
2013-01-01
Common alimentary obesity frequently occurs on a polygenic basis as a typical lifestyle disorder in the developed countries. It is associated with characteristic complex metabolic changes, which are the cornerstones for future metabolic syndrome development. The aims of our study were 1) to determine the incidence of metabolic syndrome (based on the diagnostic criteria defined by the International Diabetes Federation for children and adolescents) in Czech obese children, 2) to evaluate the incidence of insulin resistance according to HOMA-IR and QUICKI homeostatic indexes in obese children with and without metabolic syndrome, and 3) to consider the diagnostic value of these indexes for the early detection of metabolic syndrome in obese children. We therefore performed anthropometric and laboratory examinations to determine the incidence of metabolic syndrome and insulin resistance in the group of 274 children with obesity (128 boys and 146 girls) aged 9-17 years. Metabolic syndrome was found in 102 subjects (37 %). On the other hand, the presence of insulin resistance according to QUICKI HOMA-IR >3.16 in 53 % of obese subjects. This HOMA-IR limit was exceeded by 70 % children in the MS(+) group, but only by 43 % children in the MS(-) group (p<0.0001). However, a relatively high incidence of insulin resistance in obese children without metabolic syndrome raises a question whether the existing diagnostic criteria do not falsely exclude some cases of metabolic syndrome. On the basis of our results we suggest to pay a preventive attention also to obese children with insulin resistance even if they do not fulfill the actual diagnostic criteria for metabolic syndrome.
Sharma, Sushma; Fleming, Sharon E
2012-01-01
This study aimed to compare the discriminating power of HbA(1C) with other pre-diabetes diagnostic tests specifically in high-risk African American children. A cross-sectional analysis was performed on a sample of 172 children (70 boys and 102 girls) aged 9-11 years with BMI's above the 85th percentile. Fasting glucose, insulin and HbA(1C) were analyzed from the plasma samples. Of the 172 participants included in this analysis, 21 (12.2%) had HbA(1C) concentrations above the cutoff of 5.7 used to identify pre-diabetes. None (0%) of these 21 participants, however, were observed to have a glucose concentration above the pre-diabetes cutoff of 110 mg/dl, and only 13 of 21 participants had HOMA-IR above the pre-diabetes cutoff of 2.5. When compared to the previously identified glucose cutoff of 110 mg/dl and HOMA-IR cutoff of 2.5 for pre-diabetes, HbA(1C) showed high specificity (88 and 93%, respectively) but very low sensitivity (0 and 21%, respectively). Glucose, insulin and HOMA-IR were significantly interrelated, but HbA(1C) was not significantly correlated with these biochemical prediabetes assessment variables, nor with anthropometric (BMIz, WC) risk factors. Our results suggest that HbA(1C) had poor discrimination power to identify prediabetes in overweight and obese 9- to 11-year-old African American children. Future studies are recommended to compare the feasibility, sensitivity and predictive power of different screening tests currently recommended to avoid inadequacy when screening for prediabetes and diabetes. Copyright © 2012 Diabetes India. Published by Elsevier Ltd. All rights reserved.
Awede, Bonaventure; Adovoekpe, Diane; Adehan, Grace; MacFarlane, Niall G; Azonbakin, Simon; Dossou, Emmanuel; Amoussou-Guenou, Marcellin; Djrolo, François
2018-06-01
Factors associated with plasma levels of adiponectin and leptin were studied in adult subjects without diabetes from Cotonou in Benin (West-Africa). Seventy (70) men and 45 women were included in the study. Anthropometric variables were measured and a venous blood sample was drawn from each subject, after an overnight fasting period, for measurement of plasma glucose, insulin, leptin, and adiponectin levels. HOMA-IR was determined to assess insulin resistance. Adiponectin and leptin levels were higher in women than in men (with adiponectin 18.48 ± 12.77 vs.7.8 ± 10.39 μg/mL, P HOMA-IR were also higher in the females. Hyperleptinemia was observed in 66,96% of subjects and hypoadiponectinemia was present in 44.35% of subjects. In both men and women, leptin correlated with age (r = 0.2; P = 0.02), BMI (r = 0.572; P HOMA-IR (r = 0.430; P < 0.0001). No significant correlation was observed for adiponectin levels with these variables. Only in women, adiponectin was inversely correlated with fasting glucose (r = -0.423; P < 0.004). These data confirm previous descriptions of leptin but suggest that variations in factors determining serum adiponectin levels observed between ethnicities could also been seen between populations from the same ethnicity. © 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society.
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Nurhidajah Nurhidajah
2017-02-01
Full Text Available The process of germination of grains such as rice, could increase some nutritional values of amino acids and dietary fiber. Red rice and its sprouts are believed to be able to decrease blood glucose in patients with diabetes mellitus (DM. The aim of this study was to evaluate the hypoglycemic effect of red rice sprouts in STZ-NA induced diabetic rats on blood glucose level, insulin level, and HOMA-IR and HOMA-β index. This experimental study was conducted based on randomized post test only control group design using 24 male Wistar rats aged 2.5 months. Rats were divided into 4 groups, one group without induction of STZ-NA fed with a standard diet (control and three groups of STZ- NA induced with a standard diet, red rice and red rice germ. Experiments were conducted for 6 weeks. The results showed that sprouted red rice lowered blood glucose levels by 61.88 % and the value of HOMA-IR (insulin resistance parameters by 56.82%. Insulin level increased by 16.35 % and HOMA-β by 763.6 %. This study showed that red rice germ was able to decrease blood glucose levels and increase insulin resistance of DM rats and the strength of the pancreatic beta cells. ABSTRAK Proses perkecambahan biji-bijian seperti beras, dapat meningkatkan beberapa nilai gizi seperti asam amino dan serat pangan. Beras merah dan kecambahnya diyakini mampu menurunkan glukosa darah pada penderita diabetes melitus (DM. Tujuan penelitian ini adalah mengevaluasi efek hipoglikemik kecambah beras merah pada tikus diabetes yang diinduksi STZ-NA terhadap kadar glukosa darah, insulin, serta indeks HOMA-IR dan HOMA β. Penelitian ini bersifat eksperimental in vivo pada hewan coba tikus Wistar jantan usia 2,5 bulan sebanyak 24 ekor dengan desain penelitian randomized post test only control group. Tikus dibagi menjadi 4 kelompok, masing-masing 1 kelompok tanpa induksi STZ-NA dengan diet standar dan 3 kelompok diinduksi STZ-NA dengan diet standar, beras merah dan kecambah beras merah
Ghorbanzadeh, V; Mohammadi, M; Dariushnejad, H; Chodari, L; Mohaddes, G
2016-10-01
Hyperglycemia is the main risk factor for microvascular complications in type 2 diabetes. Crocin and voluntary exercise have anti-hyperglycemic effects in diabetes. In this research, we evaluated the effects of crocin and voluntary exercise alone or combined on glycemia control and heart level of VEGF-A. Animals were divided into eight groups as: control (con), diabetes (Dia), crocin (Cro), voluntary exercise (Exe), crocin and voluntary exercise (Cro-Exe), diabetic-crocin (Dia-Cro), diabetic-voluntary exercise (Dia-Exe), diabetic-crocin-voluntary exercise (Dia-Cro-Exe). Type 2 diabetes was induced by a high-fat diet (4 weeks) and injection of streptozotocin (STZ) (i.p, 35 mg/kg). Animals received oral administration of crocin (50 mg/kg) or performed voluntary exercise alone or together for 8 weeks. Oral glucose tolerance test (OGTT) was performed on overnight fasted control, diabetic and treated rats after 8 weeks of treatment. Then, serum insulin and heart VEGF-A protein levels were measured. Crocin combined with voluntary exercise significantly decreased blood glucose levels (p HOMA-IR) (p HOMA-IR) and reduced glucose levels in diabetic rats. Since both crocin and voluntary exercise can increase VEGF-A protein expression in heart tissue, they probably are able to increase angiogenesis in diabetic animals.
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Andri Hidayat
2011-04-01
Full Text Available BACKGROUND: Many studies have shown that obesity was closely related to insulin resistance via several pathways such as inflammation, oxidative stress, lipolysis, and endothelial dysfunction. This study was carried out to observe the correlation between inflammation (IL-6 and hsCRP, lipolysis process (ET-1, and endothelial dysfunction (ADMA and insulin resistance (HOMA-IR in centrally obese men. METHODS: This was a cross sectional study on 62 male subjects aged 30–60 years old with waist circumference (WC >90 cm. IL-6, ET-1 and ADMA levels were measured using ELISA method, while hsCRP and insulin were measured using chemiluminescence method. All blood testings were conducted in Prodia Clinical Laboratory. RESULTS: The results showed that WC was significantly correlated with hsCRP (r=0.294, p=0.022, ET-1 (r=0.257, p=0.047 and ADMA (r=0.338, p=0.009. We also found a significant correlation between hsCRP with HOMA-IR (r=0.324, p=0.021, ADMA with HOMA-IR (r=0.280, p=0.045 and IL-6 with hsCRP (r=0.437, p=0.003. CONCLUSIONS: hsCRP and ADMA have significant correlation with HOMA-IR in centrally obese men. HOMA-IR significantly increases in subjects with ADMA above median and either IL-6 or hsCRP above median, as compared to those in the other groups. Inflammation and endothelial dysfunction are important causal pathways of insulin resistance state in centrally obese men. KEYWORDS: obesity, IL-6, hsCRP, ET-1, ADMA, HOMA-IR.
GÜMÜŞ, Yeliz
2012-01-01
Gestasyonel diabetes mellitus gebelikle birlikte ortaya çıkan veya ilk olarak gebelikte tanısı konan karbonhidrat intoleransı olarak tanımlanmaktadır. Toplumlardaki ağırlık artışı ve obezite oranlarının giderek yükselmesine paralel olarak GDM prevalansı da artmaktadır. Makrozomi, omuz distosisi, doğum esnasında fiziksel yaralanma, hipoglisemi, hiperbilirubinemi, solunum problemleri ve çocukluk çağında obezite gibi fetal ve neonatal risklerin yanı sıra, preeklampsi, sezaryen ile doğum...
Hilbe, Joseph M
2009-01-01
This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...
Zehsaz, Farzad; Farhangi, Negin; Mirheidari, Lamia
2014-01-01
The purpose of the present study was to investigate the effects of a 12-week training program on serum CXC ligand 5, tumor necrosis factor α (TNF-α) and insulin resistance index in obese sedentary women. To this end, twenty-four obese sedentary women were evaluated before and after a 12-week exercise program including a brief warm-up, followed by ~45 min per session of aerobic exercise at an intensity of 60-75% of age-predicted maximum heart rate (~300 kcal/day), followed by a brief cool down, five times per week. After the exercise program, body weight, waist circumference, waist to hip ratio, percentage body fat mass, fasting glucose and insulin of participants were decreased. Furthermore, serum CXCL5 levels were significantly decreased from 2693.2 ±375.8 to 2290.2 ±345.9 pg/ml (p HOMA-IR (p < 0.001) and TNF-α (p < 0.001). Exercise training induced weight loss resulted in a significant reduction in serum CXCL5 concentrations and caused an improvement in insulin resistance in obese sedentary women.
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Ana Lúcia Farias de Azevedo Salgado
2010-06-01
Full Text Available CONTEXT: Due to its good correlation to glycemic clamp, HOMA-IR has been widely utilized as insulin resistance index in clinical and epidemiological studies involving non-alcoholic fatty liver disease carriers. However, values used for this parameter have shown large variability. OBJECTIVE: To identify the HOMA-IR cut value that best distinguishes non-diabetic non-alcoholic fatty liver disease patients from a control group. METHODS: One hundred sixteen non-alcoholic fatty liver disease patients were studied, diagnosed by clinical, biochemical, and liver image or biopsy criteria, and 88 healthy individuals, without any liver disease and testing for oral glucose tolerance within normality. These groups did not differ in age and gender. All were submitted to oral glucose tolerance test and blood samples were collected for glucose and insulin measurements by immunofluorometric method. HOMA-IR was calculated according to the formula: fasting insulin (µU/L x fasting glucose (nmol/L/22.5. RESULTS: NAFLD patients showed higher insulin, glycemia, and HOMA-IR values than control group, even when excluding glucose intolerant and diabetes mellitus patients by their glycemic curves. HOMA-IR 75th percentile for control group was 1.78 and the best area under the curve index was obtained for HOMA-IR values of 2.0 [AUC= 0.840 (0.781-0.899 CI 95%, sensitivity (Se: 85%, specificity (Sp: 83%] while value 2.5 showed best specificity without important loss in sensitivity [AUC=0,831 (0.773-0.888 Se = 72%, Sp = 94%]. CONCLUSION: HOMA-IR values above or equal to 2.0 or 2.5 show enhanced diagnostic value in distinguishing non-alcoholic fatty liver disease carriers from control group individuals.CONTEXTO: Pela sua boa correlação com o "clamp" glicêmico, o HOMA-IR tem sido largamente utilizado como índice de resistência insulínica em estudos clínicos e epidemiológicos em pacientes com doença hepática gordurosa não-alcoólica. Porém os valores utilizados para
Qu, Chunmei; Zhou, Xiaoxin; Yang, Gangyi; Li, Ling; Liu, Hua; Liang, Zerong
2016-03-01
The euglycemic-hyperinsulinemic clamp (EHC) is not available in most clinical settings and is costly, time consuming and invasive, and requires trained staff. Therefore, an accessible and inexpensive test to identify insulin resistance (IR) is needed. The aim of this study is to assess whether zinc-α2-glycoprotein (ZAG) index [Ln ZAG/homeostasis model assessment of IR (HOMA-IR)] is a better surrogate index for estimating IR or metabolic syndrome (MetS) compared with other surrogate indices. We performed a population-based cross-sectional study. Two hundred healthy subjects, 102 polycystic ovary syndrome (PCOS) patients, 97 newly diagnosed type 2 diabetes mellitus (nT2DM) and 84 impaired glucose tolerance (IGT) subjects were enrolled. The EHC was performed to identify IR. Circulating ZAG and adiponectin levels were determined by ELISA. The ZAG index was significantly lower in participants with IR including IGT, nT2DM and PCOS than in those without IR. In addition, subjects with MetS had lower ZAG indices and higher the product of fasting triglycerides and glucose (TyG) indices than those without MetS. The ZAG index showed a significantly stronger association with M values than the other surrogate indices, whereas the TyG index showed a stronger association with MetS. The optimal cutoff value of the ZAG index for detection of IR was 2.97 with a sensitivity of 88% and a specificity of 91%, whereas the optimal cutoff value of TyG index for detection of MetS was 4.90 with a sensitivity of 82% and a specificity of 86%. The ZAG index is a better marker than the other surrogate indices for identifying IR, whereas the TyG index has high sensitivity and specificity for identifying MetS. Copyright © 2016 Elsevier Ltd. All rights reserved.
(Non) linear regression modelling
Cizek, P.; Gentle, J.E.; Hardle, W.K.; Mori, Y.
2012-01-01
We will study causal relationships of a known form between random variables. Given a model, we distinguish one or more dependent (endogenous) variables Y = (Y1,…,Yl), l ∈ N, which are explained by a model, and independent (exogenous, explanatory) variables X = (X1,…,Xp),p ∈ N, which explain or
Panel Smooth Transition Regression Models
DEFF Research Database (Denmark)
González, Andrés; Terasvirta, Timo; Dijk, Dick van
We introduce the panel smooth transition regression model. This new model is intended for characterizing heterogeneous panels, allowing the regression coefficients to vary both across individuals and over time. Specifically, heterogeneity is allowed for by assuming that these coefficients are bou...
Forecasting with Dynamic Regression Models
Pankratz, Alan
2012-01-01
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
Modified Regression Correlation Coefficient for Poisson Regression Model
Kaengthong, Nattacha; Domthong, Uthumporn
2017-09-01
This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).
Study of beta-cell function (by HOMA model) in metabolic syndrome.
Garg, M K; Dutta, M K; Mahalle, Namita
2011-07-01
The clustering of cardiovascular risk factors is termed the metabolic syndrome (MS), which strongly predict risk of diabetes and cardiovascular disease. Many studies implicate insulin resistance (IR) in the development of diabetes, but ignore the contribution of beta-cell dysfunction. Hence, we studied beta-cell function, as assessed by HOMA model, in subjects with MS. We studied 50 subjects with MS diagnosed by IDF criteria and 24 healthy age- and sex-matched controls. Clinical evaluation included anthropometry, body fat analysis by bioimpedance, biochemical, and insulin measurement. IR and secretion were calculated by HOMA model. Subjects with MS had more IR (HOMA-IR) than controls (3.35 ± 3.14 vs. 1.76 ± 0.53, P = 0.029) and secreted less insulin (HOMA-S) than controls (66.80 ± 69.66 vs. 144.27 ± 101.61, P = 0.0003), although plasma insulin levels were comparable in both groups (10.7 ± 10.2 vs. 8.2 ± 2.38, P = 0.44). HOMA-IR and HOMA-S were related with number of metabolic abnormalities. HOMA-IR was positively associated with body mass index, waist hip ratio, body fat mass, and percent body fat. HOMA-S was negatively associated with waist hip ratio, fasting plasma glucose and total cholesterol and positively with basal metabolic rate. Percent body fat was an independent predictor of HOMA-IR and waist hip ratio of HOMA-S in multiple regression analysis. Subjects with MS have increased IR and decreased insulin secretion compared with healthy controls. Lifestyle measures have been shown to improve IR, insulin secretion, and various components and effects of MS. Hence, there is an urgent need for public health measures to prevent ongoing epidemic of diabetes and cardiovascular disease.
Irace, C; Carallo, C; Scavelli, F B; De Franceschi, M S; Esposito, T; Tripolino, C; Gnasso, A
2013-07-01
The present investigation was designed to test the association between carotid atherosclerosis and two simple markers of insulin resistance, i.e. HOMA-Index and TyG-Index. The study was performed in two different cohorts. In the first cohort, 330 individuals were enrolled. Blood pressure, lipids, glucose, waist and cigarette smoking were evaluated. HOMA-IR and TyG-Index were calculated as markers of prevalent hepatic and muscular insulin resistance respectively. Carotid atherosclerosis was assessed by Doppler ultrasonography. The association between cardiovascular risk factors, markers of insulin resistance and carotid atherosclerosis was assessed by multiple logistic regression analyses. In the second cohort, limited to the evaluation of TyG-Index, 1432 subjects were studied. In the first cohort, TyG-Index was significantly associated with carotid atherosclerosis in a model including age, sex, diabetes, cigarette smoking and LDL cholesterol, while HOMA-IR was not. When components of metabolic syndrome were added to the model as dichotomous variables (absent/present), TyG-Index retained its predictive power. The same result was obtained when the metabolic syndrome was added to the model (absence/presence). The association between TyG-Index and carotid atherosclerosis was confirmed in the second cohort. The present findings suggest that TyG-Index is better associated with carotid atherosclerosis than HOMA-IR. © 2013 John Wiley & Sons Ltd.
Nonparametric Mixture of Regression Models.
Huang, Mian; Li, Runze; Wang, Shaoli
2013-07-01
Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.
Regression Models for Repairable Systems
Czech Academy of Sciences Publication Activity Database
Novák, Petr
2015-01-01
Roč. 17, č. 4 (2015), s. 963-972 ISSN 1387-5841 Institutional support: RVO:67985556 Keywords : Reliability analysis * Repair models * Regression Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.782, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/novak-0450902.pdf
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Imelda Novianti
2012-04-01
Full Text Available BACKGROUND: Central obesity is the accumulation of visceral (intra-abdominal fat and is strongly known to be associated with insulin resistance and type 2 diabetes mellitus (T2DM. Obesity can cause adipocyte hypertrophy that results in dysregulation of adipokine expression. The abnormal function of adipocytes may play an important role in the development of a chronic low-grade proinflammatory state associated with obesity. Adiponectin, retinol binding protein (RBP-4 and fetuin-A play a role in the pathophysiology of insulin resistance. Expression of fetuin-A is increased due to fat accumulation in the liver. Elevated concentration of fetuin-A in the circulation can impair insulin signaling in muscle and liver as well as suppress adiponectin secretion, although its molecular mechanism is still unclear. The aim of this study was to identify the relationship of fetuin-A, adiponectin, RBP-4 and hsCRP with insulin resistance in obese non diabetic men. METHODS: This was an observational study with a cross-sectional design. The study subjects were 64 men with non diabetic abdominal obesity, characterized by waist circumference of 98.47±5.88 cm and fasting blood glucose of 85.75±8.36 mg/dL. RESULTS: This study showed that fetuin-A was positively correlated with HOMA-IR in obese non diabetic men with insulin resistance (r=0.128; p=0.570, although not significant. Fetuin-A was found to be correlated with adiponectin, RBP-4 and hsCRP (r=0.150; p=0.233; r=0.050; p=0.711; r=-0.04; p=0.445, although not significant. CONCLUSIONS: The concentration of fetuin-A showed a tendency to be positively correlated with HOMA-IR and with RBP-4 in obese non diabetic men, although statistically not significant. The concentration of fetuin-A showed a tendency to be negatively correlated with adiponectin and hsCRP although statistically not significant. There was no interrelationship between fetuin-A, adiponectin, RBP-4, hsCRP and HOMA-IR. Elevated concentrations of fetuin
Interpretation of commonly used statistical regression models.
Kasza, Jessica; Wolfe, Rory
2014-01-01
A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.
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Chunling Zhou
Full Text Available Despite growing interest in the protective role that dietary antioxidant vitamins may have in the development of type 2 diabetes (T2D, little epidemiological evidence is available in non-Western populations especially about the possible mediators underlying in this role. The present study aimed to investigate the association of vitamin C and vitamin E intakes with T2D risk in Chinese adults and examine the potential mediators. 178 incident T2D cases among 3483 participants in the Harbin People Health Study (HPHS, and 522 newly diagnosed T2D among 7595 participants in the Harbin Cohort Study on Diet, Nutrition and Chronic Non-communicable Diseases (HDNNCDS were studied. In the multivariable-adjusted logistics regression model, the relative risks (RRs were 1.00, 0.75, and 0.76 (Ptrend = 0.003 across tertiles of vitamin C intake in the HDNNCDS, and this association was validated in the HPHS with RRs of 1.00, 0.47, and 0.46 (Ptrend = 0.002. The RRs were 1.00, 0.72, and 0.76 (Ptrend = 0.039 when T2D diagnosed by haemoglobin A1c in the HDNNCDS. The mediation analysis discovered that insulin resistance (indicated by homeostasis model assessment and oxidative stress (indicated by plasma total antioxidative capacity partly mediated this association. But no association was evident between vitamin E intake and T2D. In conclusion, our research adds further support to the role of vitamin C intake in reducing the development of T2D in the broader population studied. The results also suggested that this association was partly mediated by inhibiting or ameliorating oxidative stress and insulin resistance.
A Seemingly Unrelated Poisson Regression Model
King, Gary
1989-01-01
This article introduces a new estimator for the analysis of two contemporaneously correlated endogenous event count variables. This seemingly unrelated Poisson regression model (SUPREME) estimator combines the efficiencies created by single equation Poisson regression model estimators and insights from "seemingly unrelated" linear regression models.
Gaussian Process Regression Model in Spatial Logistic Regression
Sofro, A.; Oktaviarina, A.
2018-01-01
Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.
[Homeostasis model assessment (HOMA) values in Chilean elderly subjects].
Garmendia, María Luisa; Lera, Lydia; Sánchez, Hugo; Uauy, Ricardo; Albala, Cecilia
2009-11-01
The homeostasis assessment model for insulin resistance (HOMA-IR) estimates insulin resistance using basal insulin and glucose values and has a good concordance with values obtained with the euglycemic clamp. However it has a high variability that depends on environmental, genetic and physiologic factors. Therefore it is imperative to establish normal HOMA values in different populations. To report HOMA-IR values in Chilean elderly subjects and to determine the best cutoff point to diagnose insulin resistance. Cross sectional study of 1003 subjects older than 60 years of whom 803 (71% women) did not have diabetes. In 154 subjects, an oral glucose tolerance test was also performed. Insulin resistance (IR) was defined as the HOMA value corresponding to percentile 75 of subjects without over or underweight. The behavior of HOMA-IR in metabolic syndrome was studied and receiver operating curves (ROC) were calculated, using glucose intolerance defined as a blood glucose over 140 mg/dl and hyperinsulinemia, defined as a serum insulin over 60 microU/ml, two hours after the glucose load. Median HOMA-IR values were 1.7. Percentile 75 in subjects without obesity or underweight was 2.57. The area under the ROC curve, when comparing HOMA-IR with glucose intolerance and hyperinsulinemia, was 0.8 (95% confidence values 0.72-0.87), with HOMA-IR values ranging from 2.04 to 2.33. HOMA-IR is a useful method to determine insulin resistance in epidemiological studies. The HOMA-IR cutoff point for insulin resistance defined in thi spopulation was 2.6.
Regression models of reactor diagnostic signals
International Nuclear Information System (INIS)
Vavrin, J.
1989-01-01
The application is described of an autoregression model as the simplest regression model of diagnostic signals in experimental analysis of diagnostic systems, in in-service monitoring of normal and anomalous conditions and their diagnostics. The method of diagnostics is described using a regression type diagnostic data base and regression spectral diagnostics. The diagnostics is described of neutron noise signals from anomalous modes in the experimental fuel assembly of a reactor. (author)
Variable importance in latent variable regression models
Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.
2014-01-01
The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable
Regression modeling of ground-water flow
Cooley, R.L.; Naff, R.L.
1985-01-01
Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)
[From clinical judgment to linear regression model.
Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O
2013-01-01
When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.
Regression Models for Market-Shares
DEFF Research Database (Denmark)
Birch, Kristina; Olsen, Jørgen Kai; Tjur, Tue
2005-01-01
On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put on the interpretat......On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put...... on the interpretation of the parameters in relation to models for the total sales based on discrete choice models.Key words and phrases. MCI model, discrete choice model, market-shares, price elasitcity, regression model....
Cassani, Roberta Soares Lara; Forti, Adriana Costa e; Pareja, José Carlos; Tambascia, Marcos Antonio; Geloneze, Bruno
2016-01-01
Background The major adverse consequences of obesity are associated with the development of insulin resistance (IR) and adiposopathy. The Homeostasis Model Assessment-Adiponectin (HOMA-AD) was proposed as a modified version of the HOMA1-IR, which incorporates adiponectin in the denominator of the index. Objectives To evaluate the performance of the HOMA-AD index compared with the HOMA1-IR index as a surrogate marker of IR in women, and to establish the cutoff value of the HOMA-AD. Subjects/Methods The Brazilian Metabolic Syndrome Study (BRAMS) is a cross-sectional multicenter survey. The data from 1,061 subjects met the desired criteria: 18–65 years old, BMI: 18.5–49.9 Kg/m² and without diabetes. The IR was assessed by the indexes HOMA1-IR and HOMA-AD (total sample) and by the hyperglycemic clamp (n = 49). Metabolic syndrome was defined using the IDF criteria. Results For the IR assessed by the clamp, the HOMA-AD demonstrated a stronger coefficient of correlation (r = -0.64) compared with the HOMA1-IR (r = -0.56); p 0.05). The optimal cutoff identified for the HOMA-AD for the diagnosis of IR was 0.95. Conclusions The HOMA-AD index was demonstrated to be a useful surrogate marker for detecting IR among adult women and presented a similar performance compared with the HOMA1-IR index. These results may assist physicians and researchers in determining which method to use to evaluate IR in light of the available facilities. PMID:27490249
Vilela, Brunna Sullara; Vasques, Ana Carolina Junqueira; Cassani, Roberta Soares Lara; Forti, Adriana Costa E; Pareja, José Carlos; Tambascia, Marcos Antonio; Geloneze, Bruno
2016-01-01
The major adverse consequences of obesity are associated with the development of insulin resistance (IR) and adiposopathy. The Homeostasis Model Assessment-Adiponectin (HOMA-AD) was proposed as a modified version of the HOMA1-IR, which incorporates adiponectin in the denominator of the index. To evaluate the performance of the HOMA-AD index compared with the HOMA1-IR index as a surrogate marker of IR in women, and to establish the cutoff value of the HOMA-AD. The Brazilian Metabolic Syndrome Study (BRAMS) is a cross-sectional multicenter survey. The data from 1,061 subjects met the desired criteria: 18-65 years old, BMI: 18.5-49.9 Kg/m² and without diabetes. The IR was assessed by the indexes HOMA1-IR and HOMA-AD (total sample) and by the hyperglycemic clamp (n = 49). Metabolic syndrome was defined using the IDF criteria. For the IR assessed by the clamp, the HOMA-AD demonstrated a stronger coefficient of correlation (r = -0.64) compared with the HOMA1-IR (r = -0.56); p HOMA1-IR, the HOMA-AD showed higher values of the AUC for the identification of IR based on the clamp test (AUC: 0.844 vs. AUC: 0.804) and on the metabolic syndrome (AUC: 0.703 vs. AUC: 0.689), respectively; p HOMA-AD in comparison with the HOMA1-IR in the diagnosis of IR and metabolic syndrome (p > 0.05). The optimal cutoff identified for the HOMA-AD for the diagnosis of IR was 0.95. The HOMA-AD index was demonstrated to be a useful surrogate marker for detecting IR among adult women and presented a similar performance compared with the HOMA1-IR index. These results may assist physicians and researchers in determining which method to use to evaluate IR in light of the available facilities.
Directory of Open Access Journals (Sweden)
Brunna Sullara Vilela
Full Text Available The major adverse consequences of obesity are associated with the development of insulin resistance (IR and adiposopathy. The Homeostasis Model Assessment-Adiponectin (HOMA-AD was proposed as a modified version of the HOMA1-IR, which incorporates adiponectin in the denominator of the index.To evaluate the performance of the HOMA-AD index compared with the HOMA1-IR index as a surrogate marker of IR in women, and to establish the cutoff value of the HOMA-AD.The Brazilian Metabolic Syndrome Study (BRAMS is a cross-sectional multicenter survey. The data from 1,061 subjects met the desired criteria: 18-65 years old, BMI: 18.5-49.9 Kg/m² and without diabetes. The IR was assessed by the indexes HOMA1-IR and HOMA-AD (total sample and by the hyperglycemic clamp (n = 49. Metabolic syndrome was defined using the IDF criteria.For the IR assessed by the clamp, the HOMA-AD demonstrated a stronger coefficient of correlation (r = -0.64 compared with the HOMA1-IR (r = -0.56; p 0.05. The optimal cutoff identified for the HOMA-AD for the diagnosis of IR was 0.95.The HOMA-AD index was demonstrated to be a useful surrogate marker for detecting IR among adult women and presented a similar performance compared with the HOMA1-IR index. These results may assist physicians and researchers in determining which method to use to evaluate IR in light of the available facilities.
Categorical regression dose-response modeling
The goal of this training is to provide participants with training on the use of the U.S. EPA’s Categorical Regression soft¬ware (CatReg) and its application to risk assessment. Categorical regression fits mathematical models to toxicity data that have been assigned ord...
Applied Regression Modeling A Business Approach
Pardoe, Iain
2012-01-01
An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a
Regression Models For Multivariate Count Data.
Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei
2017-01-01
Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.
Testing homogeneity in Weibull-regression models.
Bolfarine, Heleno; Valença, Dione M
2005-10-01
In survival studies with families or geographical units it may be of interest testing whether such groups are homogeneous for given explanatory variables. In this paper we consider score type tests for group homogeneity based on a mixing model in which the group effect is modelled as a random variable. As opposed to hazard-based frailty models, this model presents survival times that conditioned on the random effect, has an accelerated failure time representation. The test statistics requires only estimation of the conventional regression model without the random effect and does not require specifying the distribution of the random effect. The tests are derived for a Weibull regression model and in the uncensored situation, a closed form is obtained for the test statistic. A simulation study is used for comparing the power of the tests. The proposed tests are applied to real data sets with censored data.
Mixed-effects regression models in linguistics
Heylen, Kris; Geeraerts, Dirk
2018-01-01
When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addres...
Regression modeling methods, theory, and computation with SAS
Panik, Michael
2009-01-01
Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,
Influence diagnostics in meta-regression model.
Shi, Lei; Zuo, ShanShan; Yu, Dalei; Zhou, Xiaohua
2017-09-01
This paper studies the influence diagnostics in meta-regression model including case deletion diagnostic and local influence analysis. We derive the subset deletion formulae for the estimation of regression coefficient and heterogeneity variance and obtain the corresponding influence measures. The DerSimonian and Laird estimation and maximum likelihood estimation methods in meta-regression are considered, respectively, to derive the results. Internal and external residual and leverage measure are defined. The local influence analysis based on case-weights perturbation scheme, responses perturbation scheme, covariate perturbation scheme, and within-variance perturbation scheme are explored. We introduce a method by simultaneous perturbing responses, covariate, and within-variance to obtain the local influence measure, which has an advantage of capable to compare the influence magnitude of influential studies from different perturbations. An example is used to illustrate the proposed methodology. Copyright © 2017 John Wiley & Sons, Ltd.
AIRLINE ACTIVITY FORECASTING BY REGRESSION MODELS
Directory of Open Access Journals (Sweden)
Н. Білак
2012-04-01
Full Text Available Proposed linear and nonlinear regression models, which take into account the equation of trend and seasonality indices for the analysis and restore the volume of passenger traffic over the past period of time and its prediction for future years, as well as the algorithm of formation of these models based on statistical analysis over the years. The desired model is the first step for the synthesis of more complex models, which will enable forecasting of passenger (income level airline with the highest accuracy and time urgency.
Modeling oil production based on symbolic regression
International Nuclear Information System (INIS)
Yang, Guangfei; Li, Xianneng; Wang, Jianliang; Lian, Lian; Ma, Tieju
2015-01-01
Numerous models have been proposed to forecast the future trends of oil production and almost all of them are based on some predefined assumptions with various uncertainties. In this study, we propose a novel data-driven approach that uses symbolic regression to model oil production. We validate our approach on both synthetic and real data, and the results prove that symbolic regression could effectively identify the true models beneath the oil production data and also make reliable predictions. Symbolic regression indicates that world oil production will peak in 2021, which broadly agrees with other techniques used by researchers. Our results also show that the rate of decline after the peak is almost half the rate of increase before the peak, and it takes nearly 12 years to drop 4% from the peak. These predictions are more optimistic than those in several other reports, and the smoother decline will provide the world, especially the developing countries, with more time to orchestrate mitigation plans. -- Highlights: •A data-driven approach has been shown to be effective at modeling the oil production. •The Hubbert model could be discovered automatically from data. •The peak of world oil production is predicted to appear in 2021. •The decline rate after peak is half of the increase rate before peak. •Oil production projected to decline 4% post-peak
Geographically weighted regression model on poverty indicator
Slamet, I.; Nugroho, N. F. T. A.; Muslich
2017-12-01
In this research, we applied geographically weighted regression (GWR) for analyzing the poverty in Central Java. We consider Gaussian Kernel as weighted function. The GWR uses the diagonal matrix resulted from calculating kernel Gaussian function as a weighted function in the regression model. The kernel weights is used to handle spatial effects on the data so that a model can be obtained for each location. The purpose of this paper is to model of poverty percentage data in Central Java province using GWR with Gaussian kernel weighted function and to determine the influencing factors in each regency/city in Central Java province. Based on the research, we obtained geographically weighted regression model with Gaussian kernel weighted function on poverty percentage data in Central Java province. We found that percentage of population working as farmers, population growth rate, percentage of households with regular sanitation, and BPJS beneficiaries are the variables that affect the percentage of poverty in Central Java province. In this research, we found the determination coefficient R2 are 68.64%. There are two categories of district which are influenced by different of significance factors.
Yin, Jinhua; Li, Ming; Xu, Lu; Wang, Ying; Cheng, Hong; Zhao, Xiaoyuan; Mi, Jie
2013-11-15
The aim of this study is to assess the association between the degree of insulin resistance and the different components of the metabolic syndrome among Chinese children and adolescents. Moreover, to determine the cut-off values for homeostasis model assessment of insulin resistance (HOMA-IR) at MS risk. 3203 Chinese children aged 6 to 18 years were recruited. Anthropometric and biochemical parameters were measured. Metabolic syndrome (MS) was identified by a modified Adult Treatment Panel III (ATP III) definition. HOMA-IR index was calculated and the normal reference ranges were defined from the healthy participants. Receiver operating characteristic (ROC) analysis was used to find the optimal cutoff of HOMA-IR for diagnosis of MS. With the increase of insulin resistance (quintile of HOMA-IR value), the ORs of suffering MS or its related components were significantly increased. Participants in the highest quintile of HOMA-IR were about 60 times more likely to be classified with metabolic syndrome than those in the lowest quintile group. Similarly, the mean values of insulin and HOMA-IR increased with the number of MS components. The present HOMA-IR cutoff point corresponding to the 95th percentile of our healthy reference children was 3.0 for whole participants, 2.6 for children in prepubertal stage and 3.2 in pubertal period, respectively. The optimal point for diagnosis of MS was 2.3 in total participants, 1.7 in prepubertal children and 2.6 in pubertal adolescents, respectively, by ROC curve, which yielded high sensitivity and moderate specificity for a screening test. According to HOMA-IR > 3.0, the prevalence of insulin resistance in obese or MS children were 44.3% and 61.6% respectively. Our data indicates insulin resistance is common among Chinese obese children and adolescents, and is strongly related to MS risk, therefore requiring consideration early in life. As a reliable measure of insulin resistance and assessment of MS risk, the optimal HOMA-IR cut
Adaptive regression for modeling nonlinear relationships
Knafl, George J
2016-01-01
This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the s...
Bayesian Inference of a Multivariate Regression Model
Directory of Open Access Journals (Sweden)
Marick S. Sinay
2014-01-01
Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.
General regression and representation model for classification.
Directory of Open Access Journals (Sweden)
Jianjun Qian
Full Text Available Recently, the regularized coding-based classification methods (e.g. SRC and CRC show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients and the specific information (weight matrix of image pixels to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR and robust general regression and representation classifier (R-GRR. The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms.
Confidence bands for inverse regression models
International Nuclear Information System (INIS)
Birke, Melanie; Bissantz, Nicolai; Holzmann, Hajo
2010-01-01
We construct uniform confidence bands for the regression function in inverse, homoscedastic regression models with convolution-type operators. Here, the convolution is between two non-periodic functions on the whole real line rather than between two periodic functions on a compact interval, since the former situation arguably arises more often in applications. First, following Bickel and Rosenblatt (1973 Ann. Stat. 1 1071–95) we construct asymptotic confidence bands which are based on strong approximations and on a limit theorem for the supremum of a stationary Gaussian process. Further, we propose bootstrap confidence bands based on the residual bootstrap and prove consistency of the bootstrap procedure. A simulation study shows that the bootstrap confidence bands perform reasonably well for moderate sample sizes. Finally, we apply our method to data from a gel electrophoresis experiment with genetically engineered neuronal receptor subunits incubated with rat brain extract
Multitask Quantile Regression under the Transnormal Model.
Fan, Jianqing; Xue, Lingzhou; Zou, Hui
2016-01-01
We consider estimating multi-task quantile regression under the transnormal model, with focus on high-dimensional setting. We derive a surprisingly simple closed-form solution through rank-based covariance regularization. In particular, we propose the rank-based ℓ 1 penalization with positive definite constraints for estimating sparse covariance matrices, and the rank-based banded Cholesky decomposition regularization for estimating banded precision matrices. By taking advantage of alternating direction method of multipliers, nearest correlation matrix projection is introduced that inherits sampling properties of the unprojected one. Our work combines strengths of quantile regression and rank-based covariance regularization to simultaneously deal with nonlinearity and nonnormality for high-dimensional regression. Furthermore, the proposed method strikes a good balance between robustness and efficiency, achieves the "oracle"-like convergence rate, and provides the provable prediction interval under the high-dimensional setting. The finite-sample performance of the proposed method is also examined. The performance of our proposed rank-based method is demonstrated in a real application to analyze the protein mass spectroscopy data.
Crime Modeling using Spatial Regression Approach
Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.
2018-01-01
Act of criminality in Indonesia increased both variety and quantity every year. As murder, rape, assault, vandalism, theft, fraud, fencing, and other cases that make people feel unsafe. Risk of society exposed to crime is the number of reported cases in the police institution. The higher of the number of reporter to the police institution then the number of crime in the region is increasing. In this research, modeling criminality in South Sulawesi, Indonesia with the dependent variable used is the society exposed to the risk of crime. Modelling done by area approach is the using Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods. The independent variable used is the population density, the number of poor population, GDP per capita, unemployment and the human development index (HDI). Based on the analysis using spatial regression can be shown that there are no dependencies spatial both lag or errors in South Sulawesi.
AN APPLICATION OF FUNCTIONAL MULTIVARIATE REGRESSION MODEL TO MULTICLASS CLASSIFICATION
Krzyśko, Mirosław; Smaga, Łukasz
2017-01-01
In this paper, the scale response functional multivariate regression model is considered. By using the basis functions representation of functional predictors and regression coefficients, this model is rewritten as a multivariate regression model. This representation of the functional multivariate regression model is used for multiclass classification for multivariate functional data. Computational experiments performed on real labelled data sets demonstrate the effectiveness of the proposed ...
Entrepreneurial intention modeling using hierarchical multiple regression
Directory of Open Access Journals (Sweden)
Marina Jeger
2014-12-01
Full Text Available The goal of this study is to identify the contribution of effectuation dimensions to the predictive power of the entrepreneurial intention model over and above that which can be accounted for by other predictors selected and confirmed in previous studies. As is often the case in social and behavioral studies, some variables are likely to be highly correlated with each other. Therefore, the relative amount of variance in the criterion variable explained by each of the predictors depends on several factors such as the order of variable entry and sample specifics. The results show the modest predictive power of two dimensions of effectuation prior to the introduction of the theory of planned behavior elements. The article highlights the main advantages of applying hierarchical regression in social sciences as well as in the specific context of entrepreneurial intention formation, and addresses some of the potential pitfalls that this type of analysis entails.
An Additive-Multiplicative Cox-Aalen Regression Model
DEFF Research Database (Denmark)
Scheike, Thomas H.; Zhang, Mei-Jie
2002-01-01
Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects...
Variable selection and model choice in geoadditive regression models.
Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard
2009-06-01
Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.
Hierarchical regression analysis in structural Equation Modeling
de Jong, P.F.
1999-01-01
In a hierarchical or fixed-order regression analysis, the independent variables are entered into the regression equation in a prespecified order. Such an analysis is often performed when the extra amount of variance accounted for in a dependent variable by a specific independent variable is the main
Poisson Mixture Regression Models for Heart Disease Prediction.
Mufudza, Chipo; Erol, Hamza
2016-01-01
Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.
Modeling maximum daily temperature using a varying coefficient regression model
Han Li; Xinwei Deng; Dong-Yum Kim; Eric P. Smith
2014-01-01
Relationships between stream water and air temperatures are often modeled using linear or nonlinear regression methods. Despite a strong relationship between water and air temperatures and a variety of models that are effective for data summarized on a weekly basis, such models did not yield consistently good predictions for summaries such as daily maximum temperature...
Introduction to the use of regression models in epidemiology.
Bender, Ralf
2009-01-01
Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.
DEFF Research Database (Denmark)
Lau, Cathrine; Pedersen, Oluf; Færch, Kristine
2005-01-01
, and insulin resistance was estimated using the homeostasis model assessment of insulin resistance (HOMA-IR). Multiple regressions were performed with HOMA-IR as the dependent variable and carbohydrate-related factors as explanatory variables. All models were adjusted for age, sex, smoking, physical activity......, total energy intake, BMI, and waist circumference. RESULTS - intake of lactose was positively associated with HOMA-IR (P < 0.0001), whereas daily glycemic load and intake of glucose, fructose, dietary fiber, total carbohydrate, fruit, and vegetables were inversely associated with HOMA-IR (P < 0...
Model performance analysis and model validation in logistic regression
Directory of Open Access Journals (Sweden)
Rosa Arboretti Giancristofaro
2007-10-01
Full Text Available In this paper a new model validation procedure for a logistic regression model is presented. At first, we illustrate a brief review of different techniques of model validation. Next, we define a number of properties required for a model to be considered "good", and a number of quantitative performance measures. Lastly, we describe a methodology for the assessment of the performance of a given model by using an example taken from a management study.
Logistic Regression Modeling of Diminishing Manufacturing Sources for Integrated Circuits
National Research Council Canada - National Science Library
Gravier, Michael
1999-01-01
.... The research identified logistic regression as a powerful tool for analysis of DMSMS and further developed twenty models attempting to identify the "best" way to model and predict DMSMS using logistic regression...
Vaeth, Michael; Skovlund, Eva
2004-06-15
For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.
The MIDAS Touch: Mixed Data Sampling Regression Models
Ghysels, Eric; Santa-Clara, Pedro; Valkanov, Rossen
2004-01-01
We introduce Mixed Data Sampling (henceforth MIDAS) regression models. The regressions involve time series data sampled at different frequencies. Technically speaking MIDAS models specify conditional expectations as a distributed lag of regressors recorded at some higher sampling frequencies. We examine the asymptotic properties of MIDAS regression estimation and compare it with traditional distributed lag models. MIDAS regressions have wide applicability in macroeconomics and ï¿½nance.
Model selection in kernel ridge regression
DEFF Research Database (Denmark)
Exterkate, Peter
2013-01-01
Kernel ridge regression is a technique to perform ridge regression with a potentially infinite number of nonlinear transformations of the independent variables as regressors. This method is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts....... The influence of the choice of kernel and the setting of tuning parameters on forecast accuracy is investigated. Several popular kernels are reviewed, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. The latter two kernels are interpreted in terms of their smoothing properties......, and the tuning parameters associated to all these kernels are related to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, guidelines are provided for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study...
Mixture of Regression Models with Single-Index
Xiang, Sijia; Yao, Weixin
2016-01-01
In this article, we propose a class of semiparametric mixture regression models with single-index. We argue that many recently proposed semiparametric/nonparametric mixture regression models can be considered special cases of the proposed model. However, unlike existing semiparametric mixture regression models, the new pro- posed model can easily incorporate multivariate predictors into the nonparametric components. Backfitting estimates and the corresponding algorithms have been proposed for...
Linear regression crash prediction models : issues and proposed solutions.
2010-05-01
The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...
Model-based Quantile Regression for Discrete Data
Padellini, Tullia; Rue, Haavard
2018-01-01
Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite
Forecasting Ebola with a regression transmission model
Directory of Open Access Journals (Sweden)
Jason Asher
2018-03-01
Full Text Available We describe a relatively simple stochastic model of Ebola transmission that was used to produce forecasts with the lowest mean absolute error among Ebola Forecasting Challenge participants. The model enabled prediction of peak incidence, the timing of this peak, and final size of the outbreak. The underlying discrete-time compartmental model used a time-varying reproductive rate modeled as a multiplicative random walk driven by the number of infectious individuals. This structure generalizes traditional Susceptible-Infected-Recovered (SIR disease modeling approaches and allows for the flexible consideration of outbreaks with complex trajectories of disease dynamics. Keywords: Ebola, Forecasting, Mathematical modeling, Bayesian inference
Forecasting Ebola with a regression transmission model
Asher, Jason
2017-01-01
We describe a relatively simple stochastic model of Ebola transmission that was used to produce forecasts with the lowest mean absolute error among Ebola Forecasting Challenge participants. The model enabled prediction of peak incidence, the timing of this peak, and final size of the outbreak. The underlying discrete-time compartmental model used a time-varying reproductive rate modeled as a multiplicative random walk driven by the number of infectious individuals. This structure generalizes ...
Model Selection in Kernel Ridge Regression
DEFF Research Database (Denmark)
Exterkate, Peter
Kernel ridge regression is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts. This paper investigates the influence of the choice of kernel and the setting of tuning parameters on forecast accuracy. We review several popular kernels......, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. We interpret the latter two kernels in terms of their smoothing properties, and we relate the tuning parameters associated to all these kernels to smoothness measures of the prediction function and to the signal-to-noise ratio. Based...... on these interpretations, we provide guidelines for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study confirms the practical usefulness of these rules of thumb. Finally, the flexible and smooth functional forms provided by the Gaussian and Sinc kernels makes them widely...
International Nuclear Information System (INIS)
Jafri, Y.Z.; Kamal, L.
2007-01-01
Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)
Corporate prediction models, ratios or regression analysis?
Bijnen, E.J.; Wijn, M.F.C.M.
1994-01-01
The models developed in the literature with respect to the prediction of a company s failure are based on ratios. It has been shown before that these models should be rejected on theoretical grounds. Our study of industrial companies in the Netherlands shows that the ratios which are used in
STREAMFLOW AND WATER QUALITY REGRESSION MODELING ...
African Journals Online (AJOL)
... downstream Obigbo station show: consistent time-trends in degree of contamination; linear and non-linear relationships for water quality models against total dissolved solids (TDS), total suspended sediment (TSS), chloride, pH and sulphate; and non-linear relationship for streamflow and water quality transport models.
Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model
Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami
2017-06-01
A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.
Multiattribute shopping models and ridge regression analysis
Timmermans, H.J.P.
1981-01-01
Policy decisions regarding retailing facilities essentially involve multiple attributes of shopping centres. If mathematical shopping models are to contribute to these decision processes, their structure should reflect the multiattribute character of retailing planning. Examination of existing
Linear Regression Models for Estimating True Subsurface ...
Indian Academy of Sciences (India)
47
The objective is to minimize the processing time and computer memory required. 10 to carry out inversion .... to the mainland by two long bridges. .... term. In this approach, the model converges when the squared sum of the differences. 143.
Moderation analysis using a two-level regression model.
Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott
2014-10-01
Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.
Alternative regression models to assess increase in childhood BMI
Beyerlein, Andreas; Fahrmeir, Ludwig; Mansmann, Ulrich; Toschke, André M
2008-01-01
Abstract Background Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 childre...
Directory of Open Access Journals (Sweden)
Hong-Juan Li
2013-04-01
Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.
Li, Siou; Yin, Changhao; Zhao, Weina; Zhu, Haifu; Xu, Dan; Xu, Qing; Jiao, Yang; Wang, Xue; Qiao, Hong
2018-01-01
Whether insulin resistance (IR) predicts worse functional outcome in ischemic stroke is still a matter of debate. The aim of the present study is to determine the association between IR and risk of poor outcome in 173 Chinese nondiabetic patients with acute ischemic stroke. This is a prospective, population-based cohort study. Insulin sensitivity, expressed by the homeostasis model assessment (HOMA) of insulin sensitivity (HOMA index = (fasting insulin × fasting glucose)/22.5). IR was defined by HOMA-IR index in the top quartile (Q4). Functional impairment was evaluated at discharge using the modified Rankin scale (mRS). The median (interquartile range) HOMA-IR was 2.14 (1.17–2.83), and Q4 was at least 2.83. There was a significantly positive correlation between HOMA-IR and National Institutes of Health Stroke Scale (r = 0.408; PIR group were associated with a higher risk of poor functional outcome (odds ratio (OR) = 3.23; 95% confidence interval (CI) = 1.75–5.08; P=0.001). In multivariate models comparing the third and fourth quartiles against the first quartile of the HOMA-IR, levels of HOMA-IR were associated with poor outcome, and the adjusted risk of poor outcome increased by 207% (OR = 3.05 (95% CI 1.70–4.89), P=0.006) and 429% (5.29 (3.05–9.80), PHOMA-IR to clinical examination variables (P=0.02). High HOMA-IR index is associated with a poor functional outcome in nondiabetic patients with acute ischemic stroke. PMID:29588341
Poisson Mixture Regression Models for Heart Disease Prediction
Erol, Hamza
2016-01-01
Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611
A test for the parameters of multiple linear regression models ...
African Journals Online (AJOL)
A test for the parameters of multiple linear regression models is developed for conducting tests simultaneously on all the parameters of multiple linear regression models. The test is robust relative to the assumptions of homogeneity of variances and absence of serial correlation of the classical F-test. Under certain null and ...
Mixed Frequency Data Sampling Regression Models: The R Package midasr
Directory of Open Access Journals (Sweden)
Eric Ghysels
2016-08-01
Full Text Available When modeling economic relationships it is increasingly common to encounter data sampled at different frequencies. We introduce the R package midasr which enables estimating regression models with variables sampled at different frequencies within a MIDAS regression framework put forward in work by Ghysels, Santa-Clara, and Valkanov (2002. In this article we define a general autoregressive MIDAS regression model with multiple variables of different frequencies and show how it can be specified using the familiar R formula interface and estimated using various optimization methods chosen by the researcher. We discuss how to check the validity of the estimated model both in terms of numerical convergence and statistical adequacy of a chosen regression specification, how to perform model selection based on a information criterion, how to assess forecasting accuracy of the MIDAS regression model and how to obtain a forecast aggregation of different MIDAS regression models. We illustrate the capabilities of the package with a simulated MIDAS regression model and give two empirical examples of application of MIDAS regression.
Impact of multicollinearity on small sample hydrologic regression models
Kroll, Charles N.; Song, Peter
2013-06-01
Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.
A generalized multivariate regression model for modelling ocean wave heights
Wang, X. L.; Feng, Y.; Swail, V. R.
2012-04-01
In this study, a generalized multivariate linear regression model is developed to represent the relationship between 6-hourly ocean significant wave heights (Hs) and the corresponding 6-hourly mean sea level pressure (MSLP) fields. The model is calibrated using the ERA-Interim reanalysis of Hs and MSLP fields for 1981-2000, and is validated using the ERA-Interim reanalysis for 2001-2010 and ERA40 reanalysis of Hs and MSLP for 1958-2001. The performance of the fitted model is evaluated in terms of Pierce skill score, frequency bias index, and correlation skill score. Being not normally distributed, wave heights are subjected to a data adaptive Box-Cox transformation before being used in the model fitting. Also, since 6-hourly data are being modelled, lag-1 autocorrelation must be and is accounted for. The models with and without Box-Cox transformation, and with and without accounting for autocorrelation, are inter-compared in terms of their prediction skills. The fitted MSLP-Hs relationship is then used to reconstruct historical wave height climate from the 6-hourly MSLP fields taken from the Twentieth Century Reanalysis (20CR, Compo et al. 2011), and to project possible future wave height climates using CMIP5 model simulations of MSLP fields. The reconstructed and projected wave heights, both seasonal means and maxima, are subject to a trend analysis that allows for non-linear (polynomial) trends.
Identification of Influential Points in a Linear Regression Model
Directory of Open Access Journals (Sweden)
Jan Grosz
2011-03-01
Full Text Available The article deals with the detection and identification of influential points in the linear regression model. Three methods of detection of outliers and leverage points are described. These procedures can also be used for one-sample (independentdatasets. This paper briefly describes theoretical aspects of several robust methods as well. Robust statistics is a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. A simulation model of the simple linear regression is presented.
Detection of epistatic effects with logic regression and a classical linear regression model.
Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata
2014-02-01
To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.
Random regression models for detection of gene by environment interaction
Directory of Open Access Journals (Sweden)
Meuwissen Theo HE
2007-02-01
Full Text Available Abstract Two random regression models, where the effect of a putative QTL was regressed on an environmental gradient, are described. The first model estimates the correlation between intercept and slope of the random regression, while the other model restricts this correlation to 1 or -1, which is expected under a bi-allelic QTL model. The random regression models were compared to a model assuming no gene by environment interactions. The comparison was done with regards to the models ability to detect QTL, to position them accurately and to detect possible QTL by environment interactions. A simulation study based on a granddaughter design was conducted, and QTL were assumed, either by assigning an effect independent of the environment or as a linear function of a simulated environmental gradient. It was concluded that the random regression models were suitable for detection of QTL effects, in the presence and absence of interactions with environmental gradients. Fixing the correlation between intercept and slope of the random regression had a positive effect on power when the QTL effects re-ranked between environments.
Keith, Timothy Z
2014-01-01
Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. Covers both MR and SEM, while explaining their relevance to one another Also includes path analysis, confirmatory factor analysis, and latent growth modeling Figures and tables throughout provide examples and illustrate key concepts and techniques For additional resources, please visit: http://tzkeith.com/.
Tutorial on Using Regression Models with Count Outcomes Using R
Directory of Open Access Journals (Sweden)
A. Alexander Beaujean
2016-02-01
Full Text Available Education researchers often study count variables, such as times a student reached a goal, discipline referrals, and absences. Most researchers that study these variables use typical regression methods (i.e., ordinary least-squares either with or without transforming the count variables. In either case, using typical regression for count data can produce parameter estimates that are biased, thus diminishing any inferences made from such data. As count-variable regression models are seldom taught in training programs, we present a tutorial to help educational researchers use such methods in their own research. We demonstrate analyzing and interpreting count data using Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. The count regression methods are introduced through an example using the number of times students skipped class. The data for this example are freely available and the R syntax used run the example analyses are included in the Appendix.
Regularized multivariate regression models with skew-t error distributions
Chen, Lianfu; Pourahmadi, Mohsen; Maadooliat, Mehdi
2014-01-01
We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both
Correlation-regression model for physico-chemical quality of ...
African Journals Online (AJOL)
abusaad
areas, suggesting that groundwater quality in urban areas is closely related with land use ... the ground water, with correlation and regression model is also presented. ...... WHO (World Health Organization) (1985). Health hazards from nitrates.
Wavelet regression model in forecasting crude oil price
Hamid, Mohd Helmie; Shabri, Ani
2017-05-01
This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.
Real estate value prediction using multivariate regression models
Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav
2017-11-01
The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.
Application of random regression models to the genetic evaluation ...
African Journals Online (AJOL)
The model included fixed regression on AM (range from 30 to 138 mo) and the effect of herd-measurement date concatenation. Random parts of the model were RRM coefficients for additive and permanent environmental effects, while residual effects were modelled to account for heterogeneity of variance by AY. Estimates ...
The APT model as reduced-rank regression
Bekker, P.A.; Dobbelstein, P.; Wansbeek, T.J.
Integrating the two steps of an arbitrage pricing theory (APT) model leads to a reduced-rank regression (RRR) model. So the results on RRR can be used to estimate APT models, making estimation very simple. We give a succinct derivation of estimation of RRR, derive the asymptotic variance of RRR
Alternative regression models to assess increase in childhood BMI
Directory of Open Access Journals (Sweden)
Mansmann Ulrich
2008-09-01
Full Text Available Abstract Background Body mass index (BMI data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs, quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS. We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. Results GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. Conclusion GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.
Alternative regression models to assess increase in childhood BMI.
Beyerlein, Andreas; Fahrmeir, Ludwig; Mansmann, Ulrich; Toschke, André M
2008-09-08
Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.
Robust mislabel logistic regression without modeling mislabel probabilities.
Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun
2018-03-01
Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.
Linear regression models for quantitative assessment of left ...
African Journals Online (AJOL)
Changes in left ventricular structures and function have been reported in cardiomyopathies. No prediction models have been established in this environment. This study established regression models for prediction of left ventricular structures in normal subjects. A sample of normal subjects was drawn from a large urban ...
Geographically Weighted Logistic Regression Applied to Credit Scoring Models
Directory of Open Access Journals (Sweden)
Pedro Henrique Melo Albuquerque
Full Text Available Abstract This study used real data from a Brazilian financial institution on transactions involving Consumer Direct Credit (CDC, granted to clients residing in the Distrito Federal (DF, to construct credit scoring models via Logistic Regression and Geographically Weighted Logistic Regression (GWLR techniques. The aims were: to verify whether the factors that influence credit risk differ according to the borrower’s geographic location; to compare the set of models estimated via GWLR with the global model estimated via Logistic Regression, in terms of predictive power and financial losses for the institution; and to verify the viability of using the GWLR technique to develop credit scoring models. The metrics used to compare the models developed via the two techniques were the AICc informational criterion, the accuracy of the models, the percentage of false positives, the sum of the value of false positive debt, and the expected monetary value of portfolio default compared with the monetary value of defaults observed. The models estimated for each region in the DF were distinct in their variables and coefficients (parameters, with it being concluded that credit risk was influenced differently in each region in the study. The Logistic Regression and GWLR methodologies presented very close results, in terms of predictive power and financial losses for the institution, and the study demonstrated viability in using the GWLR technique to develop credit scoring models for the target population in the study.
Physics constrained nonlinear regression models for time series
International Nuclear Information System (INIS)
Majda, Andrew J; Harlim, John
2013-01-01
A central issue in contemporary science is the development of data driven statistical nonlinear dynamical models for time series of partial observations of nature or a complex physical model. It has been established recently that ad hoc quadratic multi-level regression (MLR) models can have finite-time blow up of statistical solutions and/or pathological behaviour of their invariant measure. Here a new class of physics constrained multi-level quadratic regression models are introduced, analysed and applied to build reduced stochastic models from data of nonlinear systems. These models have the advantages of incorporating memory effects in time as well as the nonlinear noise from energy conserving nonlinear interactions. The mathematical guidelines for the performance and behaviour of these physics constrained MLR models as well as filtering algorithms for their implementation are developed here. Data driven applications of these new multi-level nonlinear regression models are developed for test models involving a nonlinear oscillator with memory effects and the difficult test case of the truncated Burgers–Hopf model. These new physics constrained quadratic MLR models are proposed here as process models for Bayesian estimation through Markov chain Monte Carlo algorithms of low frequency behaviour in complex physical data. (paper)
Model-based Quantile Regression for Discrete Data
Padellini, Tullia
2018-04-10
Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite the fact that this leads to a proper posterior for the regression coefficients, the resulting posterior variance is however affected by an unidentifiable parameter, hence any inferential procedure beside point estimation is unreliable. We propose a model-based approach for quantile regression that considers quantiles of the generating distribution directly, and thus allows for a proper uncertainty quantification. We then create a link between quantile regression and generalised linear models by mapping the quantiles to the parameter of the response variable, and we exploit it to fit the model with R-INLA. We extend it also in the case of discrete responses, where there is no 1-to-1 relationship between quantiles and distribution\\'s parameter, by introducing continuous generalisations of the most common discrete variables (Poisson, Binomial and Negative Binomial) to be exploited in the fitting.
Maximum Entropy Discrimination Poisson Regression for Software Reliability Modeling.
Chatzis, Sotirios P; Andreou, Andreas S
2015-11-01
Reliably predicting software defects is one of the most significant tasks in software engineering. Two of the major components of modern software reliability modeling approaches are: 1) extraction of salient features for software system representation, based on appropriately designed software metrics and 2) development of intricate regression models for count data, to allow effective software reliability data modeling and prediction. Surprisingly, research in the latter frontier of count data regression modeling has been rather limited. More specifically, a lack of simple and efficient algorithms for posterior computation has made the Bayesian approaches appear unattractive, and thus underdeveloped in the context of software reliability modeling. In this paper, we try to address these issues by introducing a novel Bayesian regression model for count data, based on the concept of max-margin data modeling, effected in the context of a fully Bayesian model treatment with simple and efficient posterior distribution updates. Our novel approach yields a more discriminative learning technique, making more effective use of our training data during model inference. In addition, it allows of better handling uncertainty in the modeled data, which can be a significant problem when the training data are limited. We derive elegant inference algorithms for our model under the mean-field paradigm and exhibit its effectiveness using the publicly available benchmark data sets.
Forecasting daily meteorological time series using ARIMA and regression models
Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir
2018-04-01
The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.
Multiple Response Regression for Gaussian Mixture Models with Known Labels.
Lee, Wonyul; Du, Ying; Sun, Wei; Hayes, D Neil; Liu, Yufeng
2012-12-01
Multiple response regression is a useful regression technique to model multiple response variables using the same set of predictor variables. Most existing methods for multiple response regression are designed for modeling homogeneous data. In many applications, however, one may have heterogeneous data where the samples are divided into multiple groups. Our motivating example is a cancer dataset where the samples belong to multiple cancer subtypes. In this paper, we consider modeling the data coming from a mixture of several Gaussian distributions with known group labels. A naive approach is to split the data into several groups according to the labels and model each group separately. Although it is simple, this approach ignores potential common structures across different groups. We propose new penalized methods to model all groups jointly in which the common and unique structures can be identified. The proposed methods estimate the regression coefficient matrix, as well as the conditional inverse covariance matrix of response variables. Asymptotic properties of the proposed methods are explored. Through numerical examples, we demonstrate that both estimation and prediction can be improved by modeling all groups jointly using the proposed methods. An application to a glioblastoma cancer dataset reveals some interesting common and unique gene relationships across different cancer subtypes.
Thermal Efficiency Degradation Diagnosis Method Using Regression Model
International Nuclear Information System (INIS)
Jee, Chang Hyun; Heo, Gyun Young; Jang, Seok Won; Lee, In Cheol
2011-01-01
This paper proposes an idea for thermal efficiency degradation diagnosis in turbine cycles, which is based on turbine cycle simulation under abnormal conditions and a linear regression model. The correlation between the inputs for representing degradation conditions (normally unmeasured but intrinsic states) and the simulation outputs (normally measured but superficial states) was analyzed with the linear regression model. The regression models can inversely response an associated intrinsic state for a superficial state observed from a power plant. The diagnosis method proposed herein is classified into three processes, 1) simulations for degradation conditions to get measured states (referred as what-if method), 2) development of the linear model correlating intrinsic and superficial states, and 3) determination of an intrinsic state using the superficial states of current plant and the linear regression model (referred as inverse what-if method). The what-if method is to generate the outputs for the inputs including various root causes and/or boundary conditions whereas the inverse what-if method is the process of calculating the inverse matrix with the given superficial states, that is, component degradation modes. The method suggested in this paper was validated using the turbine cycle model for an operating power plant
Harrell , Jr , Frank E
2015-01-01
This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modeling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for fitting nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. The reader will gain a keen understanding of predictive accuracy, and the harm of categorizing continuous predictors or outcomes. This text realistically...
Flexible competing risks regression modeling and goodness-of-fit
DEFF Research Database (Denmark)
Scheike, Thomas; Zhang, Mei-Jie
2008-01-01
In this paper we consider different approaches for estimation and assessment of covariate effects for the cumulative incidence curve in the competing risks model. The classic approach is to model all cause-specific hazards and then estimate the cumulative incidence curve based on these cause...... models that is easy to fit and contains the Fine-Gray model as a special case. One advantage of this approach is that our regression modeling allows for non-proportional hazards. This leads to a new simple goodness-of-fit procedure for the proportional subdistribution hazards assumption that is very easy...... of the flexible regression models to analyze competing risks data when non-proportionality is present in the data....
The art of regression modeling in road safety
Hauer, Ezra
2015-01-01
This unique book explains how to fashion useful regression models from commonly available data to erect models essential for evidence-based road safety management and research. Composed from techniques and best practices presented over many years of lectures and workshops, The Art of Regression Modeling in Road Safety illustrates that fruitful modeling cannot be done without substantive knowledge about the modeled phenomenon. Class-tested in courses and workshops across North America, the book is ideal for professionals, researchers, university professors, and graduate students with an interest in, or responsibilities related to, road safety. This book also: · Presents for the first time a powerful analytical tool for road safety researchers and practitioners · Includes problems and solutions in each chapter as well as data and spreadsheets for running models and PowerPoint presentation slides · Features pedagogy well-suited for graduate courses and workshops including problems, solutions, and PowerPoint p...
Model building strategy for logistic regression: purposeful selection.
Zhang, Zhongheng
2016-03-01
Logistic regression is one of the most commonly used models to account for confounders in medical literature. The article introduces how to perform purposeful selection model building strategy with R. I stress on the use of likelihood ratio test to see whether deleting a variable will have significant impact on model fit. A deleted variable should also be checked for whether it is an important adjustment of remaining covariates. Interaction should be checked to disentangle complex relationship between covariates and their synergistic effect on response variable. Model should be checked for the goodness-of-fit (GOF). In other words, how the fitted model reflects the real data. Hosmer-Lemeshow GOF test is the most widely used for logistic regression model.
Andrade, Maria Izabel Siqueira de; Oliveira, Juliana Souza; Leal, Vanessa Sá; Lima, Niedja Maria da Silva; Costa, Emília Chagas; Aquino, Nathalia Barbosa de; Lira, Pedro Israel Cabral de
2016-06-01
To identify cutoff points of the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) index established for adolescents and discuss their applicability for the diagnosis of insulin resistance in Brazilian adolescents. A systematic review was performed in the PubMed, Lilacs and SciELO databases, using the following descriptors: "Adolescents", "insulin resistance" and "ROC curve". Original articles carried out with adolescents published between 2005 and 2015 in Portuguese, English or Spanish languages, which included the statistical analysis using ROC curve to determine the index cutoff (HOMA-IR) were included. A total of 184 articles were identified and after the study phases were applied, seven articles were selected for the review. All selected studies established their cutoffs using a ROC curve, with the lowest observed cutoff of 1.65 for girls and 1.95 for boys and the highest of 3.82 for girls and 5.22 for boys. Of the studies analyzed, one proposed external validity, recommending the use of the HOMA-IR cutoff >2.5 for both genders. The HOMA-IR index constitutes a reliable method for the detection of insulin resistance in adolescents, as long as it uses cutoffs that are more adequate for the reality of the study population, allowing early diagnosis of insulin resistance and enabling multidisciplinary interventions aiming at health promotion of this population. Copyright © 2015 Sociedade de Pediatria de São Paulo. Publicado por Elsevier Editora Ltda. All rights reserved.
Regression analysis of a chemical reaction fouling model
International Nuclear Information System (INIS)
Vasak, F.; Epstein, N.
1996-01-01
A previously reported mathematical model for the initial chemical reaction fouling of a heated tube is critically examined in the light of the experimental data for which it was developed. A regression analysis of the model with respect to that data shows that the reference point upon which the two adjustable parameters of the model were originally based was well chosen, albeit fortuitously. (author). 3 refs., 2 tabs., 2 figs
Spatial stochastic regression modelling of urban land use
International Nuclear Information System (INIS)
Arshad, S H M; Jaafar, J; Abiden, M Z Z; Latif, Z A; Rasam, A R A
2014-01-01
Urbanization is very closely linked to industrialization, commercialization or overall economic growth and development. This results in innumerable benefits of the quantity and quality of the urban environment and lifestyle but on the other hand contributes to unbounded development, urban sprawl, overcrowding and decreasing standard of living. Regulation and observation of urban development activities is crucial. The understanding of urban systems that promotes urban growth are also essential for the purpose of policy making, formulating development strategies as well as development plan preparation. This study aims to compare two different stochastic regression modeling techniques for spatial structure models of urban growth in the same specific study area. Both techniques will utilize the same datasets and their results will be analyzed. The work starts by producing an urban growth model by using stochastic regression modeling techniques namely the Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR). The two techniques are compared to and it is found that, GWR seems to be a more significant stochastic regression model compared to OLS, it gives a smaller AICc (Akaike's Information Corrected Criterion) value and its output is more spatially explainable
Direction of Effects in Multiple Linear Regression Models.
Wiedermann, Wolfgang; von Eye, Alexander
2015-01-01
Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D'Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed.
Logistic regression for risk factor modelling in stuttering research.
Reed, Phil; Wu, Yaqionq
2013-06-01
To outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering. The principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed. The way the statistical procedures are employed are demonstrated with some hypothetical data. Research into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome. After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression. Copyright © 2012 Elsevier Inc. All rights reserved.
Modeling and prediction of flotation performance using support vector regression
Directory of Open Access Journals (Sweden)
Despotović Vladimir
2017-01-01
Full Text Available Continuous efforts have been made in recent year to improve the process of paper recycling, as it is of critical importance for saving the wood, water and energy resources. Flotation deinking is considered to be one of the key methods for separation of ink particles from the cellulose fibres. Attempts to model the flotation deinking process have often resulted in complex models that are difficult to implement and use. In this paper a model for prediction of flotation performance based on Support Vector Regression (SVR, is presented. Representative data samples were created in laboratory, under a variety of practical control variables for the flotation deinking process, including different reagents, pH values and flotation residence time. Predictive model was created that was trained on these data samples, and the flotation performance was assessed showing that Support Vector Regression is a promising method even when dataset used for training the model is limited.
Bayesian approach to errors-in-variables in regression models
Rozliman, Nur Aainaa; Ibrahim, Adriana Irawati Nur; Yunus, Rossita Mohammad
2017-05-01
In many applications and experiments, data sets are often contaminated with error or mismeasured covariates. When at least one of the covariates in a model is measured with error, Errors-in-Variables (EIV) model can be used. Measurement error, when not corrected, would cause misleading statistical inferences and analysis. Therefore, our goal is to examine the relationship of the outcome variable and the unobserved exposure variable given the observed mismeasured surrogate by applying the Bayesian formulation to the EIV model. We shall extend the flexible parametric method proposed by Hossain and Gustafson (2009) to another nonlinear regression model which is the Poisson regression model. We shall then illustrate the application of this approach via a simulation study using Markov chain Monte Carlo sampling methods.
Time series regression model for infectious disease and weather.
Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro
2015-10-01
Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
Variable Selection for Regression Models of Percentile Flows
Fouad, G.
2017-12-01
Percentile flows describe the flow magnitude equaled or exceeded for a given percent of time, and are widely used in water resource management. However, these statistics are normally unavailable since most basins are ungauged. Percentile flows of ungauged basins are often predicted using regression models based on readily observable basin characteristics, such as mean elevation. The number of these independent variables is too large to evaluate all possible models. A subset of models is typically evaluated using automatic procedures, like stepwise regression. This ignores a large variety of methods from the field of feature (variable) selection and physical understanding of percentile flows. A study of 918 basins in the United States was conducted to compare an automatic regression procedure to the following variable selection methods: (1) principal component analysis, (2) correlation analysis, (3) random forests, (4) genetic programming, (5) Bayesian networks, and (6) physical understanding. The automatic regression procedure only performed better than principal component analysis. Poor performance of the regression procedure was due to a commonly used filter for multicollinearity, which rejected the strongest models because they had cross-correlated independent variables. Multicollinearity did not decrease model performance in validation because of a representative set of calibration basins. Variable selection methods based strictly on predictive power (numbers 2-5 from above) performed similarly, likely indicating a limit to the predictive power of the variables. Similar performance was also reached using variables selected based on physical understanding, a finding that substantiates recent calls to emphasize physical understanding in modeling for predictions in ungauged basins. The strongest variables highlighted the importance of geology and land cover, whereas widely used topographic variables were the weakest predictors. Variables suffered from a high
Linearity and Misspecification Tests for Vector Smooth Transition Regression Models
DEFF Research Database (Denmark)
Teräsvirta, Timo; Yang, Yukai
The purpose of the paper is to derive Lagrange multiplier and Lagrange multiplier type specification and misspecification tests for vector smooth transition regression models. We report results from simulation studies in which the size and power properties of the proposed asymptotic tests in small...
Application of multilinear regression analysis in modeling of soil ...
African Journals Online (AJOL)
The application of Multi-Linear Regression Analysis (MLRA) model for predicting soil properties in Calabar South offers a technical guide and solution in foundation designs problems in the area. Forty-five soil samples were collected from fifteen different boreholes at a different depth and 270 tests were carried out for CBR, ...
Efficient estimation of an additive quantile regression model
Cheng, Y.; de Gooijer, J.G.; Zerom, D.
2009-01-01
In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). By
Efficient estimation of an additive quantile regression model
Cheng, Y.; de Gooijer, J.G.; Zerom, D.
2010-01-01
In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). By
Efficient estimation of an additive quantile regression model
Cheng, Y.; de Gooijer, J.G.; Zerom, D.
2011-01-01
In this paper, two non-parametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a more viable alternative to existing kernel-based approaches. The second estimator
A binary logistic regression model with complex sampling design of ...
African Journals Online (AJOL)
2017-09-03
Sep 3, 2017 ... Bi-variable and multi-variable binary logistic regression model with complex sampling design was fitted. .... Data was entered into STATA-12 and analyzed using. SPSS-21. .... lack of access/too far or costs too much. 35. 1.2.
Transpiration of glasshouse rose crops: evaluation of regression models
Baas, R.; Rijssel, van E.
2006-01-01
Regression models of transpiration (T) based on global radiation inside the greenhouse (G), with or without energy input from heating pipes (Eh) and/or vapor pressure deficit (VPD) were parameterized. Therefore, data on T, G, temperatures from air, canopy and heating pipes, and VPD from both a
Approximating prediction uncertainty for random forest regression models
John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne
2016-01-01
Machine learning approaches such as random forest haveÂ increased for the spatial modeling and mapping of continuousÂ variables. Random forest is a non-parametric ensembleÂ approach, and unlike traditional regression approaches thereÂ is no direct quantification of prediction error. UnderstandingÂ prediction uncertainty is important when using model-basedÂ continuous maps as...
CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model
DEFF Research Database (Denmark)
Dyrholm, Mads; Hansen, Lars Kai
2004-01-01
We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least square...... estimation. We demonstrate the method on synthetic data and finally separate speech and music in a real room recording....
On concurvity in nonlinear and nonparametric regression models
Directory of Open Access Journals (Sweden)
Sonia Amodio
2014-12-01
Full Text Available When data are affected by multicollinearity in the linear regression framework, then concurvity will be present in fitting a generalized additive model (GAM. The term concurvity describes nonlinear dependencies among the predictor variables. As collinearity results in inflated variance of the estimated regression coefficients in the linear regression model, the result of the presence of concurvity leads to instability of the estimated coefficients in GAMs. Even if the backfitting algorithm will always converge to a solution, in case of concurvity the final solution of the backfitting procedure in fitting a GAM is influenced by the starting functions. While exact concurvity is highly unlikely, approximate concurvity, the analogue of multicollinearity, is of practical concern as it can lead to upwardly biased estimates of the parameters and to underestimation of their standard errors, increasing the risk of committing type I error. We compare the existing approaches to detect concurvity, pointing out their advantages and drawbacks, using simulated and real data sets. As a result, this paper will provide a general criterion to detect concurvity in nonlinear and non parametric regression models.
Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate
Directory of Open Access Journals (Sweden)
Minh Vu Trieu
2017-03-01
Full Text Available This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS, Brazilian tensile strength (BTS, rock brittleness index (BI, the distance between planes of weakness (DPW, and the alpha angle (Alpha between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP. Four (4 statistical regression models (two linear and two nonlinear are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2 of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.
Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate
Minh, Vu Trieu; Katushin, Dmitri; Antonov, Maksim; Veinthal, Renno
2017-03-01
This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW), and the alpha angle (Alpha) between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP). Four (4) statistical regression models (two linear and two nonlinear) are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2) of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.
Detection of Outliers in Regression Model for Medical Data
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Stephen Raj S
2017-07-01
Full Text Available In regression analysis, an outlier is an observation for which the residual is large in magnitude compared to other observations in the data set. The detection of outliers and influential points is an important step of the regression analysis. Outlier detection methods have been used to detect and remove anomalous values from data. In this paper, we detect the presence of outliers in simple linear regression models for medical data set. Chatterjee and Hadi mentioned that the ordinary residuals are not appropriate for diagnostic purposes; a transformed version of them is preferable. First, we investigate the presence of outliers based on existing procedures of residuals and standardized residuals. Next, we have used the new approach of standardized scores for detecting outliers without the use of predicted values. The performance of the new approach was verified with the real-life data.
Hierarchical Neural Regression Models for Customer Churn Prediction
Directory of Open Access Journals (Sweden)
Golshan Mohammadi
2013-01-01
Full Text Available As customers are the main assets of each industry, customer churn prediction is becoming a major task for companies to remain in competition with competitors. In the literature, the better applicability and efficiency of hierarchical data mining techniques has been reported. This paper considers three hierarchical models by combining four different data mining techniques for churn prediction, which are backpropagation artificial neural networks (ANN, self-organizing maps (SOM, alpha-cut fuzzy c-means (α-FCM, and Cox proportional hazards regression model. The hierarchical models are ANN + ANN + Cox, SOM + ANN + Cox, and α-FCM + ANN + Cox. In particular, the first component of the models aims to cluster data in two churner and nonchurner groups and also filter out unrepresentative data or outliers. Then, the clustered data as the outputs are used to assign customers to churner and nonchurner groups by the second technique. Finally, the correctly classified data are used to create Cox proportional hazards model. To evaluate the performance of the hierarchical models, an Iranian mobile dataset is considered. The experimental results show that the hierarchical models outperform the single Cox regression baseline model in terms of prediction accuracy, Types I and II errors, RMSE, and MAD metrics. In addition, the α-FCM + ANN + Cox model significantly performs better than the two other hierarchical models.
Directory of Open Access Journals (Sweden)
Luiz Gustavo de Oliveira e Silva
2012-12-01
Full Text Available RACIONAL: A vitamina A participa de várias funções primordiais no organismo humano e as suas concentrações séricas podem estar diminuídas nas doenças crônicas não transmissíveis. OBJETIVO: Avaliar a relação entre o estado nutricional da vitamina A, e a regressão da esteatose hepática em indivíduos submetidos à gastroplastia em Y-de-Roux para tratamento da obesidade classe III. MÉTODOS: Foram estudados 30 pacientes obesos classe III, de ambos os sexos, com esteatose hepática, submetidos à gastroplastia em Y-de-Roux. Seis meses após a operação, os pacientes foram submetidos à ultrassonografia abdominal e distribuídos em dois grupos: grupo 1 - pacientes com esteatose detectada na ultrassonografia e grupo 2 - pacientes sem esteatose detectada na ultrassonografia. No pré-operatório e seis meses após a operação foram realizadas análises antropométricas e exames bioquímicos: insulina basal, glicemia, Homeostasis Model Assessment Index (HOMA IR, colesterol, HDL, LDL, triglicerídeos, AST, ALT, Gama-GT, albumina, bilirrubina total, retinol, e beta caroteno. RESULTADOS: A média de perda de peso foi de 35,05 + 10,47 (pBACKGROUND: Vitamin A participates in several essentials functions in the human body and their serum concentrations may be decreased in non-transmissible diseases. AIM: To assess the relationship of the nutritional status of Vitamin A through the serum concentrations of retinol and beta carotene, with regression of hepatic steatosis in individuals who undergone Roux-en-Y gastric bypass surgery for treatment of class III obesity. METHODS: Were included 30 individuals, male and female, submitted to Roux-en-Y gastric bypass for treatment of class III obesity, who were diagnosed through an abdominal ultrasonography as presenting hepatic steatosis. From the result of an ultrasonography screened six months after the surgical procedure those subjects were divided into two groups: group 1 - patients with steatosis
Electricity consumption forecasting in Italy using linear regression models
Energy Technology Data Exchange (ETDEWEB)
Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio [DIAM, Seconda Universita degli Studi di Napoli, Via Roma 29, 81031 Aversa (CE) (Italy)
2009-09-15
The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to -0.06, while long run elasticities are equal to -0.24 and -0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values. In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models. A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of {+-}1% for the best case and {+-}11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account. (author)
Electricity consumption forecasting in Italy using linear regression models
International Nuclear Information System (INIS)
Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio
2009-01-01
The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to -0.06, while long run elasticities are equal to -0.24 and -0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values. In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models. A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of ±1% for the best case and ±11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account. (author)
Regression Model to Predict Global Solar Irradiance in Malaysia
Directory of Open Access Journals (Sweden)
Hairuniza Ahmed Kutty
2015-01-01
Full Text Available A novel regression model is developed to estimate the monthly global solar irradiance in Malaysia. The model is developed based on different available meteorological parameters, including temperature, cloud cover, rain precipitate, relative humidity, wind speed, pressure, and gust speed, by implementing regression analysis. This paper reports on the details of the analysis of the effect of each prediction parameter to identify the parameters that are relevant to estimating global solar irradiance. In addition, the proposed model is compared in terms of the root mean square error (RMSE, mean bias error (MBE, and the coefficient of determination (R2 with other models available from literature studies. Seven models based on single parameters (PM1 to PM7 and five multiple-parameter models (PM7 to PM12 are proposed. The new models perform well, with RMSE ranging from 0.429% to 1.774%, R2 ranging from 0.942 to 0.992, and MBE ranging from −0.1571% to 0.6025%. In general, cloud cover significantly affects the estimation of global solar irradiance. However, cloud cover in Malaysia lacks sufficient influence when included into multiple-parameter models although it performs fairly well in single-parameter prediction models.
Directory of Open Access Journals (Sweden)
Eun-Jung Rhee
2011-04-01
Full Text Available BackgroundWe performed a retrospective longitudinal study on the effects of changes in weight, body composition, and homeostasis model assessment (HOMA indices on glycemic progression in subjects without diabetes during a four-year follow-up period in a community cohort without intentional intervention.MethodsFrom 28,440 non-diabetic subjects who participated in a medical check-up program in 2004, data on anthropometric and metabolic parameters were obtained after four years in 2008. Body composition analyses were performed with a bioelectrical impedance analyzer. Skeletal muscle index (SMI, % was calculated with lean mass/weight×100. Subjects were divided into three groups according to weight change status in four years: weight loss (≤-5.0%, stable weight (-5.0 to 5.0%, weight gain (≥5.0%. Progressors were defined as the subjects who progressed to impaired fasting glucose or diabetes.ResultsProgressors showed worse baseline metabolic profiles compared with non-progressors. In logistic regression analyses, the increase in changes of HOMA-insulin resistance (HOMA-IR in four years presented higher odds ratios for glycemic progression compared with other changes during that period. Among the components of body composition, a change in waist-hip ratio was the strongest predictor, and SMI change in four years was a significant negative predictor for glycemic progression. Changes in HOMA β-cell function in four years was a negative predictor for glycemic progression.ConclusionIncreased interval changes in HOMA-IR, weight gain and waist-hip ratio was associated with glycemic progression during a four-year period without intentional intervention in non-diabetic Korean subjects.
Two-step variable selection in quantile regression models
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FAN Yali
2015-06-01
Full Text Available We propose a two-step variable selection procedure for high dimensional quantile regressions, in which the dimension of the covariates, pn is much larger than the sample size n. In the first step, we perform ℓ1 penalty, and we demonstrate that the first step penalized estimator with the LASSO penalty can reduce the model from an ultra-high dimensional to a model whose size has the same order as that of the true model, and the selected model can cover the true model. The second step excludes the remained irrelevant covariates by applying the adaptive LASSO penalty to the reduced model obtained from the first step. Under some regularity conditions, we show that our procedure enjoys the model selection consistency. We conduct a simulation study and a real data analysis to evaluate the finite sample performance of the proposed approach.
New robust statistical procedures for the polytomous logistic regression models.
Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro
2018-05-17
This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.
THE REGRESSION MODEL OF IRAN LIBRARIES ORGANIZATIONAL CLIMATE
Jahani, Mohammad Ali; Yaminfirooz, Mousa; Siamian, Hasan
2015-01-01
Background: The purpose of this study was to drawing a regression model of organizational climate of central libraries of Iran?s universities. Methods: This study is an applied research. The statistical population of this study consisted of 96 employees of the central libraries of Iran?s public universities selected among the 117 universities affiliated to the Ministry of Health by Stratified Sampling method (510 people). Climate Qual localized questionnaire was used as research tools. For pr...
Online Statistical Modeling (Regression Analysis) for Independent Responses
Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus
2017-06-01
Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.
Reconstruction of missing daily streamflow data using dynamic regression models
Tencaliec, Patricia; Favre, Anne-Catherine; Prieur, Clémentine; Mathevet, Thibault
2015-12-01
River discharge is one of the most important quantities in hydrology. It provides fundamental records for water resources management and climate change monitoring. Even very short data-gaps in this information can cause extremely different analysis outputs. Therefore, reconstructing missing data of incomplete data sets is an important step regarding the performance of the environmental models, engineering, and research applications, thus it presents a great challenge. The objective of this paper is to introduce an effective technique for reconstructing missing daily discharge data when one has access to only daily streamflow data. The proposed procedure uses a combination of regression and autoregressive integrated moving average models (ARIMA) called dynamic regression model. This model uses the linear relationship between neighbor and correlated stations and then adjusts the residual term by fitting an ARIMA structure. Application of the model to eight daily streamflow data for the Durance river watershed showed that the model yields reliable estimates for the missing data in the time series. Simulation studies were also conducted to evaluate the performance of the procedure.
Predicting and Modelling of Survival Data when Cox's Regression Model does not hold
DEFF Research Database (Denmark)
Scheike, Thomas H.; Zhang, Mei-Jie
2002-01-01
Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects...
Extended cox regression model: The choice of timefunction
Isik, Hatice; Tutkun, Nihal Ata; Karasoy, Durdu
2017-07-01
Cox regression model (CRM), which takes into account the effect of censored observations, is one the most applicative and usedmodels in survival analysis to evaluate the effects of covariates. Proportional hazard (PH), requires a constant hazard ratio over time, is the assumptionofCRM. Using extended CRM provides the test of including a time dependent covariate to assess the PH assumption or an alternative model in case of nonproportional hazards. In this study, the different types of real data sets are used to choose the time function and the differences between time functions are analyzed and discussed.
A test of inflated zeros for Poisson regression models.
He, Hua; Zhang, Hui; Ye, Peng; Tang, Wan
2017-01-01
Excessive zeros are common in practice and may cause overdispersion and invalidate inference when fitting Poisson regression models. There is a large body of literature on zero-inflated Poisson models. However, methods for testing whether there are excessive zeros are less well developed. The Vuong test comparing a Poisson and a zero-inflated Poisson model is commonly applied in practice. However, the type I error of the test often deviates seriously from the nominal level, rendering serious doubts on the validity of the test in such applications. In this paper, we develop a new approach for testing inflated zeros under the Poisson model. Unlike the Vuong test for inflated zeros, our method does not require a zero-inflated Poisson model to perform the test. Simulation studies show that when compared with the Vuong test our approach not only better at controlling type I error rate, but also yield more power.
Multivariate Frequency-Severity Regression Models in Insurance
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Edward W. Frees
2016-02-01
Full Text Available In insurance and related industries including healthcare, it is common to have several outcome measures that the analyst wishes to understand using explanatory variables. For example, in automobile insurance, an accident may result in payments for damage to one’s own vehicle, damage to another party’s vehicle, or personal injury. It is also common to be interested in the frequency of accidents in addition to the severity of the claim amounts. This paper synthesizes and extends the literature on multivariate frequency-severity regression modeling with a focus on insurance industry applications. Regression models for understanding the distribution of each outcome continue to be developed yet there now exists a solid body of literature for the marginal outcomes. This paper contributes to this body of literature by focusing on the use of a copula for modeling the dependence among these outcomes; a major advantage of this tool is that it preserves the body of work established for marginal models. We illustrate this approach using data from the Wisconsin Local Government Property Insurance Fund. This fund offers insurance protection for (i property; (ii motor vehicle; and (iii contractors’ equipment claims. In addition to several claim types and frequency-severity components, outcomes can be further categorized by time and space, requiring complex dependency modeling. We find significant dependencies for these data; specifically, we find that dependencies among lines are stronger than the dependencies between the frequency and average severity within each line.
Augmented Beta rectangular regression models: A Bayesian perspective.
Wang, Jue; Luo, Sheng
2016-01-01
Mixed effects Beta regression models based on Beta distributions have been widely used to analyze longitudinal percentage or proportional data ranging between zero and one. However, Beta distributions are not flexible to extreme outliers or excessive events around tail areas, and they do not account for the presence of the boundary values zeros and ones because these values are not in the support of the Beta distributions. To address these issues, we propose a mixed effects model using Beta rectangular distribution and augment it with the probabilities of zero and one. We conduct extensive simulation studies to assess the performance of mixed effects models based on both the Beta and Beta rectangular distributions under various scenarios. The simulation studies suggest that the regression models based on Beta rectangular distributions improve the accuracy of parameter estimates in the presence of outliers and heavy tails. The proposed models are applied to the motivating Neuroprotection Exploratory Trials in Parkinson's Disease (PD) Long-term Study-1 (LS-1 study, n = 1741), developed by The National Institute of Neurological Disorders and Stroke Exploratory Trials in Parkinson's Disease (NINDS NET-PD) network. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Bayesian semiparametric regression models to characterize molecular evolution
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Datta Saheli
2012-10-01
Full Text Available Abstract Background Statistical models and methods that associate changes in the physicochemical properties of amino acids with natural selection at the molecular level typically do not take into account the correlations between such properties. We propose a Bayesian hierarchical regression model with a generalization of the Dirichlet process prior on the distribution of the regression coefficients that describes the relationship between the changes in amino acid distances and natural selection in protein-coding DNA sequence alignments. Results The Bayesian semiparametric approach is illustrated with simulated data and the abalone lysin sperm data. Our method identifies groups of properties which, for this particular dataset, have a similar effect on evolution. The model also provides nonparametric site-specific estimates for the strength of conservation of these properties. Conclusions The model described here is distinguished by its ability to handle a large number of amino acid properties simultaneously, while taking into account that such data can be correlated. The multi-level clustering ability of the model allows for appealing interpretations of the results in terms of properties that are roughly equivalent from the standpoint of molecular evolution.
Regularized multivariate regression models with skew-t error distributions
Chen, Lianfu
2014-06-01
We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both the regression coefficient and inverse scale matrices simultaneously. The sparsity is introduced through penalizing the negative log-likelihood by adding L1-penalties on the entries of the two matrices. Taking advantage of the hierarchical representation of skew-t distributions, and using the expectation conditional maximization (ECM) algorithm, we reduce the problem to penalized normal likelihood and develop a procedure to minimize the ensuing objective function. Using a simulation study the performance of the method is assessed, and the methodology is illustrated using a real data set with a 24-dimensional response vector. © 2014 Elsevier B.V.
Modeling the number of car theft using Poisson regression
Zulkifli, Malina; Ling, Agnes Beh Yen; Kasim, Maznah Mat; Ismail, Noriszura
2016-10-01
Regression analysis is the most popular statistical methods used to express the relationship between the variables of response with the covariates. The aim of this paper is to evaluate the factors that influence the number of car theft using Poisson regression model. This paper will focus on the number of car thefts that occurred in districts in Peninsular Malaysia. There are two groups of factor that have been considered, namely district descriptive factors and socio and demographic factors. The result of the study showed that Bumiputera composition, Chinese composition, Other ethnic composition, foreign migration, number of residence with the age between 25 to 64, number of employed person and number of unemployed person are the most influence factors that affect the car theft cases. These information are very useful for the law enforcement department, insurance company and car owners in order to reduce and limiting the car theft cases in Peninsular Malaysia.
Dynamic Regression Intervention Modeling for the Malaysian Daily Load
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Fadhilah Abdrazak
2014-05-01
Full Text Available Malaysia is a unique country due to having both fixed and moving holidays. These moving holidays may overlap with other fixed holidays and therefore, increase the complexity of the load forecasting activities. The errors due to holidays’ effects in the load forecasting are known to be higher than other factors. If these effects can be estimated and removed, the behavior of the series could be better viewed. Thus, the aim of this paper is to improve the forecasting errors by using a dynamic regression model with intervention analysis. Based on the linear transfer function method, a daily load model consists of either peak or average is developed. The developed model outperformed the seasonal ARIMA model in estimating the fixed and moving holidays’ effects and achieved a smaller Mean Absolute Percentage Error (MAPE in load forecast.
Learning Supervised Topic Models for Classification and Regression from Crowds.
Rodrigues, Filipe; Lourenco, Mariana; Ribeiro, Bernardete; Pereira, Francisco C
2017-12-01
The growing need to analyze large collections of documents has led to great developments in topic modeling. Since documents are frequently associated with other related variables, such as labels or ratings, much interest has been placed on supervised topic models. However, the nature of most annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this article, we propose two supervised topic models, one for classification and another for regression problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages of the proposed model over state-of-the-art approaches.
Continuous validation of ASTEC containment models and regression testing
International Nuclear Information System (INIS)
Nowack, Holger; Reinke, Nils; Sonnenkalb, Martin
2014-01-01
The focus of the ASTEC (Accident Source Term Evaluation Code) development at GRS is primarily on the containment module CPA (Containment Part of ASTEC), whose modelling is to a large extent based on the GRS containment code COCOSYS (COntainment COde SYStem). Validation is usually understood as the approval of the modelling capabilities by calculations of appropriate experiments done by external users different from the code developers. During the development process of ASTEC CPA, bugs and unintended side effects may occur, which leads to changes in the results of the initially conducted validation. Due to the involvement of a considerable number of developers in the coding of ASTEC modules, validation of the code alone, even if executed repeatedly, is not sufficient. Therefore, a regression testing procedure has been implemented in order to ensure that the initially obtained validation results are still valid with succeeding code versions. Within the regression testing procedure, calculations of experiments and plant sequences are performed with the same input deck but applying two different code versions. For every test-case the up-to-date code version is compared to the preceding one on the basis of physical parameters deemed to be characteristic for the test-case under consideration. In the case of post-calculations of experiments also a comparison to experimental data is carried out. Three validation cases from the regression testing procedure are presented within this paper. The very good post-calculation of the HDR E11.1 experiment shows the high quality modelling of thermal-hydraulics in ASTEC CPA. Aerosol behaviour is validated on the BMC VANAM M3 experiment, and the results show also a very good agreement with experimental data. Finally, iodine behaviour is checked in the validation test-case of the THAI IOD-11 experiment. Within this test-case, the comparison of the ASTEC versions V2.0r1 and V2.0r2 shows how an error was detected by the regression testing
Modeling of the Monthly Rainfall-Runoff Process Through Regressions
Directory of Open Access Journals (Sweden)
Campos-Aranda Daniel Francisco
2014-10-01
Full Text Available To solve the problems associated with the assessment of water resources of a river, the modeling of the rainfall-runoff process (RRP allows the deduction of runoff missing data and to extend its record, since generally the information available on precipitation is larger. It also enables the estimation of inputs to reservoirs, when their building led to the suppression of the gauging station. The simplest mathematical model that can be set for the RRP is the linear regression or curve on a monthly basis. Such a model is described in detail and is calibrated with the simultaneous record of monthly rainfall and runoff in Ballesmi hydrometric station, which covers 35 years. Since the runoff of this station has an important contribution from the spring discharge, the record is corrected first by removing that contribution. In order to do this a procedure was developed based either on the monthly average regional runoff coefficients or on nearby and similar watershed; in this case the Tancuilín gauging station was used. Both stations belong to the Partial Hydrologic Region No. 26 (Lower Rio Panuco and are located within the state of San Luis Potosi, México. The study performed indicates that the monthly regression model, due to its conceptual approach, faithfully reproduces monthly average runoff volumes and achieves an excellent approximation in relation to the dispersion, proved by calculation of the means and standard deviations.
Genetic evaluation of European quails by random regression models
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Flaviana Miranda Gonçalves
2012-09-01
Full Text Available The objective of this study was to compare different random regression models, defined from different classes of heterogeneity of variance combined with different Legendre polynomial orders for the estimate of (covariance of quails. The data came from 28,076 observations of 4,507 female meat quails of the LF1 lineage. Quail body weights were determined at birth and 1, 14, 21, 28, 35 and 42 days of age. Six different classes of residual variance were fitted to Legendre polynomial functions (orders ranging from 2 to 6 to determine which model had the best fit to describe the (covariance structures as a function of time. According to the evaluated criteria (AIC, BIC and LRT, the model with six classes of residual variances and of sixth-order Legendre polynomial was the best fit. The estimated additive genetic variance increased from birth to 28 days of age, and dropped slightly from 35 to 42 days. The heritability estimates decreased along the growth curve and changed from 0.51 (1 day to 0.16 (42 days. Animal genetic and permanent environmental correlation estimates between weights and age classes were always high and positive, except for birth weight. The sixth order Legendre polynomial, along with the residual variance divided into six classes was the best fit for the growth rate curve of meat quails; therefore, they should be considered for breeding evaluation processes by random regression models.
Interpreting parameters in the logistic regression model with random effects
DEFF Research Database (Denmark)
Larsen, Klaus; Petersen, Jørgen Holm; Budtz-Jørgensen, Esben
2000-01-01
interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects......interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects...
Learning Supervised Topic Models for Classification and Regression from Crowds
DEFF Research Database (Denmark)
Rodrigues, Filipe; Lourenco, Mariana; Ribeiro, Bernardete
2017-01-01
problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages...... annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this article, we propose two supervised topic models, one for classification and another for regression...
Preference learning with evolutionary Multivariate Adaptive Regression Spline model
DEFF Research Database (Denmark)
Abou-Zleikha, Mohamed; Shaker, Noor; Christensen, Mads Græsbøll
2015-01-01
This paper introduces a novel approach for pairwise preference learning through combining an evolutionary method with Multivariate Adaptive Regression Spline (MARS). Collecting users' feedback through pairwise preferences is recommended over other ranking approaches as this method is more appealing...... for function approximation as well as being relatively easy to interpret. MARS models are evolved based on their efficiency in learning pairwise data. The method is tested on two datasets that collectively provide pairwise preference data of five cognitive states expressed by users. The method is analysed...
Predicting Performance on MOOC Assessments using Multi-Regression Models
Ren, Zhiyun; Rangwala, Huzefa; Johri, Aditya
2016-01-01
The past few years has seen the rapid growth of data min- ing approaches for the analysis of data obtained from Mas- sive Open Online Courses (MOOCs). The objectives of this study are to develop approaches to predict the scores a stu- dent may achieve on a given grade-related assessment based on information, considered as prior performance or prior ac- tivity in the course. We develop a personalized linear mul- tiple regression (PLMR) model to predict the grade for a student, prior to attempt...
Analytical and regression models of glass rod drawing process
Alekseeva, L. B.
2018-03-01
The process of drawing glass rods (light guides) is being studied. The parameters of the process affecting the quality of the light guide have been determined. To solve the problem, mathematical models based on general equations of continuum mechanics are used. The conditions for the stable flow of the drawing process have been found, which are determined by the stability of the motion of the glass mass in the formation zone to small uncontrolled perturbations. The sensitivity of the formation zone to perturbations of the drawing speed and viscosity is estimated. Experimental models of the drawing process, based on the regression analysis methods, have been obtained. These models make it possible to customize a specific production process to obtain light guides of the required quality. They allow one to find the optimum combination of process parameters in the chosen area and to determine the required accuracy of maintaining them at a specified level.
Regression Models for Predicting Force Coefficients of Aerofoils
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Mohammed ABDUL AKBAR
2015-09-01
Full Text Available Renewable sources of energy are attractive and advantageous in a lot of different ways. Among the renewable energy sources, wind energy is the fastest growing type. Among wind energy converters, Vertical axis wind turbines (VAWTs have received renewed interest in the past decade due to some of the advantages they possess over their horizontal axis counterparts. VAWTs have evolved into complex 3-D shapes. A key component in predicting the output of VAWTs through analytical studies is obtaining the values of lift and drag coefficients which is a function of shape of the aerofoil, ‘angle of attack’ of wind and Reynolds’s number of flow. Sandia National Laboratories have carried out extensive experiments on aerofoils for the Reynolds number in the range of those experienced by VAWTs. The volume of experimental data thus obtained is huge. The current paper discusses three Regression analysis models developed wherein lift and drag coefficients can be found out using simple formula without having to deal with the bulk of the data. Drag coefficients and Lift coefficients were being successfully estimated by regression models with R2 values as high as 0.98.
Complex Environmental Data Modelling Using Adaptive General Regression Neural Networks
Kanevski, Mikhail
2015-04-01
The research deals with an adaptation and application of Adaptive General Regression Neural Networks (GRNN) to high dimensional environmental data. GRNN [1,2,3] are efficient modelling tools both for spatial and temporal data and are based on nonparametric kernel methods closely related to classical Nadaraya-Watson estimator. Adaptive GRNN, using anisotropic kernels, can be also applied for features selection tasks when working with high dimensional data [1,3]. In the present research Adaptive GRNN are used to study geospatial data predictability and relevant feature selection using both simulated and real data case studies. The original raw data were either three dimensional monthly precipitation data or monthly wind speeds embedded into 13 dimensional space constructed by geographical coordinates and geo-features calculated from digital elevation model. GRNN were applied in two different ways: 1) adaptive GRNN with the resulting list of features ordered according to their relevancy; and 2) adaptive GRNN applied to evaluate all possible models N [in case of wind fields N=(2^13 -1)=8191] and rank them according to the cross-validation error. In both cases training were carried out applying leave-one-out procedure. An important result of the study is that the set of the most relevant features depends on the month (strong seasonal effect) and year. The predictabilities of precipitation and wind field patterns, estimated using the cross-validation and testing errors of raw and shuffled data, were studied in detail. The results of both approaches were qualitatively and quantitatively compared. In conclusion, Adaptive GRNN with their ability to select features and efficient modelling of complex high dimensional data can be widely used in automatic/on-line mapping and as an integrated part of environmental decision support systems. 1. Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning for Spatial Environmental Data. Theory, applications and software. EPFL Press
Conditional Monte Carlo randomization tests for regression models.
Parhat, Parwen; Rosenberger, William F; Diao, Guoqing
2014-08-15
We discuss the computation of randomization tests for clinical trials of two treatments when the primary outcome is based on a regression model. We begin by revisiting the seminal paper of Gail, Tan, and Piantadosi (1988), and then describe a method based on Monte Carlo generation of randomization sequences. The tests based on this Monte Carlo procedure are design based, in that they incorporate the particular randomization procedure used. We discuss permuted block designs, complete randomization, and biased coin designs. We also use a new technique by Plamadeala and Rosenberger (2012) for simple computation of conditional randomization tests. Like Gail, Tan, and Piantadosi, we focus on residuals from generalized linear models and martingale residuals from survival models. Such techniques do not apply to longitudinal data analysis, and we introduce a method for computation of randomization tests based on the predicted rate of change from a generalized linear mixed model when outcomes are longitudinal. We show, by simulation, that these randomization tests preserve the size and power well under model misspecification. Copyright © 2014 John Wiley & Sons, Ltd.
Genomic breeding value estimation using nonparametric additive regression models
Directory of Open Access Journals (Sweden)
Solberg Trygve
2009-01-01
Full Text Available Abstract Genomic selection refers to the use of genomewide dense markers for breeding value estimation and subsequently for selection. The main challenge of genomic breeding value estimation is the estimation of many effects from a limited number of observations. Bayesian methods have been proposed to successfully cope with these challenges. As an alternative class of models, non- and semiparametric models were recently introduced. The present study investigated the ability of nonparametric additive regression models to predict genomic breeding values. The genotypes were modelled for each marker or pair of flanking markers (i.e. the predictors separately. The nonparametric functions for the predictors were estimated simultaneously using additive model theory, applying a binomial kernel. The optimal degree of smoothing was determined by bootstrapping. A mutation-drift-balance simulation was carried out. The breeding values of the last generation (genotyped was predicted using data from the next last generation (genotyped and phenotyped. The results show moderate to high accuracies of the predicted breeding values. A determination of predictor specific degree of smoothing increased the accuracy.
Global Land Use Regression Model for Nitrogen Dioxide Air Pollution.
Larkin, Andrew; Geddes, Jeffrey A; Martin, Randall V; Xiao, Qingyang; Liu, Yang; Marshall, Julian D; Brauer, Michael; Hystad, Perry
2017-06-20
Nitrogen dioxide is a common air pollutant with growing evidence of health impacts independent of other common pollutants such as ozone and particulate matter. However, the worldwide distribution of NO 2 exposure and associated impacts on health is still largely uncertain. To advance global exposure estimates we created a global nitrogen dioxide (NO 2 ) land use regression model for 2011 using annual measurements from 5,220 air monitors in 58 countries. The model captured 54% of global NO 2 variation, with a mean absolute error of 3.7 ppb. Regional performance varied from R 2 = 0.42 (Africa) to 0.67 (South America). Repeated 10% cross-validation using bootstrap sampling (n = 10,000) demonstrated a robust performance with respect to air monitor sampling in North America, Europe, and Asia (adjusted R 2 within 2%) but not for Africa and Oceania (adjusted R 2 within 11%) where NO 2 monitoring data are sparse. The final model included 10 variables that captured both between and within-city spatial gradients in NO 2 concentrations. Variable contributions differed between continental regions, but major roads within 100 m and satellite-derived NO 2 were consistently the strongest predictors. The resulting model can be used for global risk assessments and health studies, particularly in countries without existing NO 2 monitoring data or models.
Drought Patterns Forecasting using an Auto-Regressive Logistic Model
del Jesus, M.; Sheffield, J.; Méndez Incera, F. J.; Losada, I. J.; Espejo, A.
2014-12-01
Drought is characterized by a water deficit that may manifest across a large range of spatial and temporal scales. Drought may create important socio-economic consequences, many times of catastrophic dimensions. A quantifiable definition of drought is elusive because depending on its impacts, consequences and generation mechanism, different water deficit periods may be identified as a drought by virtue of some definitions but not by others. Droughts are linked to the water cycle and, although a climate change signal may not have emerged yet, they are also intimately linked to climate.In this work we develop an auto-regressive logistic model for drought prediction at different temporal scales that makes use of a spatially explicit framework. Our model allows to include covariates, continuous or categorical, to improve the performance of the auto-regressive component.Our approach makes use of dimensionality reduction (principal component analysis) and classification techniques (K-Means and maximum dissimilarity) to simplify the representation of complex climatic patterns, such as sea surface temperature (SST) and sea level pressure (SLP), while including information on their spatial structure, i.e. considering their spatial patterns. This procedure allows us to include in the analysis multivariate representation of complex climatic phenomena, as the El Niño-Southern Oscillation. We also explore the impact of other climate-related variables such as sun spots. The model allows to quantify the uncertainty of the forecasts and can be easily adapted to make predictions under future climatic scenarios. The framework herein presented may be extended to other applications such as flash flood analysis, or risk assessment of natural hazards.
A Gompertz regression model for fern spores germination
Directory of Open Access Journals (Sweden)
Gabriel y Galán, Jose María
2015-06-01
Full Text Available Germination is one of the most important biological processes for both seed and spore plants, also for fungi. At present, mathematical models of germination have been developed in fungi, bryophytes and several plant species. However, ferns are the only group whose germination has never been modelled. In this work we develop a regression model of the germination of fern spores. We have found that for Blechnum serrulatum, Blechnum yungense, Cheilanthes pilosa, Niphidium macbridei and Polypodium feuillei species the Gompertz growth model describe satisfactorily cumulative germination. An important result is that regression parameters are independent of fern species and the model is not affected by intraspecific variation. Our results show that the Gompertz curve represents a general germination model for all the non-green spore leptosporangiate ferns, including in the paper a discussion about the physiological and ecological meaning of the model.La germinación es uno de los procesos biológicos más relevantes tanto para las plantas con esporas, como para las plantas con semillas y los hongos. Hasta el momento, se han desarrollado modelos de germinación para hongos, briofitos y diversas especies de espermatófitos. Los helechos son el único grupo de plantas cuya germinación nunca ha sido modelizada. En este trabajo se desarrolla un modelo de regresión para explicar la germinación de las esporas de helechos. Observamos que para las especies Blechnum serrulatum, Blechnum yungense, Cheilanthes pilosa, Niphidium macbridei y Polypodium feuillei el modelo de crecimiento de Gompertz describe satisfactoriamente la germinación acumulativa. Un importante resultado es que los parámetros de la regresión son independientes de la especie y que el modelo no está afectado por variación intraespecífica. Por lo tanto, los resultados del trabajo muestran que la curva de Gompertz puede representar un modelo general para todos los helechos leptosporangiados
Collision prediction models using multivariate Poisson-lognormal regression.
El-Basyouny, Karim; Sayed, Tarek
2009-07-01
This paper advocates the use of multivariate Poisson-lognormal (MVPLN) regression to develop models for collision count data. The MVPLN approach presents an opportunity to incorporate the correlations across collision severity levels and their influence on safety analyses. The paper introduces a new multivariate hazardous location identification technique, which generalizes the univariate posterior probability of excess that has been commonly proposed and applied in the literature. In addition, the paper presents an alternative approach for quantifying the effect of the multivariate structure on the precision of expected collision frequency. The MVPLN approach is compared with the independent (separate) univariate Poisson-lognormal (PLN) models with respect to model inference, goodness-of-fit, identification of hot spots and precision of expected collision frequency. The MVPLN is modeled using the WinBUGS platform which facilitates computation of posterior distributions as well as providing a goodness-of-fit measure for model comparisons. The results indicate that the estimates of the extra Poisson variation parameters were considerably smaller under MVPLN leading to higher precision. The improvement in precision is due mainly to the fact that MVPLN accounts for the correlation between the latent variables representing property damage only (PDO) and injuries plus fatalities (I+F). This correlation was estimated at 0.758, which is highly significant, suggesting that higher PDO rates are associated with higher I+F rates, as the collision likelihood for both types is likely to rise due to similar deficiencies in roadway design and/or other unobserved factors. In terms of goodness-of-fit, the MVPLN model provided a superior fit than the independent univariate models. The multivariate hazardous location identification results demonstrated that some hazardous locations could be overlooked if the analysis was restricted to the univariate models.
THE REGRESSION MODEL OF IRAN LIBRARIES ORGANIZATIONAL CLIMATE.
Jahani, Mohammad Ali; Yaminfirooz, Mousa; Siamian, Hasan
2015-10-01
The purpose of this study was to drawing a regression model of organizational climate of central libraries of Iran's universities. This study is an applied research. The statistical population of this study consisted of 96 employees of the central libraries of Iran's public universities selected among the 117 universities affiliated to the Ministry of Health by Stratified Sampling method (510 people). Climate Qual localized questionnaire was used as research tools. For predicting the organizational climate pattern of the libraries is used from the multivariate linear regression and track diagram. of the 9 variables affecting organizational climate, 5 variables of innovation, teamwork, customer service, psychological safety and deep diversity play a major role in prediction of the organizational climate of Iran's libraries. The results also indicate that each of these variables with different coefficient have the power to predict organizational climate but the climate score of psychological safety (0.94) plays a very crucial role in predicting the organizational climate. Track diagram showed that five variables of teamwork, customer service, psychological safety, deep diversity and innovation directly effects on the organizational climate variable that contribution of the team work from this influence is more than any other variables. Of the indicator of the organizational climate of climateQual, the contribution of the team work from this influence is more than any other variables that reinforcement of teamwork in academic libraries can be more effective in improving the organizational climate of this type libraries.
Makni, Emna; Moalla, Wassim; Lac, Gérard; Aouichaoui, Chirine; Cannon, Daniel; Elloumi, Mohamed; Tabka, Zouhair
2012-02-01
The aim of this study was to examine the efficacy of three indices i.e. adiponectin/leptin ratio, HOMA-IR and HOMA-AD in assessing insulin resistance among obese children. One hundred and twenty-two obese children (57 girls, 65 boys): mean age 13.7±1.3 years, BMI 30.1±4.5kg/m(2), eight tanner stage I, 48 tanner stage II-III, 66 tanner stage IV-V, participated in this study. They were classified into four groups according to sex and the presence of metabolic syndrome characteristics: with metabolic syndrome (MS; 21 girls and 36 boys) and controls without metabolic syndrome (CON, 36 girls and 29 boys). The correlations between these three indices of insulin resistance and the MS criteria were analyzed using linear and multiple regressions and receiver operating characteristics (ROC) curves analysis. The majority of anthropometric and biological parameters as well as adiponectin/leptin ratio, HOMA-IR and HOMA-AD were significantly different between MS and CON in both sexes. Both HOMA-AD and HOMA-IR were significantly correlated with the majority of metabolic syndrome components than was the adiponectin/leptin ratio in MS of both sexes. In boys and girls with and without MS, multiple regression analyses highlighted that both HOMA-AD and adiponectin/leptin ratio (r=-0.99 and r=-0.54 for MS girls and boys respectively, 0.05HOMA-AD and HOMA-IR (r=0.66 and r=0.31 for MS girls and boys respectively, 0.05HOMA-IR. Additionally, the area under the ROC curves for predicting insulin resistance were 0.69 (CI 95%, 0.60-0.77), 0.68 (CI 95%, 0.59-0.76) and 0.71 (CI 95%, 0.62-0.79) for adiponectin/leptin ratio, HOMA-IR and HOMA-AD, respectively. The current study strengthens the validity of the HOMA-AD as an adequate tool for determining insulin resistance among obese children with MS. Copyright Â© 2012. Published by Elsevier Masson SAS.
Meta-Modeling by Symbolic Regression and Pareto Simulated Annealing
Stinstra, E.; Rennen, G.; Teeuwen, G.J.A.
2006-01-01
The subject of this paper is a new approach to Symbolic Regression.Other publications on Symbolic Regression use Genetic Programming.This paper describes an alternative method based on Pareto Simulated Annealing.Our method is based on linear regression for the estimation of constants.Interval
Modeling Information Content Via Dirichlet-Multinomial Regression Analysis.
Ferrari, Alberto
2017-01-01
Shannon entropy is being increasingly used in biomedical research as an index of complexity and information content in sequences of symbols, e.g. languages, amino acid sequences, DNA methylation patterns and animal vocalizations. Yet, distributional properties of information entropy as a random variable have seldom been the object of study, leading to researchers mainly using linear models or simulation-based analytical approach to assess differences in information content, when entropy is measured repeatedly in different experimental conditions. Here a method to perform inference on entropy in such conditions is proposed. Building on results coming from studies in the field of Bayesian entropy estimation, a symmetric Dirichlet-multinomial regression model, able to deal efficiently with the issue of mean entropy estimation, is formulated. Through a simulation study the model is shown to outperform linear modeling in a vast range of scenarios and to have promising statistical properties. As a practical example, the method is applied to a data set coming from a real experiment on animal communication.
Variable selection in Logistic regression model with genetic algorithm.
Zhang, Zhongheng; Trevino, Victor; Hoseini, Sayed Shahabuddin; Belciug, Smaranda; Boopathi, Arumugam Manivanna; Zhang, Ping; Gorunescu, Florin; Subha, Velappan; Dai, Songshi
2018-02-01
Variable or feature selection is one of the most important steps in model specification. Especially in the case of medical-decision making, the direct use of a medical database, without a previous analysis and preprocessing step, is often counterproductive. In this way, the variable selection represents the method of choosing the most relevant attributes from the database in order to build a robust learning models and, thus, to improve the performance of the models used in the decision process. In biomedical research, the purpose of variable selection is to select clinically important and statistically significant variables, while excluding unrelated or noise variables. A variety of methods exist for variable selection, but none of them is without limitations. For example, the stepwise approach, which is highly used, adds the best variable in each cycle generally producing an acceptable set of variables. Nevertheless, it is limited by the fact that it commonly trapped in local optima. The best subset approach can systematically search the entire covariate pattern space, but the solution pool can be extremely large with tens to hundreds of variables, which is the case in nowadays clinical data. Genetic algorithms (GA) are heuristic optimization approaches and can be used for variable selection in multivariable regression models. This tutorial paper aims to provide a step-by-step approach to the use of GA in variable selection. The R code provided in the text can be extended and adapted to other data analysis needs.
Electricity prices forecasting by automatic dynamic harmonic regression models
International Nuclear Information System (INIS)
Pedregal, Diego J.; Trapero, Juan R.
2007-01-01
The changes experienced by electricity markets in recent years have created the necessity for more accurate forecast tools of electricity prices, both for producers and consumers. Many methodologies have been applied to this aim, but in the view of the authors, state space models are not yet fully exploited. The present paper proposes a univariate dynamic harmonic regression model set up in a state space framework for forecasting prices in these markets. The advantages of the approach are threefold. Firstly, a fast automatic identification and estimation procedure is proposed based on the frequency domain. Secondly, the recursive algorithms applied offer adaptive predictions that compare favourably with respect to other techniques. Finally, since the method is based on unobserved components models, explicit information about trend, seasonal and irregular behaviours of the series can be extracted. This information is of great value to the electricity companies' managers in order to improve their strategies, i.e. it provides management innovations. The good forecast performance and the rapid adaptability of the model to changes in the data are illustrated with actual prices taken from the PJM interconnection in the US and for the Spanish market for the year 2002. (author)
Characteristics and Properties of a Simple Linear Regression Model
Directory of Open Access Journals (Sweden)
Kowal Robert
2016-12-01
Full Text Available A simple linear regression model is one of the pillars of classic econometrics. Despite the passage of time, it continues to raise interest both from the theoretical side as well as from the application side. One of the many fundamental questions in the model concerns determining derivative characteristics and studying the properties existing in their scope, referring to the first of these aspects. The literature of the subject provides several classic solutions in that regard. In the paper, a completely new design is proposed, based on the direct application of variance and its properties, resulting from the non-correlation of certain estimators with the mean, within the scope of which some fundamental dependencies of the model characteristics are obtained in a much more compact manner. The apparatus allows for a simple and uniform demonstration of multiple dependencies and fundamental properties in the model, and it does it in an intuitive manner. The results were obtained in a classic, traditional area, where everything, as it might seem, has already been thoroughly studied and discovered.
Bayesian Regression of Thermodynamic Models of Redox Active Materials
Energy Technology Data Exchange (ETDEWEB)
Johnston, Katherine [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2017-09-01
Finding a suitable functional redox material is a critical challenge to achieving scalable, economically viable technologies for storing concentrated solar energy in the form of a defected oxide. Demonstrating e ectiveness for thermal storage or solar fuel is largely accomplished by using a thermodynamic model derived from experimental data. The purpose of this project is to test the accuracy of our regression model on representative data sets. Determining the accuracy of the model includes parameter tting the model to the data, comparing the model using di erent numbers of param- eters, and analyzing the entropy and enthalpy calculated from the model. Three data sets were considered in this project: two demonstrating materials for solar fuels by wa- ter splitting and the other of a material for thermal storage. Using Bayesian Inference and Markov Chain Monte Carlo (MCMC), parameter estimation was preformed on the three data sets. Good results were achieved, except some there was some deviations on the edges of the data input ranges. The evidence values were then calculated in a variety of ways and used to compare models with di erent number of parameters. It was believed that at least one of the parameters was unnecessary and comparing evidence values demonstrated that the parameter was need on one data set and not signi cantly helpful on another. The entropy was calculated by taking the derivative in one variable and integrating over another. and its uncertainty was also calculated by evaluating the entropy over multiple MCMC samples. Afterwards, all the parts were written up as a tutorial for the Uncertainty Quanti cation Toolkit (UQTk).
Convergence diagnostics for Eigenvalue problems with linear regression model
International Nuclear Information System (INIS)
Shi, Bo; Petrovic, Bojan
2011-01-01
Although the Monte Carlo method has been extensively used for criticality/Eigenvalue problems, a reliable, robust, and efficient convergence diagnostics method is still desired. Most methods are based on integral parameters (multiplication factor, entropy) and either condense the local distribution information into a single value (e.g., entropy) or even disregard it. We propose to employ the detailed cycle-by-cycle local flux evolution obtained by using mesh tally mechanism to assess the source and flux convergence. By applying a linear regression model to each individual mesh in a mesh tally for convergence diagnostics, a global convergence criterion can be obtained. We exemplify this method on two problems and obtain promising diagnostics results. (author)
The R Package threg to Implement Threshold Regression Models
Directory of Open Access Journals (Sweden)
Tao Xiao
2015-08-01
This new package includes four functions: threg, and the methods hr, predict and plot for threg objects returned by threg. The threg function is the model-fitting function which is used to calculate regression coefficient estimates, asymptotic standard errors and p values. The hr method for threg objects is the hazard-ratio calculation function which provides the estimates of hazard ratios at selected time points for specified scenarios (based on given categories or value settings of covariates. The predict method for threg objects is used for prediction. And the plot method for threg objects provides plots for curves of estimated hazard functions, survival functions and probability density functions of the first-hitting-time; function curves corresponding to different scenarios can be overlaid in the same plot for comparison to give additional research insights.
Ng, Kar Yong; Awang, Norhashidah
2018-01-06
Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.
Ultracentrifuge separative power modeling with multivariate regression using covariance matrix
International Nuclear Information System (INIS)
Migliavacca, Elder
2004-01-01
In this work, the least-squares methodology with covariance matrix is applied to determine a data curve fitting to obtain a performance function for the separative power δU of a ultracentrifuge as a function of variables that are experimentally controlled. The experimental data refer to 460 experiments on the ultracentrifugation process for uranium isotope separation. The experimental uncertainties related with these independent variables are considered in the calculation of the experimental separative power values, determining an experimental data input covariance matrix. The process variables, which significantly influence the δU values are chosen in order to give information on the ultracentrifuge behaviour when submitted to several levels of feed flow rate F, cut θ and product line pressure P p . After the model goodness-of-fit validation, a residual analysis is carried out to verify the assumed basis concerning its randomness and independence and mainly the existence of residual heteroscedasticity with any explained regression model variable. The surface curves are made relating the separative power with the control variables F, θ and P p to compare the fitted model with the experimental data and finally to calculate their optimized values. (author)
Modeling Pan Evaporation for Kuwait by Multiple Linear Regression
Almedeij, Jaber
2012-01-01
Evaporation is an important parameter for many projects related to hydrology and water resources systems. This paper constitutes the first study conducted in Kuwait to obtain empirical relations for the estimation of daily and monthly pan evaporation as functions of available meteorological data of temperature, relative humidity, and wind speed. The data used here for the modeling are daily measurements of substantial continuity coverage, within a period of 17 years between January 1993 and December 2009, which can be considered representative of the desert climate of the urban zone of the country. Multiple linear regression technique is used with a procedure of variable selection for fitting the best model forms. The correlations of evaporation with temperature and relative humidity are also transformed in order to linearize the existing curvilinear patterns of the data by using power and exponential functions, respectively. The evaporation models suggested with the best variable combinations were shown to produce results that are in a reasonable agreement with observation values. PMID:23226984
International Nuclear Information System (INIS)
Che Jinxing; Wang Jianzhou
2010-01-01
In this paper, we present the use of different mathematical models to forecast electricity price under deregulated power. A successful prediction tool of electricity price can help both power producers and consumers plan their bidding strategies. Inspired by that the support vector regression (SVR) model, with the ε-insensitive loss function, admits of the residual within the boundary values of ε-tube, we propose a hybrid model that combines both SVR and Auto-regressive integrated moving average (ARIMA) models to take advantage of the unique strength of SVR and ARIMA models in nonlinear and linear modeling, which is called SVRARIMA. A nonlinear analysis of the time-series indicates the convenience of nonlinear modeling, the SVR is applied to capture the nonlinear patterns. ARIMA models have been successfully applied in solving the residuals regression estimation problems. The experimental results demonstrate that the model proposed outperforms the existing neural-network approaches, the traditional ARIMA models and other hybrid models based on the root mean square error and mean absolute percentage error.
An Ordered Regression Model to Predict Transit Passengers’ Behavioural Intentions
Energy Technology Data Exchange (ETDEWEB)
Oña, J. de; Oña, R. de; Eboli, L.; Forciniti, C.; Mazzulla, G.
2016-07-01
Passengers’ behavioural intentions after experiencing transit services can be viewed as signals that show if a customer continues to utilise a company’s service. Users’ behavioural intentions can depend on a series of aspects that are difficult to measure directly. More recently, transit passengers’ behavioural intentions have been just considered together with the concepts of service quality and customer satisfaction. Due to the characteristics of the ways for evaluating passengers’ behavioural intentions, service quality and customer satisfaction, we retain that this kind of issue could be analysed also by applying ordered regression models. This work aims to propose just an ordered probit model for analysing service quality factors that can influence passengers’ behavioural intentions towards the use of transit services. The case study is the LRT of Seville (Spain), where a survey was conducted in order to collect the opinions of the passengers about the existing transit service, and to have a measure of the aspects that can influence the intentions of the users to continue using the transit service in the future. (Author)
Heterogeneous Breast Phantom Development for Microwave Imaging Using Regression Models
Directory of Open Access Journals (Sweden)
Camerin Hahn
2012-01-01
Full Text Available As new algorithms for microwave imaging emerge, it is important to have standard accurate benchmarking tests. Currently, most researchers use homogeneous phantoms for testing new algorithms. These simple structures lack the heterogeneity of the dielectric properties of human tissue and are inadequate for testing these algorithms for medical imaging. To adequately test breast microwave imaging algorithms, the phantom has to resemble different breast tissues physically and in terms of dielectric properties. We propose a systematic approach in designing phantoms that not only have dielectric properties close to breast tissues but also can be easily shaped to realistic physical models. The approach is based on regression model to match phantom's dielectric properties with the breast tissue dielectric properties found in Lazebnik et al. (2007. However, the methodology proposed here can be used to create phantoms for any tissue type as long as ex vivo, in vitro, or in vivo tissue dielectric properties are measured and available. Therefore, using this method, accurate benchmarking phantoms for testing emerging microwave imaging algorithms can be developed.
application of multilinear regression analysis in modeling of soil
African Journals Online (AJOL)
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Accordingly [1, 3] in their work, they applied linear regression ... (MLRA) is a statistical technique that uses several explanatory ... order to check this, they adopted bivariate correlation analysis .... groups, namely A-1 through A-7, based on their relative expected ..... Multivariate Regression in Gorgan Province North of Iran” ...
Wheat flour dough Alveograph characteristics predicted by Mixolab regression models.
Codină, Georgiana Gabriela; Mironeasa, Silvia; Mironeasa, Costel; Popa, Ciprian N; Tamba-Berehoiu, Radiana
2012-02-01
In Romania, the Alveograph is the most used device to evaluate the rheological properties of wheat flour dough, but lately the Mixolab device has begun to play an important role in the breadmaking industry. These two instruments are based on different principles but there are some correlations that can be found between the parameters determined by the Mixolab and the rheological properties of wheat dough measured with the Alveograph. Statistical analysis on 80 wheat flour samples using the backward stepwise multiple regression method showed that Mixolab values using the ‘Chopin S’ protocol (40 samples) and ‘Chopin + ’ protocol (40 samples) can be used to elaborate predictive models for estimating the value of the rheological properties of wheat dough: baking strength (W), dough tenacity (P) and extensibility (L). The correlation analysis confirmed significant findings (P 0.70 for P, R²(adjusted) > 0.70 for W and R²(adjusted) > 0.38 for L, at a 95% confidence interval. Copyright © 2011 Society of Chemical Industry.
Application of regression model on stream water quality parameters
International Nuclear Information System (INIS)
Suleman, M.; Maqbool, F.; Malik, A.H.; Bhatti, Z.A.
2012-01-01
Statistical analysis was conducted to evaluate the effect of solid waste leachate from the open solid waste dumping site of Salhad on the stream water quality. Five sites were selected along the stream. Two sites were selected prior to mixing of leachate with the surface water. One was of leachate and other two sites were affected with leachate. Samples were analyzed for pH, water temperature, electrical conductivity (EC), total dissolved solids (TDS), Biological oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO) and total bacterial load (TBL). In this study correlation coefficient r among different water quality parameters of various sites were calculated by using Pearson model and then average of each correlation between two parameters were also calculated, which shows TDS and EC and pH and BOD have significantly increasing r value, while temperature and TDS, temp and EC, DO and BL, DO and COD have decreasing r value. Single factor ANOVA at 5% level of significance was used which shows EC, TDS, TCL and COD were significantly differ among various sites. By the application of these two statistical approaches TDS and EC shows strongly positive correlation because the ions from the dissolved solids in water influence the ability of that water to conduct an electrical current. These two parameters significantly vary among 5 sites which are further confirmed by using linear regression. (author)
The microcomputer scientific software series 2: general linear model--regression.
Harold M. Rauscher
1983-01-01
The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...
MODELING SNAKE MICROHABITAT FROM RADIOTELEMETRY STUDIES USING POLYTOMOUS LOGISTIC REGRESSION
Multivariate analysis of snake microhabitat has historically used techniques that were derived under assumptions of normality and common covariance structure (e.g., discriminant function analysis, MANOVA). In this study, polytomous logistic regression (PLR which does not require ...
Methods of Detecting Outliers in A Regression Analysis Model ...
African Journals Online (AJOL)
PROF. O. E. OSUAGWU
2013-06-01
Jun 1, 2013 ... especially true in observational studies .... Simple linear regression and multiple ... The simple linear ..... Grubbs,F.E (1950): Sample Criteria for Testing Outlying observations: Annals of ... In experimental design, the Relative.
231 Using Multiple Regression Analysis in Modelling the Role of ...
African Journals Online (AJOL)
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of Internal Revenue, Tourism Bureau and hotel records. The multiple regression .... additional guest facilities such as restaurant, a swimming pool or child care and social function ... and provide good quality service to the public. Conclusion.
Song, Chao; Kwan, Mei-Po; Zhu, Jiping
2017-04-08
An increasing number of fires are occurring with the rapid development of cities, resulting in increased risk for human beings and the environment. This study compares geographically weighted regression-based models, including geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), which integrates spatial and temporal effects and global linear regression models (LM) for modeling fire risk at the city scale. The results show that the road density and the spatial distribution of enterprises have the strongest influences on fire risk, which implies that we should focus on areas where roads and enterprises are densely clustered. In addition, locations with a large number of enterprises have fewer fire ignition records, probably because of strict management and prevention measures. A changing number of significant variables across space indicate that heterogeneity mainly exists in the northern and eastern rural and suburban areas of Hefei city, where human-related facilities or road construction are only clustered in the city sub-centers. GTWR can capture small changes in the spatiotemporal heterogeneity of the variables while GWR and LM cannot. An approach that integrates space and time enables us to better understand the dynamic changes in fire risk. Thus governments can use the results to manage fire safety at the city scale.
DEFF Research Database (Denmark)
Azarang, Leyla; Scheike, Thomas; de Uña-Álvarez, Jacobo
2017-01-01
In this work, we present direct regression analysis for the transition probabilities in the possibly non-Markov progressive illness–death model. The method is based on binomial regression, where the response is the indicator of the occupancy for the given state along time. Randomly weighted score...
A logistic regression model for Ghana National Health Insurance claims
Directory of Open Access Journals (Sweden)
Samuel Antwi
2013-07-01
Full Text Available In August 2003, the Ghanaian Government made history by implementing the first National Health Insurance System (NHIS in Sub-Saharan Africa. Within three years, over half of the country’s population had voluntarily enrolled into the National Health Insurance Scheme. This study had three objectives: 1 To estimate the risk factors that influences the Ghana national health insurance claims. 2 To estimate the magnitude of each of the risk factors in relation to the Ghana national health insurance claims. In this work, data was collected from the policyholders of the Ghana National Health Insurance Scheme with the help of the National Health Insurance database and the patients’ attendance register of the Koforidua Regional Hospital, from 1st January to 31st December 2011. Quantitative analysis was done using the generalized linear regression (GLR models. The results indicate that risk factors such as sex, age, marital status, distance and length of stay at the hospital were important predictors of health insurance claims. However, it was found that the risk factors; health status, billed charges and income level are not good predictors of national health insurance claim. The outcome of the study shows that sex, age, marital status, distance and length of stay at the hospital are statistically significant in the determination of the Ghana National health insurance premiums since they considerably influence claims. We recommended, among other things that, the National Health Insurance Authority should facilitate the institutionalization of the collection of appropriate data on a continuous basis to help in the determination of future premiums.
A generalized additive regression model for survival times
DEFF Research Database (Denmark)
Scheike, Thomas H.
2001-01-01
Additive Aalen model; counting process; disability model; illness-death model; generalized additive models; multiple time-scales; non-parametric estimation; survival data; varying-coefficient models......Additive Aalen model; counting process; disability model; illness-death model; generalized additive models; multiple time-scales; non-parametric estimation; survival data; varying-coefficient models...
Faraway, Julian J
2005-01-01
Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway''s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author''s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the ...
Batis, Carolina; Mendez, Michelle A; Gordon-Larsen, Penny; Sotres-Alvarez, Daniela; Adair, Linda; Popkin, Barry
2016-02-01
We examined the association between dietary patterns and diabetes using the strengths of two methods: principal component analysis (PCA) to identify the eating patterns of the population and reduced rank regression (RRR) to derive a pattern that explains the variation in glycated Hb (HbA1c), homeostasis model assessment of insulin resistance (HOMA-IR) and fasting glucose. We measured diet over a 3 d period with 24 h recalls and a household food inventory in 2006 and used it to derive PCA and RRR dietary patterns. The outcomes were measured in 2009. Adults (n 4316) from the China Health and Nutrition Survey. The adjusted odds ratio for diabetes prevalence (HbA1c≥6·5 %), comparing the highest dietary pattern score quartile with the lowest, was 1·26 (95 % CI 0·76, 2·08) for a modern high-wheat pattern (PCA; wheat products, fruits, eggs, milk, instant noodles and frozen dumplings), 0·76 (95 % CI 0·49, 1·17) for a traditional southern pattern (PCA; rice, meat, poultry and fish) and 2·37 (95 % CI 1·56, 3·60) for the pattern derived with RRR. By comparing the dietary pattern structures of RRR and PCA, we found that the RRR pattern was also behaviourally meaningful. It combined the deleterious effects of the modern high-wheat pattern (high intakes of wheat buns and breads, deep-fried wheat and soya milk) with the deleterious effects of consuming the opposite of the traditional southern pattern (low intakes of rice, poultry and game, fish and seafood). Our findings suggest that using both PCA and RRR provided useful insights when studying the association of dietary patterns with diabetes.
A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs
Karabatsos, George; Walker, Stephen G.
2013-01-01
The regression discontinuity (RD) design (Thistlewaite & Campbell, 1960; Cook, 2008) provides a framework to identify and estimate causal effects from a non-randomized design. Each subject of a RD design is assigned to the treatment (versus assignment to a non-treatment) whenever her/his observed value of the assignment variable equals or…
Parametric vs. Nonparametric Regression Modelling within Clinical Decision Support
Czech Academy of Sciences Publication Activity Database
Kalina, Jan; Zvárová, Jana
2017-01-01
Roč. 5, č. 1 (2017), s. 21-27 ISSN 1805-8698 R&D Projects: GA ČR GA17-01251S Institutional support: RVO:67985807 Keywords : decision support systems * decision rules * statistical analysis * nonparametric regression Subject RIV: IN - Informatics, Computer Science OBOR OECD: Statistics and probability
Directory of Open Access Journals (Sweden)
Nataša Šarlija
2017-01-01
Full Text Available This study sheds light on the most common issues related to applying logistic regression in prediction models for company growth. The purpose of the paper is 1 to provide a detailed demonstration of the steps in developing a growth prediction model based on logistic regression analysis, 2 to discuss common pitfalls and methodological errors in developing a model, and 3 to provide solutions and possible ways of overcoming these issues. Special attention is devoted to the question of satisfying logistic regression assumptions, selecting and defining dependent and independent variables, using classification tables and ROC curves, for reporting model strength, interpreting odds ratios as effect measures and evaluating performance of the prediction model. Development of a logistic regression model in this paper focuses on a prediction model of company growth. The analysis is based on predominantly financial data from a sample of 1471 small and medium-sized Croatian companies active between 2009 and 2014. The financial data is presented in the form of financial ratios divided into nine main groups depicting following areas of business: liquidity, leverage, activity, profitability, research and development, investing and export. The growth prediction model indicates aspects of a business critical for achieving high growth. In that respect, the contribution of this paper is twofold. First, methodological, in terms of pointing out pitfalls and potential solutions in logistic regression modelling, and secondly, theoretical, in terms of identifying factors responsible for high growth of small and medium-sized companies.
Grajeda, Laura M; Ivanescu, Andrada; Saito, Mayuko; Crainiceanu, Ciprian; Jaganath, Devan; Gilman, Robert H; Crabtree, Jean E; Kelleher, Dermott; Cabrera, Lilia; Cama, Vitaliano; Checkley, William
2016-01-01
Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19
Directory of Open Access Journals (Sweden)
Soyoung Park
2017-07-01
Full Text Available This study mapped and analyzed groundwater potential using two different models, logistic regression (LR and multivariate adaptive regression splines (MARS, and compared the results. A spatial database was constructed for groundwater well data and groundwater influence factors. Groundwater well data with a high potential yield of ≥70 m3/d were extracted, and 859 locations (70% were used for model training, whereas the other 365 locations (30% were used for model validation. We analyzed 16 groundwater influence factors including altitude, slope degree, slope aspect, plan curvature, profile curvature, topographic wetness index, stream power index, sediment transport index, distance from drainage, drainage density, lithology, distance from fault, fault density, distance from lineament, lineament density, and land cover. Groundwater potential maps (GPMs were constructed using LR and MARS models and tested using a receiver operating characteristics curve. Based on this analysis, the area under the curve (AUC for the success rate curve of GPMs created using the MARS and LR models was 0.867 and 0.838, and the AUC for the prediction rate curve was 0.836 and 0.801, respectively. This implies that the MARS model is useful and effective for groundwater potential analysis in the study area.
Semiparametric Mixtures of Regressions with Single-index for Model Based Clustering
Xiang, Sijia; Yao, Weixin
2017-01-01
In this article, we propose two classes of semiparametric mixture regression models with single-index for model based clustering. Unlike many semiparametric/nonparametric mixture regression models that can only be applied to low dimensional predictors, the new semiparametric models can easily incorporate high dimensional predictors into the nonparametric components. The proposed models are very general, and many of the recently proposed semiparametric/nonparametric mixture regression models a...
Semiparametric nonlinear quantile regression model for financial returns
Czech Academy of Sciences Publication Activity Database
Avdulaj, Krenar; Baruník, Jozef
2017-01-01
Roč. 21, č. 1 (2017), s. 81-97 ISSN 1081-1826 R&D Projects: GA ČR(CZ) GBP402/12/G097 Institutional support: RVO:67985556 Keywords : copula quantile regression * realized volatility * value-at-risk Subject RIV: AH - Economic s OBOR OECD: Applied Economic s, Econometrics Impact factor: 0.649, year: 2016 http://library.utia.cas.cz/separaty/2017/E/avdulaj-0472346.pdf
Bonellie, Sandra R
2012-10-01
To illustrate the use of regression and logistic regression models to investigate changes over time in size of babies particularly in relation to social deprivation, age of the mother and smoking. Mean birthweight has been found to be increasing in many countries in recent years, but there are still a group of babies who are born with low birthweights. Population-based retrospective cohort study. Multiple linear regression and logistic regression models are used to analyse data on term 'singleton births' from Scottish hospitals between 1994-2003. Mothers who smoke are shown to give birth to lighter babies on average, a difference of approximately 0.57 Standard deviations lower (95% confidence interval. 0.55-0.58) when adjusted for sex and parity. These mothers are also more likely to have babies that are low birthweight (odds ratio 3.46, 95% confidence interval 3.30-3.63) compared with non-smokers. Low birthweight is 30% more likely where the mother lives in the most deprived areas compared with the least deprived, (odds ratio 1.30, 95% confidence interval 1.21-1.40). Smoking during pregnancy is shown to have a detrimental effect on the size of infants at birth. This effect explains some, though not all, of the observed socioeconomic birthweight. It also explains much of the observed birthweight differences by the age of the mother. Identifying mothers at greater risk of having a low birthweight baby as important implications for the care and advice this group receives. © 2012 Blackwell Publishing Ltd.
Beta Regression Finite Mixture Models of Polarization and Priming
Smithson, Michael; Merkle, Edgar C.; Verkuilen, Jay
2011-01-01
This paper describes the application of finite-mixture general linear models based on the beta distribution to modeling response styles, polarization, anchoring, and priming effects in probability judgments. These models, in turn, enhance our capacity for explicitly testing models and theories regarding the aforementioned phenomena. The mixture…
A generalized exponential time series regression model for electricity prices
DEFF Research Database (Denmark)
Haldrup, Niels; Knapik, Oskar; Proietti, Tomasso
on the estimated model, the best linear predictor is constructed. Our modeling approach provides good fit within sample and outperforms competing benchmark predictors in terms of forecasting accuracy. We also find that building separate models for each hour of the day and averaging the forecasts is a better...
Forecast Model of Urban Stagnant Water Based on Logistic Regression
Directory of Open Access Journals (Sweden)
Liu Pan
2017-01-01
Full Text Available With the development of information technology, the construction of water resource system has been gradually carried out. In the background of big data, the work of water information needs to carry out the process of quantitative to qualitative change. Analyzing the correlation of data and exploring the deep value of data which are the key of water information’s research. On the basis of the research on the water big data and the traditional data warehouse architecture, we try to find out the connection of different data source. According to the temporal and spatial correlation of stagnant water and rainfall, we use spatial interpolation to integrate data of stagnant water and rainfall which are from different data source and different sensors, then use logistic regression to find out the relationship between them.
Parental Vaccine Acceptance: A Logistic Regression Model Using Previsit Decisions.
Lee, Sara; Riley-Behringer, Maureen; Rose, Jeanmarie C; Meropol, Sharon B; Lazebnik, Rina
2017-07-01
This study explores how parents' intentions regarding vaccination prior to their children's visit were associated with actual vaccine acceptance. A convenience sample of parents accompanying 6-week-old to 17-year-old children completed a written survey at 2 pediatric practices. Using hierarchical logistic regression, for hospital-based participants (n = 216), vaccine refusal history ( P < .01) and vaccine decision made before the visit ( P < .05) explained 87% of vaccine refusals. In community-based participants (n = 100), vaccine refusal history ( P < .01) explained 81% of refusals. Over 1 in 5 parents changed their minds about vaccination during the visit. Thirty parents who were previous vaccine refusers accepted current vaccines, and 37 who had intended not to vaccinate choose vaccination. Twenty-nine parents without a refusal history declined vaccines, and 32 who did not intend to refuse before the visit declined vaccination. Future research should identify key factors to nudge parent decision making in favor of vaccination.
Directory of Open Access Journals (Sweden)
Drzewiecki Wojciech
2016-12-01
Full Text Available In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques.
Additive Intensity Regression Models in Corporate Default Analysis
DEFF Research Database (Denmark)
Lando, David; Medhat, Mamdouh; Nielsen, Mads Stenbo
2013-01-01
We consider additive intensity (Aalen) models as an alternative to the multiplicative intensity (Cox) models for analyzing the default risk of a sample of rated, nonfinancial U.S. firms. The setting allows for estimating and testing the significance of time-varying effects. We use a variety of mo...
Misspecified poisson regression models for large-scale registry data
DEFF Research Database (Denmark)
Grøn, Randi; Gerds, Thomas A.; Andersen, Per K.
2016-01-01
working models that are then likely misspecified. To support and improve conclusions drawn from such models, we discuss methods for sensitivity analysis, for estimation of average exposure effects using aggregated data, and a semi-parametric bootstrap method to obtain robust standard errors. The methods...
Logistic regression model for detecting radon prone areas in Ireland.
Elío, J; Crowley, Q; Scanlon, R; Hodgson, J; Long, S
2017-12-01
A new high spatial resolution radon risk map of Ireland has been developed, based on a combination of indoor radon measurements (n=31,910) and relevant geological information (i.e. Bedrock Geology, Quaternary Geology, soil permeability and aquifer type). Logistic regression was used to predict the probability of having an indoor radon concentration above the national reference level of 200Bqm -3 in Ireland. The four geological datasets evaluated were found to be statistically significant, and, based on combinations of these four variables, the predicted probabilities ranged from 0.57% to 75.5%. Results show that the Republic of Ireland may be divided in three main radon risk categories: High (HR), Medium (MR) and Low (LR). The probability of having an indoor radon concentration above 200Bqm -3 in each area was found to be 19%, 8% and 3%; respectively. In the Republic of Ireland, the population affected by radon concentrations above 200Bqm -3 is estimated at ca. 460k (about 10% of the total population). Of these, 57% (265k), 35% (160k) and 8% (35k) are in High, Medium and Low Risk Areas, respectively. Our results provide a high spatial resolution utility which permit customised radon-awareness information to be targeted at specific geographic areas. Copyright © 2017 Elsevier B.V. All rights reserved.
Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.
Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko
2016-03-01
In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.
Logistic Regression Modeling of Diminishing Manufacturing Sources for Integrated Circuits
National Research Council Canada - National Science Library
Gravier, Michael
1999-01-01
.... This thesis draws on available data from the electronics integrated circuit industry to attempt to assess whether statistical modeling offers a viable method for predicting the presence of DMSMS...
U.S. Environmental Protection Agency — Spreadsheets are included here to support the manuscript "Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition". This...
Martingale Regressions for a Continuous Time Model of Exchange Rates
Guo, Zi-Yi
2017-01-01
One of the daunting problems in international finance is the weak explanatory power of existing theories of the nominal exchange rates, the so-called “foreign exchange rate determination puzzle”. We propose a continuous-time model to study the impact of order flow on foreign exchange rates. The model is estimated by a newly developed econometric tool based on a time-change sampling from calendar to volatility time. The estimation results indicate that the effect of order flow on exchange rate...
Focused information criterion and model averaging based on weighted composite quantile regression
Xu, Ganggang; Wang, Suojin; Huang, Jianhua Z.
2013-01-01
We study the focused information criterion and frequentist model averaging and their application to post-model-selection inference for weighted composite quantile regression (WCQR) in the context of the additive partial linear models. With the non
Cox's regression model for dynamics of grouped unemployment data
Czech Academy of Sciences Publication Activity Database
Volf, Petr
2003-01-01
Roč. 10, č. 19 (2003), s. 151-162 ISSN 1212-074X R&D Projects: GA ČR GA402/01/0539 Institutional research plan: CEZ:AV0Z1075907 Keywords : mathematical statistics * survival analysis * Cox's model Subject RIV: BB - Applied Statistics, Operational Research
Multiple Linear Regression Model for Estimating the Price of a ...
African Journals Online (AJOL)
Ghana Mining Journal ... In the modeling, the Ordinary Least Squares (OLS) normality assumption which could introduce errors in the statistical analyses was dealt with by log transformation of the data, ensuring the data is normally ... The resultant MLRM is: Ŷi MLRM = (X'X)-1X'Y(xi') where X is the sample data matrix.
Inflation, Forecast Intervals and Long Memory Regression Models
C.S. Bos (Charles); Ph.H.B.F. Franses (Philip Hans); M. Ooms (Marius)
2001-01-01
textabstractWe examine recursive out-of-sample forecasting of monthly postwar U.S. core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading
Inflation, Forecast Intervals and Long Memory Regression Models
Ooms, M.; Bos, C.S.; Franses, P.H.
2003-01-01
We examine recursive out-of-sample forecasting of monthly postwar US core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading indicators
Data-driven modelling of LTI systems using symbolic regression
Khandelwal, D.; Toth, R.; Van den Hof, P.M.J.
2017-01-01
The aim of this project is to automate the task of data-driven identification of dynamical systems. The underlying goal is to develop an identification tool that models a physical system without distinguishing between classes of systems such as linear, nonlinear or possibly even hybrid systems. Such
Nonparametric Estimation of Regression Parameters in Measurement Error Models
Czech Academy of Sciences Publication Activity Database
Ehsanes Saleh, A.K.M.D.; Picek, J.; Kalina, Jan
2009-01-01
Roč. 67, č. 2 (2009), s. 177-200 ISSN 0026-1424 Grant - others:GA AV ČR(CZ) IAA101120801; GA MŠk(CZ) LC06024 Institutional research plan: CEZ:AV0Z10300504 Keywords : asymptotic relative efficiency(ARE) * asymptotic theory * emaculate mode * Me model * R-estimation * Reliabilty ratio(RR) Subject RIV: BB - Applied Statistics, Operational Research
Takahashi, Kiyohiko; Nakamura, Akinobu; Miyoshi, Hideaki; Nomoto, Hiroshi; Kameda, Hiraku; Cho, Kyu Yong; Nagai, So; Shimizu, Chikara; Taguri, Masataka; Terauchi, Yasuo; Atsumi, Tatsuya
2017-04-29
We attempted to identify the predictors of an inadequate hypoglycemia in insulin tolerance test (ITT), defined as a blood glucose level higher than 2.8 mmol/L after insulin injection, in Japanese patients with suspected or proven hypopituitarism. A total of 78 patients who had undergone ITT were divided into adequate and inadequate hypoglycemia groups. The relationships between the subjects' clinical parameters and inadequate hypoglycemia in ITT were analyzed. Stepwise logistic regression analysis identified high systolic blood pressure (SBP) and high homeostasis model assessment of insulin resistance (HOMA-IR) as being independent factors associated with inadequate hypoglycemia in ITT. Receiver operating characteristic (ROC) curve analysis revealed the cutoff value for inadequate hypoglycemia was 109 mmHg for SBP and 1.4 for HOMA-IR. The areas under ROC curve for SBP and HOMA-IR were 0.72 and 0.86, respectively. We confirmed that high values of SBP and HOMA-IR were associated with inadequate hypoglycemia in ITT, regardless of the degree of reduction of pituitary hormone levels. Furthermore, the strongest predictor of inadequate hypoglycemia was obtained by using the cutoff value of HOMA-IR. Our results suggest that HOMA-IR is a useful pre-screening tool for ITT in these populations.
Shaofu Zhuyu Decoction Regresses Endometriotic Lesions in a Rat Model
Directory of Open Access Journals (Sweden)
Guanghui Zhu
2018-01-01
Full Text Available The current therapies for endometriosis are restricted by various side effects and treatment outcome has been less than satisfactory. Shaofu Zhuyu Decoction (SZD, a classic traditional Chinese medicinal (TCM prescription for dysmenorrhea, has been widely used in clinical practice by TCM doctors to relieve symptoms of endometriosis. The present study aimed to investigate the effects of SZD on a rat model of endometriosis. Forty-eight female Sprague-Dawley rats with regular estrous cycles went through autotransplantation operation to establish endometriosis model. Then 38 rats with successful ectopic implants were randomized into two groups: vehicle- and SZD-treated groups. The latter were administered SZD through oral gavage for 4 weeks. By the end of the treatment period, the volume of the endometriotic lesions was measured, the histopathological properties of the ectopic endometrium were evaluated, and levels of proliferating cell nuclear antigen (PCNA, CD34, and hypoxia inducible factor- (HIF- 1α in the ectopic endometrium were detected with immunohistochemistry. Furthermore, apoptosis was assessed using the terminal deoxynucleotidyl transferase (TdT deoxyuridine 5′-triphosphate (dUTP nick-end labeling (TUNEL assay. In this study, SZD significantly reduced the size of ectopic lesions in rats with endometriosis, inhibited cell proliferation, increased cell apoptosis, and reduced microvessel density and HIF-1α expression. It suggested that SZD could be an effective therapy for the treatment and prevention of endometriosis recurrence.
[Application of detecting and taking overdispersion into account in Poisson regression model].
Bouche, G; Lepage, B; Migeot, V; Ingrand, P
2009-08-01
Researchers often use the Poisson regression model to analyze count data. Overdispersion can occur when a Poisson regression model is used, resulting in an underestimation of variance of the regression model parameters. Our objective was to take overdispersion into account and assess its impact with an illustration based on the data of a study investigating the relationship between use of the Internet to seek health information and number of primary care consultations. Three methods, overdispersed Poisson, a robust estimator, and negative binomial regression, were performed to take overdispersion into account in explaining variation in the number (Y) of primary care consultations. We tested overdispersion in the Poisson regression model using the ratio of the sum of Pearson residuals over the number of degrees of freedom (chi(2)/df). We then fitted the three models and compared parameter estimation to the estimations given by Poisson regression model. Variance of the number of primary care consultations (Var[Y]=21.03) was greater than the mean (E[Y]=5.93) and the chi(2)/df ratio was 3.26, which confirmed overdispersion. Standard errors of the parameters varied greatly between the Poisson regression model and the three other regression models. Interpretation of estimates from two variables (using the Internet to seek health information and single parent family) would have changed according to the model retained, with significant levels of 0.06 and 0.002 (Poisson), 0.29 and 0.09 (overdispersed Poisson), 0.29 and 0.13 (use of a robust estimator) and 0.45 and 0.13 (negative binomial) respectively. Different methods exist to solve the problem of underestimating variance in the Poisson regression model when overdispersion is present. The negative binomial regression model seems to be particularly accurate because of its theorical distribution ; in addition this regression is easy to perform with ordinary statistical software packages.
Linking Simple Economic Theory Models and the Cointegrated Vector AutoRegressive Model
DEFF Research Database (Denmark)
Møller, Niels Framroze
This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its stru....... Further fundamental extensions and advances to more sophisticated theory models, such as those related to dynamics and expectations (in the structural relations) are left for future papers......This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its......, it is demonstrated how other controversial hypotheses such as Rational Expectations can be formulated directly as restrictions on the CVAR-parameters. A simple example of a "Neoclassical synthetic" AS-AD model is also formulated. Finally, the partial- general equilibrium distinction is related to the CVAR as well...
Mousavi, Seyedeh Neda; Faghihi, Amirhosein; Motaghinejad, Majid; Shiasi, Maryam; Imanparast, Fatemeh; Amiri, Hamid Lorvand; Shidfar, Farzad
2018-02-01
Studies have shown that non-alcoholic fatty liver disease (NAFLD) patients are more prone to cardiovascular disease (CVD). Zinc and selenium deficiency are common in NAFLD. But the effects of zinc and selenium co-supplementation before and/or after disease progression on CVD markers are not clear in NAFLD patients. This study aimed to compare the effects of zinc and selenium co-supplementation before and/or after disease progression on some of the CVD markers in an experimental model of NAFLD. Forty male Sprague Dawley rats (197 ± 4 g) were randomly assigned into four dietary groups: control group (C; received 9% of calorie as fat), model group (M; received 82% of calorie as fat), and supplementation before (BS) or after (AS) disease progression. Animals were fed diets for 20 weeks in all groups. Fasting plasma glucose (FPG), insulin, HOMA-IR, ALT, AST, lipid profile, malondialdehyde (MDA) and vascular endothelial growth factor (VEGF) levels were measured as CVD indices. Serum ALT, AST, FPG, insulin, MDA, VEGF and HOMA-IR were significantly higher in the M than C group. Co-supplementation reduced serum ALT and AST levels in the BS and AS groups compared with the M group. FPG, insulin, HOMA-IR, VEGF, MDA, LDL/HDL-c and TC/HDL-c ratio were significantly reduced in the AS compared with the M group. TG/HDL-c ratio was significantly reduced in the BS and AS compared with the M group. Serum MDA, VEGF, Insulin and HOMA-IR were significantly lowered in the AS than BS group (p < 0.05). Zinc and selenium co-supplementation after NAFLD progression reduced CVD risk indices in an experimental model.
Using the classical linear regression model in analysis of the dependences of conveyor belt life
Directory of Open Access Journals (Sweden)
Miriam Andrejiová
2013-12-01
Full Text Available The paper deals with the classical linear regression model of the dependence of conveyor belt life on some selected parameters: thickness of paint layer, width and length of the belt, conveyor speed and quantity of transported material. The first part of the article is about regression model design, point and interval estimation of parameters, verification of statistical significance of the model, and about the parameters of the proposed regression model. The second part of the article deals with identification of influential and extreme values that can have an impact on estimation of regression model parameters. The third part focuses on assumptions of the classical regression model, i.e. on verification of independence assumptions, normality and homoscedasticity of residuals.
Suhartono, Lee, Muhammad Hisyam; Prastyo, Dedy Dwi
2015-12-01
The aim of this research is to develop a calendar variation model for forecasting retail sales data with the Eid ul-Fitr effect. The proposed model is based on two methods, namely two levels ARIMAX and regression methods. Two levels ARIMAX and regression models are built by using ARIMAX for the first level and regression for the second level. Monthly men's jeans and women's trousers sales in a retail company for the period January 2002 to September 2009 are used as case study. In general, two levels of calendar variation model yields two models, namely the first model to reconstruct the sales pattern that already occurred, and the second model to forecast the effect of increasing sales due to Eid ul-Fitr that affected sales at the same and the previous months. The results show that the proposed two level calendar variation model based on ARIMAX and regression methods yields better forecast compared to the seasonal ARIMA model and Neural Networks.
Statistical approach for selection of regression model during validation of bioanalytical method
Directory of Open Access Journals (Sweden)
Natalija Nakov
2014-06-01
Full Text Available The selection of an adequate regression model is the basis for obtaining accurate and reproducible results during the bionalytical method validation. Given the wide concentration range, frequently present in bioanalytical assays, heteroscedasticity of the data may be expected. Several weighted linear and quadratic regression models were evaluated during the selection of the adequate curve fit using nonparametric statistical tests: One sample rank test and Wilcoxon signed rank test for two independent groups of samples. The results obtained with One sample rank test could not give statistical justification for the selection of linear vs. quadratic regression models because slight differences between the error (presented through the relative residuals were obtained. Estimation of the significance of the differences in the RR was achieved using Wilcoxon signed rank test, where linear and quadratic regression models were treated as two independent groups. The application of this simple non-parametric statistical test provides statistical confirmation of the choice of an adequate regression model.
On a Robust MaxEnt Process Regression Model with Sample-Selection
Directory of Open Access Journals (Sweden)
Hea-Jung Kim
2018-04-01
Full Text Available In a regression analysis, a sample-selection bias arises when a dependent variable is partially observed as a result of the sample selection. This study introduces a Maximum Entropy (MaxEnt process regression model that assumes a MaxEnt prior distribution for its nonparametric regression function and finds that the MaxEnt process regression model includes the well-known Gaussian process regression (GPR model as a special case. Then, this special MaxEnt process regression model, i.e., the GPR model, is generalized to obtain a robust sample-selection Gaussian process regression (RSGPR model that deals with non-normal data in the sample selection. Various properties of the RSGPR model are established, including the stochastic representation, distributional hierarchy, and magnitude of the sample-selection bias. These properties are used in the paper to develop a hierarchical Bayesian methodology to estimate the model. This involves a simple and computationally feasible Markov chain Monte Carlo algorithm that avoids analytical or numerical derivatives of the log-likelihood function of the model. The performance of the RSGPR model in terms of the sample-selection bias correction, robustness to non-normality, and prediction, is demonstrated through results in simulations that attest to its good finite-sample performance.
Directory of Open Access Journals (Sweden)
Ivanka Jerić
2011-11-01
Full Text Available Predicting antitumor activity of compounds using regression models trained on a small number of compounds with measured biological activity is an ill-posed inverse problem. Yet, it occurs very often within the academic community. To counteract, up to some extent, overfitting problems caused by a small training data, we propose to use consensus of six regression models for prediction of biological activity of virtual library of compounds. The QSAR descriptors of 22 compounds related to the opioid growth factor (OGF, Tyr-Gly-Gly-Phe-Met with known antitumor activity were used to train regression models: the feed-forward artificial neural network, the k-nearest neighbor, sparseness constrained linear regression, the linear and nonlinear (with polynomial and Gaussian kernel support vector machine. Regression models were applied on a virtual library of 429 compounds that resulted in six lists with candidate compounds ranked by predicted antitumor activity. The highly ranked candidate compounds were synthesized, characterized and tested for an antiproliferative activity. Some of prepared peptides showed more pronounced activity compared with the native OGF; however, they were less active than highly ranked compounds selected previously by the radial basis function support vector machine (RBF SVM regression model. The ill-posedness of the related inverse problem causes unstable behavior of trained regression models on test data. These results point to high complexity of prediction based on the regression models trained on a small data sample.
A generalized right truncated bivariate Poisson regression model with applications to health data.
Islam, M Ataharul; Chowdhury, Rafiqul I
2017-01-01
A generalized right truncated bivariate Poisson regression model is proposed in this paper. Estimation and tests for goodness of fit and over or under dispersion are illustrated for both untruncated and right truncated bivariate Poisson regression models using marginal-conditional approach. Estimation and test procedures are illustrated for bivariate Poisson regression models with applications to Health and Retirement Study data on number of health conditions and the number of health care services utilized. The proposed test statistics are easy to compute and it is evident from the results that the models fit the data very well. A comparison between the right truncated and untruncated bivariate Poisson regression models using the test for nonnested models clearly shows that the truncated model performs significantly better than the untruncated model.
Wei, Jiawei; Carroll, Raymond J.; Maity, Arnab
2011-01-01
We consider the problem of testing for a constant nonparametric effect in a general semi-parametric regression model when there is the potential for interaction between the parametrically and nonparametrically modeled variables. The work
Koon, Sharon; Petscher, Yaacov
2015-01-01
The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by…
Analysis of dental caries using generalized linear and count regression models
Directory of Open Access Journals (Sweden)
Javali M. Phil
2013-11-01
Full Text Available Generalized linear models (GLM are generalization of linear regression models, which allow fitting regression models to response data in all the sciences especially medical and dental sciences that follow a general exponential family. These are flexible and widely used class of such models that can accommodate response variables. Count data are frequently characterized by overdispersion and excess zeros. Zero-inflated count models provide a parsimonious yet powerful way to model this type of situation. Such models assume that the data are a mixture of two separate data generation processes: one generates only zeros, and the other is either a Poisson or a negative binomial data-generating process. Zero inflated count regression models such as the zero-inflated Poisson (ZIP, zero-inflated negative binomial (ZINB regression models have been used to handle dental caries count data with many zeros. We present an evaluation framework to the suitability of applying the GLM, Poisson, NB, ZIP and ZINB to dental caries data set where the count data may exhibit evidence of many zeros and over-dispersion. Estimation of the model parameters using the method of maximum likelihood is provided. Based on the Vuong test statistic and the goodness of fit measure for dental caries data, the NB and ZINB regression models perform better than other count regression models.
Accounting for measurement error in log regression models with applications to accelerated testing.
Directory of Open Access Journals (Sweden)
Robert Richardson
Full Text Available In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.
Accounting for measurement error in log regression models with applications to accelerated testing.
Richardson, Robert; Tolley, H Dennis; Evenson, William E; Lunt, Barry M
2018-01-01
In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.
Generic global regression models for growth prediction of Salmonella in ground pork and pork cuts
DEFF Research Database (Denmark)
Buschhardt, Tasja; Hansen, Tina Beck; Bahl, Martin Iain
2017-01-01
Introduction and Objectives Models for the prediction of bacterial growth in fresh pork are primarily developed using two-step regression (i.e. primary models followed by secondary models). These models are also generally based on experiments in liquids or ground meat and neglect surface growth....... It has been shown that one-step global regressions can result in more accurate models and that bacterial growth on intact surfaces can substantially differ from growth in liquid culture. Material and Methods We used a global-regression approach to develop predictive models for the growth of Salmonella....... One part of obtained logtransformed cell counts was used for model development and another for model validation. The Ratkowsky square root model and the relative lag time (RLT) model were integrated into the logistic model with delay. Fitted parameter estimates were compared to investigate the effect...
Directory of Open Access Journals (Sweden)
Soldić-Aleksić Jasna
2009-01-01
Full Text Available Market segmentation presents one of the key concepts of the modern marketing. The main goal of market segmentation is focused on creating groups (segments of customers that have similar characteristics, needs, wishes and/or similar behavior regarding the purchase of concrete product/service. Companies can create specific marketing plan for each of these segments and therefore gain short or long term competitive advantage on the market. Depending on the concrete marketing goal, different segmentation schemes and techniques may be applied. This paper presents a predictive market segmentation model based on the application of logistic regression model and CHAID analysis. The logistic regression model was used for the purpose of variables selection (from the initial pool of eleven variables which are statistically significant for explaining the dependent variable. Selected variables were afterwards included in the CHAID procedure that generated the predictive market segmentation model. The model results are presented on the concrete empirical example in the following form: summary model results, CHAID tree, Gain chart, Index chart, risk and classification tables.
MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.
2005-01-01
Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.
Robust geographically weighted regression of modeling the Air Polluter Standard Index (APSI)
Warsito, Budi; Yasin, Hasbi; Ispriyanti, Dwi; Hoyyi, Abdul
2018-05-01
The Geographically Weighted Regression (GWR) model has been widely applied to many practical fields for exploring spatial heterogenity of a regression model. However, this method is inherently not robust to outliers. Outliers commonly exist in data sets and may lead to a distorted estimate of the underlying regression model. One of solution to handle the outliers in the regression model is to use the robust models. So this model was called Robust Geographically Weighted Regression (RGWR). This research aims to aid the government in the policy making process related to air pollution mitigation by developing a standard index model for air polluter (Air Polluter Standard Index - APSI) based on the RGWR approach. In this research, we also consider seven variables that are directly related to the air pollution level, which are the traffic velocity, the population density, the business center aspect, the air humidity, the wind velocity, the air temperature, and the area size of the urban forest. The best model is determined by the smallest AIC value. There are significance differences between Regression and RGWR in this case, but Basic GWR using the Gaussian kernel is the best model to modeling APSI because it has smallest AIC.
Amalia, Junita; Purhadi, Otok, Bambang Widjanarko
2017-11-01
Poisson distribution is a discrete distribution with count data as the random variables and it has one parameter defines both mean and variance. Poisson regression assumes mean and variance should be same (equidispersion). Nonetheless, some case of the count data unsatisfied this assumption because variance exceeds mean (over-dispersion). The ignorance of over-dispersion causes underestimates in standard error. Furthermore, it causes incorrect decision in the statistical test. Previously, paired count data has a correlation and it has bivariate Poisson distribution. If there is over-dispersion, modeling paired count data is not sufficient with simple bivariate Poisson regression. Bivariate Poisson Inverse Gaussian Regression (BPIGR) model is mix Poisson regression for modeling paired count data within over-dispersion. BPIGR model produces a global model for all locations. In another hand, each location has different geographic conditions, social, cultural and economic so that Geographically Weighted Regression (GWR) is needed. The weighting function of each location in GWR generates a different local model. Geographically Weighted Bivariate Poisson Inverse Gaussian Regression (GWBPIGR) model is used to solve over-dispersion and to generate local models. Parameter estimation of GWBPIGR model obtained by Maximum Likelihood Estimation (MLE) method. Meanwhile, hypothesis testing of GWBPIGR model acquired by Maximum Likelihood Ratio Test (MLRT) method.
Can We Use Regression Modeling to Quantify Mean Annual Streamflow at a Global-Scale?
Barbarossa, V.; Huijbregts, M. A. J.; Hendriks, J. A.; Beusen, A.; Clavreul, J.; King, H.; Schipper, A.
2016-12-01
Quantifying mean annual flow of rivers (MAF) at ungauged sites is essential for a number of applications, including assessments of global water supply, ecosystem integrity and water footprints. MAF can be quantified with spatially explicit process-based models, which might be overly time-consuming and data-intensive for this purpose, or with empirical regression models that predict MAF based on climate and catchment characteristics. Yet, regression models have mostly been developed at a regional scale and the extent to which they can be extrapolated to other regions is not known. In this study, we developed a global-scale regression model for MAF using observations of discharge and catchment characteristics from 1,885 catchments worldwide, ranging from 2 to 106 km2 in size. In addition, we compared the performance of the regression model with the predictive ability of the spatially explicit global hydrological model PCR-GLOBWB [van Beek et al., 2011] by comparing results from both models to independent measurements. We obtained a regression model explaining 89% of the variance in MAF based on catchment area, mean annual precipitation and air temperature, average slope and elevation. The regression model performed better than PCR-GLOBWB for the prediction of MAF, as root-mean-square error values were lower (0.29 - 0.38 compared to 0.49 - 0.57) and the modified index of agreement was higher (0.80 - 0.83 compared to 0.72 - 0.75). Our regression model can be applied globally at any point of the river network, provided that the input parameters are within the range of values employed in the calibration of the model. The performance is reduced for water scarce regions and further research should focus on improving such an aspect for regression-based global hydrological models.
ANALYSIS OF THE FINANCIAL PERFORMANCES OF THE FIRM, BY USING THE MULTIPLE REGRESSION MODEL
Directory of Open Access Journals (Sweden)
Constantin Anghelache
2011-11-01
Full Text Available The information achieved through the use of simple linear regression are not always enough to characterize the evolution of an economic phenomenon and, furthermore, to identify its possible future evolution. To remedy these drawbacks, the special literature includes multiple regression models, in which the evolution of the dependant variable is defined depending on two or more factorial variables.
DEFF Research Database (Denmark)
Strathe, Anders B; Mark, Thomas; Nielsen, Bjarne
2014-01-01
Random regression models were used to estimate covariance functions between cumulated feed intake (CFI) and body weight (BW) in 8424 Danish Duroc pigs. Random regressions on second order Legendre polynomials of age were used to describe genetic and permanent environmental curves in BW and CFI...
Modelling infant mortality rate in Central Java, Indonesia use generalized poisson regression method
Prahutama, Alan; Sudarno
2018-05-01
The infant mortality rate is the number of deaths under one year of age occurring among the live births in a given geographical area during a given year, per 1,000 live births occurring among the population of the given geographical area during the same year. This problem needs to be addressed because it is an important element of a country’s economic development. High infant mortality rate will disrupt the stability of a country as it relates to the sustainability of the population in the country. One of regression model that can be used to analyze the relationship between dependent variable Y in the form of discrete data and independent variable X is Poisson regression model. Recently The regression modeling used for data with dependent variable is discrete, among others, poisson regression, negative binomial regression and generalized poisson regression. In this research, generalized poisson regression modeling gives better AIC value than poisson regression. The most significant variable is the Number of health facilities (X1), while the variable that gives the most influence to infant mortality rate is the average breastfeeding (X9).
Directory of Open Access Journals (Sweden)
Mach Łukasz
2017-06-01
Full Text Available The research process aimed at building regression models, which helps to valuate residential real estate, is presented in the following article. Two widely used computational tools i.e. the classical multiple regression and regression models of artificial neural networks were used in order to build models. An attempt to define the utilitarian usefulness of the above-mentioned tools and comparative analysis of them is the aim of the conducted research. Data used for conducting analyses refers to the secondary transactional residential real estate market.
Linear regression metamodeling as a tool to summarize and present simulation model results.
Jalal, Hawre; Dowd, Bryan; Sainfort, François; Kuntz, Karen M
2013-10-01
Modelers lack a tool to systematically and clearly present complex model results, including those from sensitivity analyses. The objective was to propose linear regression metamodeling as a tool to increase transparency of decision analytic models and better communicate their results. We used a simplified cancer cure model to demonstrate our approach. The model computed the lifetime cost and benefit of 3 treatment options for cancer patients. We simulated 10,000 cohorts in a probabilistic sensitivity analysis (PSA) and regressed the model outcomes on the standardized input parameter values in a set of regression analyses. We used the regression coefficients to describe measures of sensitivity analyses, including threshold and parameter sensitivity analyses. We also compared the results of the PSA to deterministic full-factorial and one-factor-at-a-time designs. The regression intercept represented the estimated base-case outcome, and the other coefficients described the relative parameter uncertainty in the model. We defined simple relationships that compute the average and incremental net benefit of each intervention. Metamodeling produced outputs similar to traditional deterministic 1-way or 2-way sensitivity analyses but was more reliable since it used all parameter values. Linear regression metamodeling is a simple, yet powerful, tool that can assist modelers in communicating model characteristics and sensitivity analyses.
[Evaluation of estimation of prevalence ratio using bayesian log-binomial regression model].
Gao, W L; Lin, H; Liu, X N; Ren, X W; Li, J S; Shen, X P; Zhu, S L
2017-03-10
To evaluate the estimation of prevalence ratio ( PR ) by using bayesian log-binomial regression model and its application, we estimated the PR of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea in their infants by using bayesian log-binomial regression model in Openbugs software. The results showed that caregivers' recognition of infant' s risk signs of diarrhea was associated significantly with a 13% increase of medical care-seeking. Meanwhile, we compared the differences in PR 's point estimation and its interval estimation of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea and convergence of three models (model 1: not adjusting for the covariates; model 2: adjusting for duration of caregivers' education, model 3: adjusting for distance between village and township and child month-age based on model 2) between bayesian log-binomial regression model and conventional log-binomial regression model. The results showed that all three bayesian log-binomial regression models were convergence and the estimated PRs were 1.130(95 %CI : 1.005-1.265), 1.128(95 %CI : 1.001-1.264) and 1.132(95 %CI : 1.004-1.267), respectively. Conventional log-binomial regression model 1 and model 2 were convergence and their PRs were 1.130(95 % CI : 1.055-1.206) and 1.126(95 % CI : 1.051-1.203), respectively, but the model 3 was misconvergence, so COPY method was used to estimate PR , which was 1.125 (95 %CI : 1.051-1.200). In addition, the point estimation and interval estimation of PRs from three bayesian log-binomial regression models differed slightly from those of PRs from conventional log-binomial regression model, but they had a good consistency in estimating PR . Therefore, bayesian log-binomial regression model can effectively estimate PR with less misconvergence and have more advantages in application compared with conventional log-binomial regression model.
Amaliana, Luthfatul; Sa'adah, Umu; Wayan Surya Wardhani, Ni
2017-12-01
Tetanus Neonatorum is an infectious disease that can be prevented by immunization. The number of Tetanus Neonatorum cases in East Java Province is the highest in Indonesia until 2015. Tetanus Neonatorum data contain over dispersion and big enough proportion of zero-inflation. Negative Binomial (NB) regression is an alternative method when over dispersion happens in Poisson regression. However, the data containing over dispersion and zero-inflation are more appropriately analyzed by using Zero-Inflated Negative Binomial (ZINB) regression. The purpose of this study are: (1) to model Tetanus Neonatorum cases in East Java Province with 71.05 percent proportion of zero-inflation by using NB and ZINB regression, (2) to obtain the best model. The result of this study indicates that ZINB is better than NB regression with smaller AIC.
Poisson regression for modeling count and frequency outcomes in trauma research.
Gagnon, David R; Doron-LaMarca, Susan; Bell, Margret; O'Farrell, Timothy J; Taft, Casey T
2008-10-01
The authors describe how the Poisson regression method for analyzing count or frequency outcome variables can be applied in trauma studies. The outcome of interest in trauma research may represent a count of the number of incidents of behavior occurring in a given time interval, such as acts of physical aggression or substance abuse. Traditional linear regression approaches assume a normally distributed outcome variable with equal variances over the range of predictor variables, and may not be optimal for modeling count outcomes. An application of Poisson regression is presented using data from a study of intimate partner aggression among male patients in an alcohol treatment program and their female partners. Results of Poisson regression and linear regression models are compared.
A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield
Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan
2018-04-01
In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.
Drzewiecki, Wojciech
2016-12-01
In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.
As a fast and effective technique, the multiple linear regression (MLR) method has been widely used in modeling and prediction of beach bacteria concentrations. Among previous works on this subject, however, several issues were insufficiently or inconsistently addressed. Those is...
Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models
Energy Technology Data Exchange (ETDEWEB)
Pappas, S.S. [Department of Information and Communication Systems Engineering, University of the Aegean, Karlovassi, 83 200 Samos (Greece); Ekonomou, L.; Chatzarakis, G.E. [Department of Electrical Engineering Educators, ASPETE - School of Pedagogical and Technological Education, N. Heraklion, 141 21 Athens (Greece); Karamousantas, D.C. [Technological Educational Institute of Kalamata, Antikalamos, 24100 Kalamata (Greece); Katsikas, S.K. [Department of Technology Education and Digital Systems, University of Piraeus, 150 Androutsou Srt., 18 532 Piraeus (Greece); Liatsis, P. [Division of Electrical Electronic and Information Engineering, School of Engineering and Mathematical Sciences, Information and Biomedical Engineering Centre, City University, Northampton Square, London EC1V 0HB (United Kingdom)
2008-09-15
This study addresses the problem of modeling the electricity demand loads in Greece. The provided actual load data is deseasonilized and an AutoRegressive Moving Average (ARMA) model is fitted on the data off-line, using the Akaike Corrected Information Criterion (AICC). The developed model fits the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on-line/adaptive modeling is required. In both cases and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise are performed. The produced results indicate that the proposed method, which is based on the multi-model partitioning theory, tackles successfully the studied problem. For validation purposes the produced results are compared with three other established order selection criteria, namely AICC, Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The developed model could be useful in the studies that concern electricity consumption and electricity prices forecasts. (author)
Yusuf, O B; Bamgboye, E A; Afolabi, R F; Shodimu, M A
2014-09-01
Logistic regression model is widely used in health research for description and predictive purposes. Unfortunately, most researchers are sometimes not aware that the underlying principles of the techniques have failed when the algorithm for maximum likelihood does not converge. Young researchers particularly postgraduate students may not know why separation problem whether quasi or complete occurs, how to identify it and how to fix it. This study was designed to critically evaluate convergence issues in articles that employed logistic regression analysis published in an African Journal of Medicine and medical sciences between 2004 and 2013. Problems of quasi or complete separation were described and were illustrated with the National Demographic and Health Survey dataset. A critical evaluation of articles that employed logistic regression was conducted. A total of 581 articles was reviewed, of which 40 (6.9%) used binary logistic regression. Twenty-four (60.0%) stated the use of logistic regression model in the methodology while none of the articles assessed model fit. Only 3 (12.5%) properly described the procedures. Of the 40 that used the logistic regression model, the problem of convergence occurred in 6 (15.0%) of the articles. Logistic regression tends to be poorly reported in studies published between 2004 and 2013. Our findings showed that the procedure may not be well understood by researchers since very few described the process in their reports and may be totally unaware of the problem of convergence or how to deal with it.
Developing and testing a global-scale regression model to quantify mean annual streamflow
Barbarossa, Valerio; Huijbregts, Mark A. J.; Hendriks, A. Jan; Beusen, Arthur H. W.; Clavreul, Julie; King, Henry; Schipper, Aafke M.
2017-01-01
Quantifying mean annual flow of rivers (MAF) at ungauged sites is essential for assessments of global water supply, ecosystem integrity and water footprints. MAF can be quantified with spatially explicit process-based models, which might be overly time-consuming and data-intensive for this purpose, or with empirical regression models that predict MAF based on climate and catchment characteristics. Yet, regression models have mostly been developed at a regional scale and the extent to which they can be extrapolated to other regions is not known. In this study, we developed a global-scale regression model for MAF based on a dataset unprecedented in size, using observations of discharge and catchment characteristics from 1885 catchments worldwide, measuring between 2 and 106 km2. In addition, we compared the performance of the regression model with the predictive ability of the spatially explicit global hydrological model PCR-GLOBWB by comparing results from both models to independent measurements. We obtained a regression model explaining 89% of the variance in MAF based on catchment area and catchment averaged mean annual precipitation and air temperature, slope and elevation. The regression model performed better than PCR-GLOBWB for the prediction of MAF, as root-mean-square error (RMSE) values were lower (0.29-0.38 compared to 0.49-0.57) and the modified index of agreement (d) was higher (0.80-0.83 compared to 0.72-0.75). Our regression model can be applied globally to estimate MAF at any point of the river network, thus providing a feasible alternative to spatially explicit process-based global hydrological models.
Reflexion on linear regression trip production modelling method for ensuring good model quality
Suprayitno, Hitapriya; Ratnasari, Vita
2017-11-01
Transport Modelling is important. For certain cases, the conventional model still has to be used, in which having a good trip production model is capital. A good model can only be obtained from a good sample. Two of the basic principles of a good sampling is having a sample capable to represent the population characteristics and capable to produce an acceptable error at a certain confidence level. It seems that this principle is not yet quite understood and used in trip production modeling. Therefore, investigating the Trip Production Modelling practice in Indonesia and try to formulate a better modeling method for ensuring the Model Quality is necessary. This research result is presented as follows. Statistics knows a method to calculate span of prediction value at a certain confidence level for linear regression, which is called Confidence Interval of Predicted Value. The common modeling practice uses R2 as the principal quality measure, the sampling practice varies and not always conform to the sampling principles. An experiment indicates that small sample is already capable to give excellent R2 value and sample composition can significantly change the model. Hence, good R2 value, in fact, does not always mean good model quality. These lead to three basic ideas for ensuring good model quality, i.e. reformulating quality measure, calculation procedure, and sampling method. A quality measure is defined as having a good R2 value and a good Confidence Interval of Predicted Value. Calculation procedure must incorporate statistical calculation method and appropriate statistical tests needed. A good sampling method must incorporate random well distributed stratified sampling with a certain minimum number of samples. These three ideas need to be more developed and tested.
Using the Logistic Regression model in supporting decisions of establishing marketing strategies
Directory of Open Access Journals (Sweden)
Cristinel CONSTANTIN
2015-12-01
Full Text Available This paper is about an instrumental research regarding the using of Logistic Regression model for data analysis in marketing research. The decision makers inside different organisation need relevant information to support their decisions regarding the marketing strategies. The data provided by marketing research could be computed in various ways but the multivariate data analysis models can enhance the utility of the information. Among these models we can find the Logistic Regression model, which is used for dichotomous variables. Our research is based on explanation the utility of this model and interpretation of the resulted information in order to help practitioners and researchers to use it in their future investigations
Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi
2012-01-01
The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.
Chen, Baojiang; Qin, Jing
2014-05-10
In statistical analysis, a regression model is needed if one is interested in finding the relationship between a response variable and covariates. When the response depends on the covariate, then it may also depend on the function of this covariate. If one has no knowledge of this functional form but expect for monotonic increasing or decreasing, then the isotonic regression model is preferable. Estimation of parameters for isotonic regression models is based on the pool-adjacent-violators algorithm (PAVA), where the monotonicity constraints are built in. With missing data, people often employ the augmented estimating method to improve estimation efficiency by incorporating auxiliary information through a working regression model. However, under the framework of the isotonic regression model, the PAVA does not work as the monotonicity constraints are violated. In this paper, we develop an empirical likelihood-based method for isotonic regression model to incorporate the auxiliary information. Because the monotonicity constraints still hold, the PAVA can be used for parameter estimation. Simulation studies demonstrate that the proposed method can yield more efficient estimates, and in some situations, the efficiency improvement is substantial. We apply this method to a dementia study. Copyright © 2013 John Wiley & Sons, Ltd.
International Nuclear Information System (INIS)
Fang, Xiande; Xu, Yu
2011-01-01
The empirical model of turbine efficiency is necessary for the control- and/or diagnosis-oriented simulation and useful for the simulation and analysis of dynamic performances of the turbine equipment and systems, such as air cycle refrigeration systems, power plants, turbine engines, and turbochargers. Existing empirical models of turbine efficiency are insufficient because there is no suitable form available for air cycle refrigeration turbines. This work performs a critical review of empirical models (called mean value models in some literature) of turbine efficiency and develops an empirical model in the desired form for air cycle refrigeration, the dominant cooling approach in aircraft environmental control systems. The Taylor series and regression analysis are used to build the model, with the Taylor series being used to expand functions with the polytropic exponent and the regression analysis to finalize the model. The measured data of a turbocharger turbine and two air cycle refrigeration turbines are used for the regression analysis. The proposed model is compact and able to present the turbine efficiency map. Its predictions agree with the measured data very well, with the corrected coefficient of determination R c 2 ≥ 0.96 and the mean absolute percentage deviation = 1.19% for the three turbines. -- Highlights: → Performed a critical review of empirical models of turbine efficiency. → Developed an empirical model in the desired form for air cycle refrigeration, using the Taylor expansion and regression analysis. → Verified the method for developing the empirical model. → Verified the model.
Yan, Jun; Aseltine, Robert H., Jr.; Harel, Ofer
2013-01-01
Comparing regression coefficients between models when one model is nested within another is of great practical interest when two explanations of a given phenomenon are specified as linear models. The statistical problem is whether the coefficients associated with a given set of covariates change significantly when other covariates are added into…
Structured Additive Regression Models: An R Interface to BayesX
Directory of Open Access Journals (Sweden)
Nikolaus Umlauf
2015-02-01
Full Text Available Structured additive regression (STAR models provide a flexible framework for model- ing possible nonlinear effects of covariates: They contain the well established frameworks of generalized linear models and generalized additive models as special cases but also allow a wider class of effects, e.g., for geographical or spatio-temporal data, allowing for specification of complex and realistic models. BayesX is standalone software package providing software for fitting general class of STAR models. Based on a comprehensive open-source regression toolbox written in C++, BayesX uses Bayesian inference for estimating STAR models based on Markov chain Monte Carlo simulation techniques, a mixed model representation of STAR models, or stepwise regression techniques combining penalized least squares estimation with model selection. BayesX not only covers models for responses from univariate exponential families, but also models from less-standard regression situations such as models for multi-categorical responses with either ordered or unordered categories, continuous time survival data, or continuous time multi-state models. This paper presents a new fully interactive R interface to BayesX: the R package R2BayesX. With the new package, STAR models can be conveniently specified using Rs formula language (with some extended terms, fitted using the BayesX binary, represented in R with objects of suitable classes, and finally printed/summarized/plotted. This makes BayesX much more accessible to users familiar with R and adds extensive graphics capabilities for visualizing fitted STAR models. Furthermore, R2BayesX complements the already impressive capabilities for semiparametric regression in R by a comprehensive toolbox comprising in particular more complex response types and alternative inferential procedures such as simulation-based Bayesian inference.
Nagel-Alne, G E; Krontveit, R; Bohlin, J; Valle, P S; Skjerve, E; Sølverød, L S
2014-07-01
In 2001, the Norwegian Goat Health Service initiated the Healthier Goats program (HG), with the aim of eradicating caprine arthritis encephalitis, caseous lymphadenitis, and Johne's disease (caprine paratuberculosis) in Norwegian goat herds. The aim of the present study was to explore how control and eradication of the above-mentioned diseases by enrolling in HG affected milk yield by comparison with herds not enrolled in HG. Lactation curves were modeled using a multilevel cubic spline regression model where farm, goat, and lactation were included as random effect parameters. The data material contained 135,446 registrations of daily milk yield from 28,829 lactations in 43 herds. The multilevel cubic spline regression model was applied to 4 categories of data: enrolled early, control early, enrolled late, and control late. For enrolled herds, the early and late notations refer to the situation before and after enrolling in HG; for nonenrolled herds (controls), they refer to development over time, independent of HG. Total milk yield increased in the enrolled herds after eradication: the total milk yields in the fourth lactation were 634.2 and 873.3 kg in enrolled early and enrolled late herds, respectively, and 613.2 and 701.4 kg in the control early and control late herds, respectively. Day of peak yield differed between enrolled and control herds. The day of peak yield came on d 6 of lactation for the control early category for parities 2, 3, and 4, indicating an inability of the goats to further increase their milk yield from the initial level. For enrolled herds, on the other hand, peak yield came between d 49 and 56, indicating a gradual increase in milk yield after kidding. Our results indicate that enrollment in the HG disease eradication program improved the milk yield of dairy goats considerably, and that the multilevel cubic spline regression was a suitable model for exploring effects of disease control and eradication on milk yield. Copyright © 2014
Profile-driven regression for modeling and runtime optimization of mobile networks
DEFF Research Database (Denmark)
McClary, Dan; Syrotiuk, Violet; Kulahci, Murat
2010-01-01
Computer networks often display nonlinear behavior when examined over a wide range of operating conditions. There are few strategies available for modeling such behavior and optimizing such systems as they run. Profile-driven regression is developed and applied to modeling and runtime optimization...... of throughput in a mobile ad hoc network, a self-organizing collection of mobile wireless nodes without any fixed infrastructure. The intermediate models generated in profile-driven regression are used to fit an overall model of throughput, and are also used to optimize controllable factors at runtime. Unlike...
DEFF Research Database (Denmark)
Carstensen, Bendix
1996-01-01
This paper shows how to fit excess and relative risk regression models to interval censored survival data, and how to implement the models in standard statistical software. The methods developed are used for the analysis of HIV infection rates in a cohort of Danish homosexual men.......This paper shows how to fit excess and relative risk regression models to interval censored survival data, and how to implement the models in standard statistical software. The methods developed are used for the analysis of HIV infection rates in a cohort of Danish homosexual men....
The Relationship between Economic Growth and Money Laundering – a Linear Regression Model
Directory of Open Access Journals (Sweden)
Daniel Rece
2009-09-01
Full Text Available This study provides an overview of the relationship between economic growth and money laundering modeled by a least squares function. The report analyzes statistically data collected from USA, Russia, Romania and other eleven European countries, rendering a linear regression model. The study illustrates that 23.7% of the total variance in the regressand (level of money laundering is “explained” by the linear regression model. In our opinion, this model will provide critical auxiliary judgment and decision support for anti-money laundering service systems.
Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne
2012-12-01
In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.
A primer for biomedical scientists on how to execute model II linear regression analysis.
Ludbrook, John
2012-04-01
1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.
DEFF Research Database (Denmark)
Tan, Qihua; Bathum, L; Christiansen, L
2003-01-01
In this paper, we apply logistic regression models to measure genetic association with human survival for highly polymorphic and pleiotropic genes. By modelling genotype frequency as a function of age, we introduce a logistic regression model with polytomous responses to handle the polymorphic...... situation. Genotype and allele-based parameterization can be used to investigate the modes of gene action and to reduce the number of parameters, so that the power is increased while the amount of multiple testing minimized. A binomial logistic regression model with fractional polynomials is used to capture...... the age-dependent or antagonistic pleiotropic effects. The models are applied to HFE genotype data to assess the effects on human longevity by different alleles and to detect if an age-dependent effect exists. Application has shown that these methods can serve as useful tools in searching for important...
Modeling Governance KB with CATPCA to Overcome Multicollinearity in the Logistic Regression
Khikmah, L.; Wijayanto, H.; Syafitri, U. D.
2017-04-01
The problem often encounters in logistic regression modeling are multicollinearity problems. Data that have multicollinearity between explanatory variables with the result in the estimation of parameters to be bias. Besides, the multicollinearity will result in error in the classification. In general, to overcome multicollinearity in regression used stepwise regression. They are also another method to overcome multicollinearity which involves all variable for prediction. That is Principal Component Analysis (PCA). However, classical PCA in only for numeric data. Its data are categorical, one method to solve the problems is Categorical Principal Component Analysis (CATPCA). Data were used in this research were a part of data Demographic and Population Survey Indonesia (IDHS) 2012. This research focuses on the characteristic of women of using the contraceptive methods. Classification results evaluated using Area Under Curve (AUC) values. The higher the AUC value, the better. Based on AUC values, the classification of the contraceptive method using stepwise method (58.66%) is better than the logistic regression model (57.39%) and CATPCA (57.39%). Evaluation of the results of logistic regression using sensitivity, shows the opposite where CATPCA method (99.79%) is better than logistic regression method (92.43%) and stepwise (92.05%). Therefore in this study focuses on major class classification (using a contraceptive method), then the selected model is CATPCA because it can raise the level of the major class model accuracy.
Regression analysis understanding and building business and economic models using Excel
Wilson, J Holton
2012-01-01
The technique of regression analysis is used so often in business and economics today that an understanding of its use is necessary for almost everyone engaged in the field. This book will teach you the essential elements of building and understanding regression models in a business/economic context in an intuitive manner. The authors take a non-theoretical treatment that is accessible even if you have a limited statistical background. It is specifically designed to teach the correct use of regression, while advising you of its limitations and teaching about common pitfalls. This book describe
Estimasi Model Seemingly Unrelated Regression (SUR dengan Metode Generalized Least Square (GLS
Directory of Open Access Journals (Sweden)
Ade Widyaningsih
2015-04-01
Full Text Available Regression analysis is a statistical tool that is used to determine the relationship between two or more quantitative variables so that one variable can be predicted from the other variables. A method that can used to obtain a good estimation in the regression analysis is ordinary least squares method. The least squares method is used to estimate the parameters of one or more regression but relationships among the errors in the response of other estimators are not allowed. One way to overcome this problem is Seemingly Unrelated Regression model (SUR in which parameters are estimated using Generalized Least Square (GLS. In this study, the author applies SUR model using GLS method on world gasoline demand data. The author obtains that SUR using GLS is better than OLS because SUR produce smaller errors than the OLS.
Estimasi Model Seemingly Unrelated Regression (SUR dengan Metode Generalized Least Square (GLS
Directory of Open Access Journals (Sweden)
Ade Widyaningsih
2014-06-01
Full Text Available Regression analysis is a statistical tool that is used to determine the relationship between two or more quantitative variables so that one variable can be predicted from the other variables. A method that can used to obtain a good estimation in the regression analysis is ordinary least squares method. The least squares method is used to estimate the parameters of one or more regression but relationships among the errors in the response of other estimators are not allowed. One way to overcome this problem is Seemingly Unrelated Regression model (SUR in which parameters are estimated using Generalized Least Square (GLS. In this study, the author applies SUR model using GLS method on world gasoline demand data. The author obtains that SUR using GLS is better than OLS because SUR produce smaller errors than the OLS.
Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne
2016-04-01
Existing evidence suggests that ambient ultrafine particles (UFPs) (regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.
Kamaruddin, Ainur Amira; Ali, Zalila; Noor, Norlida Mohd.; Baharum, Adam; Ahmad, Wan Muhamad Amir W.
2014-07-01
Logistic regression analysis examines the influence of various factors on a dichotomous outcome by estimating the probability of the event's occurrence. Logistic regression, also called a logit model, is a statistical procedure used to model dichotomous outcomes. In the logit model the log odds of the dichotomous outcome is modeled as a linear combination of the predictor variables. The log odds ratio in logistic regression provides a description of the probabilistic relationship of the variables and the outcome. In conducting logistic regression, selection procedures are used in selecting important predictor variables, diagnostics are used to check that assumptions are valid which include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers and a test statistic is calculated to determine the aptness of the model. This study used the binary logistic regression model to investigate overweight and obesity among rural secondary school students on the basis of their demographics profile, medical history, diet and lifestyle. The results indicate that overweight and obesity of students are influenced by obesity in family and the interaction between a student's ethnicity and routine meals intake. The odds of a student being overweight and obese are higher for a student having a family history of obesity and for a non-Malay student who frequently takes routine meals as compared to a Malay student.
Validation of regression models for nitrate concentrations in the upper groundwater in sandy soils
International Nuclear Information System (INIS)
Sonneveld, M.P.W.; Brus, D.J.; Roelsma, J.
2010-01-01
For Dutch sandy regions, linear regression models have been developed that predict nitrate concentrations in the upper groundwater on the basis of residual nitrate contents in the soil in autumn. The objective of our study was to validate these regression models for one particular sandy region dominated by dairy farming. No data from this area were used for calibrating the regression models. The model was validated by additional probability sampling. This sample was used to estimate errors in 1) the predicted areal fractions where the EU standard of 50 mg l -1 is exceeded for farms with low N surpluses (ALT) and farms with higher N surpluses (REF); 2) predicted cumulative frequency distributions of nitrate concentration for both groups of farms. Both the errors in the predicted areal fractions as well as the errors in the predicted cumulative frequency distributions indicate that the regression models are invalid for the sandy soils of this study area. - This study indicates that linear regression models that predict nitrate concentrations in the upper groundwater using residual soil N contents should be applied with care.
A brief introduction to regression designs and mixed-effects modelling by a recent convert
Balling, Laura Winther
2008-01-01
This article discusses the advantages of multiple regression designs over the factorial designs traditionally used in many psycholinguistic experiments. It is shown that regression designs are typically more informative, statistically more powerful and better suited to the analysis of naturalistic tasks. The advantages of including both fixed and random effects are demonstrated with reference to linear mixed-effects models, and problems of collinearity, variable distribution and variable sele...
A computational approach to compare regression modelling strategies in prediction research.
Pajouheshnia, Romin; Pestman, Wiebe R; Teerenstra, Steven; Groenwold, Rolf H H
2016-08-25
It is often unclear which approach to fit, assess and adjust a model will yield the most accurate prediction model. We present an extension of an approach for comparing modelling strategies in linear regression to the setting of logistic regression and demonstrate its application in clinical prediction research. A framework for comparing logistic regression modelling strategies by their likelihoods was formulated using a wrapper approach. Five different strategies for modelling, including simple shrinkage methods, were compared in four empirical data sets to illustrate the concept of a priori strategy comparison. Simulations were performed in both randomly generated data and empirical data to investigate the influence of data characteristics on strategy performance. We applied the comparison framework in a case study setting. Optimal strategies were selected based on the results of a priori comparisons in a clinical data set and the performance of models built according to each strategy was assessed using the Brier score and calibration plots. The performance of modelling strategies was highly dependent on the characteristics of the development data in both linear and logistic regression settings. A priori comparisons in four empirical data sets found that no strategy consistently outperformed the others. The percentage of times that a model adjustment strategy outperformed a logistic model ranged from 3.9 to 94.9 %, depending on the strategy and data set. However, in our case study setting the a priori selection of optimal methods did not result in detectable improvement in model performance when assessed in an external data set. The performance of prediction modelling strategies is a data-dependent process and can be highly variable between data sets within the same clinical domain. A priori strategy comparison can be used to determine an optimal logistic regression modelling strategy for a given data set before selecting a final modelling approach.
Prahutama, Alan; Suparti; Wahyu Utami, Tiani
2018-03-01
Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.
truncSP: An R Package for Estimation of Semi-Parametric Truncated Linear Regression Models
Directory of Open Access Journals (Sweden)
Maria Karlsson
2014-05-01
Full Text Available Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use. Alternative estimators, designed for the estimation of truncated regression models, have been developed. This paper presents the R package truncSP. The package contains functions for the estimation of semi-parametric truncated linear regression models using three different estimators: the symmetrically trimmed least squares, quadratic mode, and left truncated estimators, all of which have been shown to have good asymptotic and ?nite sample properties. The package also provides functions for the analysis of the estimated models. Data from the environmental sciences are used to illustrate the functions in the package.
Modeling and prediction of Turkey's electricity consumption using Support Vector Regression
International Nuclear Information System (INIS)
Kavaklioglu, Kadir
2011-01-01
Support Vector Regression (SVR) methodology is used to model and predict Turkey's electricity consumption. Among various SVR formalisms, ε-SVR method was used since the training pattern set was relatively small. Electricity consumption is modeled as a function of socio-economic indicators such as population, Gross National Product, imports and exports. In order to facilitate future predictions of electricity consumption, a separate SVR model was created for each of the input variables using their current and past values; and these models were combined to yield consumption prediction values. A grid search for the model parameters was performed to find the best ε-SVR model for each variable based on Root Mean Square Error. Electricity consumption of Turkey is predicted until 2026 using data from 1975 to 2006. The results show that electricity consumption can be modeled using Support Vector Regression and the models can be used to predict future electricity consumption. (author)
Improved model of the retardance in citric acid coated ferrofluids using stepwise regression
Lin, J. F.; Qiu, X. R.
2017-06-01
Citric acid (CA) coated Fe3O4 ferrofluids (FFs) have been conducted for biomedical application. The magneto-optical retardance of CA coated FFs was measured by a Stokes polarimeter. Optimization and multiple regression of retardance in FFs were executed by Taguchi method and Microsoft Excel previously, and the F value of regression model was large enough. However, the model executed by Excel was not systematic. Instead we adopted the stepwise regression to model the retardance of CA coated FFs. From the results of stepwise regression by MATLAB, the developed model had highly predictable ability owing to F of 2.55897e+7 and correlation coefficient of one. The average absolute error of predicted retardances to measured retardances was just 0.0044%. Using the genetic algorithm (GA) in MATLAB, the optimized parametric combination was determined as [4.709 0.12 39.998 70.006] corresponding to the pH of suspension, molar ratio of CA to Fe3O4, CA volume, and coating temperature. The maximum retardance was found as 31.712°, close to that obtained by evolutionary solver in Excel and a relative error of -0.013%. Above all, the stepwise regression method was successfully used to model the retardance of CA coated FFs, and the maximum global retardance was determined by the use of GA.
On pseudo-values for regression analysis in competing risks models
DEFF Research Database (Denmark)
Graw, F; Gerds, Thomas Alexander; Schumacher, M
2009-01-01
For regression on state and transition probabilities in multi-state models Andersen et al. (Biometrika 90:15-27, 2003) propose a technique based on jackknife pseudo-values. In this article we analyze the pseudo-values suggested for competing risks models and prove some conjectures regarding their...
A Predictive Logistic Regression Model of World Conflict Using Open Source Data
2015-03-26
No correlation between the error terms and the independent variables 9. Absence of perfect multicollinearity (Menard, 2001) When assumptions are...some of the variables before initial model building. Multicollinearity , or near-linear dependence among the variables will cause problems in the...model. High multicollinearity tends to produce unreasonably high logistic regression coefficients and can result in coefficients that are not
Sample size calculation to externally validate scoring systems based on logistic regression models.
Directory of Open Access Journals (Sweden)
Antonio Palazón-Bru
Full Text Available A sample size containing at least 100 events and 100 non-events has been suggested to validate a predictive model, regardless of the model being validated and that certain factors can influence calibration of the predictive model (discrimination, parameterization and incidence. Scoring systems based on binary logistic regression models are a specific type of predictive model.The aim of this study was to develop an algorithm to determine the sample size for validating a scoring system based on a binary logistic regression model and to apply it to a case study.The algorithm was based on bootstrap samples in which the area under the ROC curve, the observed event probabilities through smooth curves, and a measure to determine the lack of calibration (estimated calibration index were calculated. To illustrate its use for interested researchers, the algorithm was applied to a scoring system, based on a binary logistic regression model, to determine mortality in intensive care units.In the case study provided, the algorithm obtained a sample size with 69 events, which is lower than the value suggested in the literature.An algorithm is provided for finding the appropriate sample size to validate scoring systems based on binary logistic regression models. This could be applied to determine the sample size in other similar cases.
Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.
2006-01-01
Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…
de Vries, S O; Fidler, Vaclav; Kuipers, Wietze D; Hunink, Maria G M
1998-01-01
The purpose of this study was to develop a model that predicts the outcome of supervised exercise for intermittent claudication. The authors present an example of the use of autoregressive logistic regression for modeling observed longitudinal data. Data were collected from 329 participants in a
Endogenous glucose production from infancy to adulthood: a non-linear regression model
Huidekoper, Hidde H.; Ackermans, Mariëtte T.; Ruiter, An F. C.; Sauerwein, Hans P.; Wijburg, Frits A.
2014-01-01
To construct a regression model for endogenous glucose production (EGP) as a function of age, and compare this with glucose supplementation using commonly used dextrose-based saline solutions at fluid maintenance rate in children. A model was constructed based on EGP data, as quantified by
Due to the complexity of the processes contributing to beach bacteria concentrations, many researchers rely on statistical modeling, among which multiple linear regression (MLR) modeling is most widely used. Despite its ease of use and interpretation, there may be time dependence...
Kleijnen, J.P.C.
1995-01-01
This tutorial discusses what-if analysis and optimization of System Dynamics models. These problems are solved, using the statistical techniques of regression analysis and design of experiments (DOE). These issues are illustrated by applying the statistical techniques to a System Dynamics model for
Genomic prediction based on data from three layer lines using non-linear regression models
Huang, H.; Windig, J.J.; Vereijken, A.; Calus, M.P.L.
2014-01-01
Background - Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. Methods - In an attempt to alleviate
Logistic regression models of factors influencing the location of bioenergy and biofuels plants
T.M. Young; R.L. Zaretzki; J.H. Perdue; F.M. Guess; X. Liu
2011-01-01
Logistic regression models were developed to identify significant factors that influence the location of existing wood-using bioenergy/biofuels plants and traditional wood-using facilities. Logistic models provided quantitative insight for variables influencing the location of woody biomass-using facilities. Availability of "thinnings to a basal area of 31.7m2/ha...
Determining factors influencing survival of breast cancer by fuzzy logistic regression model.
Nikbakht, Roya; Bahrampour, Abbas
2017-01-01
Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morphology, grade, age, and treatments (surgery, radiotherapy, and chemotherapy) were applied in the fuzzy logistic regression model. Performance of model was determined in terms of mean degree of membership (MDM). The study results showed that almost 41% of patients were in neoplasm and malignant group and more than two-third of them were still alive after 5-year follow-up. Based on the fuzzy logistic model, the most important factors influencing survival were chemotherapy, morphology, and radiotherapy, respectively. Furthermore, the MDM criteria show that the fuzzy logistic regression have a good fit on the data (MDM = 0.86). Fuzzy logistic regression model showed that chemotherapy is more important than radiotherapy in survival of patients with breast cancer. In addition, another ability of this model is calculating possibilistic odds of survival in cancer patients. The results of this study can be applied in clinical research. Furthermore, there are few studies which applied the fuzzy logistic models. Furthermore, we recommend using this model in various research areas.
Photovoltaic Array Condition Monitoring Based on Online Regression of Performance Model
DEFF Research Database (Denmark)
Spataru, Sergiu; Sera, Dezso; Kerekes, Tamas
2013-01-01
regression modeling, from PV array production, plane-of-array irradiance, and module temperature measurements, acquired during an initial learning phase of the system. After the model has been parameterized automatically, the condition monitoring system enters the normal operation phase, where...
The use of logistic regression in modelling the distributions of bird ...
African Journals Online (AJOL)
The method of logistic regression was used to model the observed geographical distribution patterns of bird species in Swaziland in relation to a set of environmental variables. Reporting rates derived from bird atlas data are used as an index of population densities. This is justified in part by the success of the modelling ...
Time series modeling by a regression approach based on a latent process.
Chamroukhi, Faicel; Samé, Allou; Govaert, Gérard; Aknin, Patrice
2009-01-01
Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process parameters. To evaluate the proposed approach, an experimental study on simulated data and real world data was performed using two alternative approaches: a heteroskedastic piecewise regression model using a global optimization algorithm based on dynamic programming, and a Hidden Markov Regression Model whose parameters are estimated by the Baum-Welch algorithm. Finally, in the context of the remote monitoring of components of the French railway infrastructure, and more particularly the switch mechanism, the proposed approach has been applied to modeling and classifying time series representing the condition measurements acquired during switch operations.
A LATENT CLASS POISSON REGRESSION-MODEL FOR HETEROGENEOUS COUNT DATA
WEDEL, M; DESARBO, WS; BULT, [No Value; RAMASWAMY, [No Value
1993-01-01
In this paper an approach is developed that accommodates heterogeneity in Poisson regression models for count data. The model developed assumes that heterogeneity arises from a distribution of both the intercept and the coefficients of the explanatory variables. We assume that the mixing
The limiting behavior of the estimated parameters in a misspecified random field regression model
DEFF Research Database (Denmark)
Dahl, Christian Møller; Qin, Yu
This paper examines the limiting properties of the estimated parameters in the random field regression model recently proposed by Hamilton (Econometrica, 2001). Though the model is parametric, it enjoys the flexibility of the nonparametric approach since it can approximate a large collection of n...
Deep ensemble learning of sparse regression models for brain disease diagnosis.
Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang
2017-04-01
Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.
Bias and Uncertainty in Regression-Calibrated Models of Groundwater Flow in Heterogeneous Media
DEFF Research Database (Denmark)
Cooley, R.L.; Christensen, Steen
2006-01-01
small. Model error is accounted for in the weighted nonlinear regression methodology developed to estimate θ* and assess model uncertainties by incorporating the second-moment matrix of the model errors into the weight matrix. Techniques developed by statisticians to analyze classical nonlinear...... are reduced in magnitude. Biases, correction factors, and confidence and prediction intervals were obtained for a test problem for which model error is large to test robustness of the methodology. Numerical results conform with the theoretical analysis....
Longitudinal beta regression models for analyzing health-related quality of life scores over time
Directory of Open Access Journals (Sweden)
Hunger Matthias
2012-09-01
Full Text Available Abstract Background Health-related quality of life (HRQL has become an increasingly important outcome parameter in clinical trials and epidemiological research. HRQL scores are typically bounded at both ends of the scale and often highly skewed. Several regression techniques have been proposed to model such data in cross-sectional studies, however, methods applicable in longitudinal research are less well researched. This study examined the use of beta regression models for analyzing longitudinal HRQL data using two empirical examples with distributional features typically encountered in practice. Methods We used SF-6D utility data from a German older age cohort study and stroke-specific HRQL data from a randomized controlled trial. We described the conceptual differences between mixed and marginal beta regression models and compared both models to the commonly used linear mixed model in terms of overall fit and predictive accuracy. Results At any measurement time, the beta distribution fitted the SF-6D utility data and stroke-specific HRQL data better than the normal distribution. The mixed beta model showed better likelihood-based fit statistics than the linear mixed model and respected the boundedness of the outcome variable. However, it tended to underestimate the true mean at the upper part of the distribution. Adjusted group means from marginal beta model and linear mixed model were nearly identical but differences could be observed with respect to standard errors. Conclusions Understanding the conceptual differences between mixed and marginal beta regression models is important for their proper use in the analysis of longitudinal HRQL data. Beta regression fits the typical distribution of HRQL data better than linear mixed models, however, if focus is on estimating group mean scores rather than making individual predictions, the two methods might not differ substantially.
Madarang, Krish J; Kang, Joo-Hyon
2014-06-01
Stormwater runoff has been identified as a source of pollution for the environment, especially for receiving waters. In order to quantify and manage the impacts of stormwater runoff on the environment, predictive models and mathematical models have been developed. Predictive tools such as regression models have been widely used to predict stormwater discharge characteristics. Storm event characteristics, such as antecedent dry days (ADD), have been related to response variables, such as pollutant loads and concentrations. However it has been a controversial issue among many studies to consider ADD as an important variable in predicting stormwater discharge characteristics. In this study, we examined the accuracy of general linear regression models in predicting discharge characteristics of roadway runoff. A total of 17 storm events were monitored in two highway segments, located in Gwangju, Korea. Data from the monitoring were used to calibrate United States Environmental Protection Agency's Storm Water Management Model (SWMM). The calibrated SWMM was simulated for 55 storm events, and the results of total suspended solid (TSS) discharge loads and event mean concentrations (EMC) were extracted. From these data, linear regression models were developed. R(2) and p-values of the regression of ADD for both TSS loads and EMCs were investigated. Results showed that pollutant loads were better predicted than pollutant EMC in the multiple regression models. Regression may not provide the true effect of site-specific characteristics, due to uncertainty in the data. Copyright © 2014 The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.
Adjusting for Confounding in Early Postlaunch Settings: Going Beyond Logistic Regression Models.
Schmidt, Amand F; Klungel, Olaf H; Groenwold, Rolf H H
2016-01-01
Postlaunch data on medical treatments can be analyzed to explore adverse events or relative effectiveness in real-life settings. These analyses are often complicated by the number of potential confounders and the possibility of model misspecification. We conducted a simulation study to compare the performance of logistic regression, propensity score, disease risk score, and stabilized inverse probability weighting methods to adjust for confounding. Model misspecification was induced in the independent derivation dataset. We evaluated performance using relative bias confidence interval coverage of the true effect, among other metrics. At low events per coefficient (1.0 and 0.5), the logistic regression estimates had a large relative bias (greater than -100%). Bias of the disease risk score estimates was at most 13.48% and 18.83%. For the propensity score model, this was 8.74% and >100%, respectively. At events per coefficient of 1.0 and 0.5, inverse probability weighting frequently failed or reduced to a crude regression, resulting in biases of -8.49% and 24.55%. Coverage of logistic regression estimates became less than the nominal level at events per coefficient ≤5. For the disease risk score, inverse probability weighting, and propensity score, coverage became less than nominal at events per coefficient ≤2.5, ≤1.0, and ≤1.0, respectively. Bias of misspecified disease risk score models was 16.55%. In settings with low events/exposed subjects per coefficient, disease risk score methods can be useful alternatives to logistic regression models, especially when propensity score models cannot be used. Despite better performance of disease risk score methods than logistic regression and propensity score models in small events per coefficient settings, bias, and coverage still deviated from nominal.
Caimmi, R.
2011-08-01
Concerning bivariate least squares linear regression, the classical approach pursued for functional models in earlier attempts ( York, 1966, 1969) is reviewed using a new formalism in terms of deviation (matrix) traces which, for unweighted data, reduce to usual quantities leaving aside an unessential (but dimensional) multiplicative factor. Within the framework of classical error models, the dependent variable relates to the independent variable according to the usual additive model. The classes of linear models considered are regression lines in the general case of correlated errors in X and in Y for weighted data, and in the opposite limiting situations of (i) uncorrelated errors in X and in Y, and (ii) completely correlated errors in X and in Y. The special case of (C) generalized orthogonal regression is considered in detail together with well known subcases, namely: (Y) errors in X negligible (ideally null) with respect to errors in Y; (X) errors in Y negligible (ideally null) with respect to errors in X; (O) genuine orthogonal regression; (R) reduced major-axis regression. In the limit of unweighted data, the results determined for functional models are compared with their counterparts related to extreme structural models i.e. the instrumental scatter is negligible (ideally null) with respect to the intrinsic scatter ( Isobe et al., 1990; Feigelson and Babu, 1992). While regression line slope and intercept estimators for functional and structural models necessarily coincide, the contrary holds for related variance estimators even if the residuals obey a Gaussian distribution, with the exception of Y models. An example of astronomical application is considered, concerning the [O/H]-[Fe/H] empirical relations deduced from five samples related to different stars and/or different methods of oxygen abundance determination. For selected samples and assigned methods, different regression models yield consistent results within the errors (∓ σ) for both
Chien, Cheng-Hung; Lin, Chih-Lang; Hu, Ching-Chih; Chang, Jia-Jang; Chien, Rong-Nan
2015-12-01
Patients with chronic hepatitis C virus (HCV) infection are at a greater risk of developing insulin resistance (IR). However, little is known about when insulin sensitivity may improve during or after treatment for hepatitis C. In this study, we examined the effect of combination therapy with pegylated interferon-α and ribavirin on IR in patients with chronic HCV infection. We also analyzed factors associated with changes in insulin sensitivity. IR was estimated by homeostasis model assessment (HOMA-IR). HOMA-IR was measured before therapy, during therapy (12 and 24 weeks), and at the end of therapy (EOT; 24 or 48 weeks). We analyzed 78 HCV patients receiving combination therapy. Twenty-two patients (28.2%) exhibited pretreatment IR (HOMA-IR >2.5). In all patients, HOMA-IR was not significantly different from baseline values at 12 weeks (P = 0.823), 24 weeks (P = 0.417), or at EOT (P = 0.158). In patients with pretreatment IR, a significant decrease in HOMA-IR was observed at 12 weeks (P = 0.023), 24 weeks (P = 0.008), and at EOT (P = 0.002). Multivariate analysis using a logistic regression model showed that baseline HOMA-IR is the only factor associated with the decline in HOMA-IR during and after therapy. The eradication of HCV infection was associated with improved insulin sensitivity among patients with pretreatment IR. This significant improvement in insulin sensitivity may occur as early as 12 weeks after the initiation of antiviral therapy.
Shi, Jinfei; Zhu, Songqing; Chen, Ruwen
2017-12-01
An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.
Construction of risk prediction model of type 2 diabetes mellitus based on logistic regression
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Li Jian
2017-01-01
Full Text Available Objective: to construct multi factor prediction model for the individual risk of T2DM, and to explore new ideas for early warning, prevention and personalized health services for T2DM. Methods: using logistic regression techniques to screen the risk factors for T2DM and construct the risk prediction model of T2DM. Results: Male’s risk prediction model logistic regression equation: logit(P=BMI × 0.735+ vegetables × (−0.671 + age × 0.838+ diastolic pressure × 0.296+ physical activity× (−2.287 + sleep ×(−0.009 +smoking ×0.214; Female’s risk prediction model logistic regression equation: logit(P=BMI ×1.979+ vegetables× (−0.292 + age × 1.355+ diastolic pressure× 0.522+ physical activity × (−2.287 + sleep × (−0.010.The area under the ROC curve of male was 0.83, the sensitivity was 0.72, the specificity was 0.86, the area under the ROC curve of female was 0.84, the sensitivity was 0.75, the specificity was 0.90. Conclusion: This study model data is from a compared study of nested case, the risk prediction model has been established by using the more mature logistic regression techniques, and the model is higher predictive sensitivity, specificity and stability.
Buonaccorsi, John P; Romeo, Giovanni; Thoresen, Magne
2018-03-01
When fitting regression models, measurement error in any of the predictors typically leads to biased coefficients and incorrect inferences. A plethora of methods have been proposed to correct for this. Obtaining standard errors and confidence intervals using the corrected estimators can be challenging and, in addition, there is concern about remaining bias in the corrected estimators. The bootstrap, which is one option to address these problems, has received limited attention in this context. It has usually been employed by simply resampling observations, which, while suitable in some situations, is not always formally justified. In addition, the simple bootstrap does not allow for estimating bias in non-linear models, including logistic regression. Model-based bootstrapping, which can potentially estimate bias in addition to being robust to the original sampling or whether the measurement error variance is constant or not, has received limited attention. However, it faces challenges that are not present in handling regression models with no measurement error. This article develops new methods for model-based bootstrapping when correcting for measurement error in logistic regression with replicate measures. The methodology is illustrated using two examples, and a series of simulations are carried out to assess and compare the simple and model-based bootstrap methods, as well as other standard methods. While not always perfect, the model-based approaches offer some distinct improvements over the other methods. © 2017, The International Biometric Society.
Multiple regression models for energy use in air-conditioned office buildings in different climates
International Nuclear Information System (INIS)
Lam, Joseph C.; Wan, Kevin K.W.; Liu Dalong; Tsang, C.L.
2010-01-01
An attempt was made to develop multiple regression models for office buildings in the five major climates in China - severe cold, cold, hot summer and cold winter, mild, and hot summer and warm winter. A total of 12 key building design variables were identified through parametric and sensitivity analysis, and considered as inputs in the regression models. The coefficient of determination R 2 varies from 0.89 in Harbin to 0.97 in Kunming, indicating that 89-97% of the variations in annual building energy use can be explained by the changes in the 12 parameters. A pseudo-random number generator based on three simple multiplicative congruential generators was employed to generate random designs for evaluation of the regression models. The difference between regression-predicted and DOE-simulated annual building energy use are largely within 10%. It is envisaged that the regression models developed can be used to estimate the likely energy savings/penalty during the initial design stage when different building schemes and design concepts are being considered.
SPSS macros to compare any two fitted values from a regression model.
Weaver, Bruce; Dubois, Sacha
2012-12-01
In regression models with first-order terms only, the coefficient for a given variable is typically interpreted as the change in the fitted value of Y for a one-unit increase in that variable, with all other variables held constant. Therefore, each regression coefficient represents the difference between two fitted values of Y. But the coefficients represent only a fraction of the possible fitted value comparisons that might be of interest to researchers. For many fitted value comparisons that are not captured by any of the regression coefficients, common statistical software packages do not provide the standard errors needed to compute confidence intervals or carry out statistical tests-particularly in more complex models that include interactions, polynomial terms, or regression splines. We describe two SPSS macros that implement a matrix algebra method for comparing any two fitted values from a regression model. The !OLScomp and !MLEcomp macros are for use with models fitted via ordinary least squares and maximum likelihood estimation, respectively. The output from the macros includes the standard error of the difference between the two fitted values, a 95% confidence interval for the difference, and a corresponding statistical test with its p-value.
Testing and Modeling Fuel Regression Rate in a Miniature Hybrid Burner
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Luciano Fanton
2012-01-01
Full Text Available Ballistic characterization of an extended group of innovative HTPB-based solid fuel formulations for hybrid rocket propulsion was performed in a lab-scale burner. An optical time-resolved technique was used to assess the quasisteady regression history of single perforation, cylindrical samples. The effects of metalized additives and radiant heat transfer on the regression rate of such formulations were assessed. Under the investigated operating conditions and based on phenomenological models from the literature, analyses of the collected experimental data show an appreciable influence of the radiant heat flux from burnt gases and soot for both unloaded and loaded fuel formulations. Pure HTPB regression rate data are satisfactorily reproduced, while the impressive initial regression rates of metalized formulations require further assessment.
Regression analysis of informative current status data with the additive hazards model.
Zhao, Shishun; Hu, Tao; Ma, Ling; Wang, Peijie; Sun, Jianguo
2015-04-01
This paper discusses regression analysis of current status failure time data arising from the additive hazards model in the presence of informative censoring. Many methods have been developed for regression analysis of current status data under various regression models if the censoring is noninformative, and also there exists a large literature on parametric analysis of informative current status data in the context of tumorgenicity experiments. In this paper, a semiparametric maximum likelihood estimation procedure is presented and in the method, the copula model is employed to describe the relationship between the failure time of interest and the censoring time. Furthermore, I-splines are used to approximate the nonparametric functions involved and the asymptotic consistency and normality of the proposed estimators are established. A simulation study is conducted and indicates that the proposed approach works well for practical situations. An illustrative example is also provided.
LINEAR REGRESSION MODEL ESTİMATİON FOR RIGHT CENSORED DATA
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Ersin Yılmaz
2016-05-01
Full Text Available In this study, firstly we will define a right censored data. If we say shortly right-censored data is censoring values that above the exact line. This may be related with scaling device. And then we will use response variable acquainted from right-censored explanatory variables. Then the linear regression model will be estimated. For censored data’s existence, Kaplan-Meier weights will be used for the estimation of the model. With the weights regression model will be consistent and unbiased with that. And also there is a method for the censored data that is a semi parametric regression and this method also give useful results for censored data too. This study also might be useful for the health studies because of the censored data used in medical issues generally.
Li, Tao
2018-06-01
The complexity of aluminum electrolysis process leads the temperature for aluminum reduction cells hard to measure directly. However, temperature is the control center of aluminum production. To solve this problem, combining some aluminum plant's practice data, this paper presents a Soft-sensing model of temperature for aluminum electrolysis process on Improved Twin Support Vector Regression (ITSVR). ITSVR eliminates the slow learning speed of Support Vector Regression (SVR) and the over-fit risk of Twin Support Vector Regression (TSVR) by introducing a regularization term into the objective function of TSVR, which ensures the structural risk minimization principle and lower computational complexity. Finally, the model with some other parameters as auxiliary variable, predicts the temperature by ITSVR. The simulation result shows Soft-sensing model based on ITSVR has short time-consuming and better generalization.
Combination of supervised and semi-supervised regression models for improved unbiased estimation
DEFF Research Database (Denmark)
Arenas-Garía, Jeronimo; Moriana-Varo, Carlos; Larsen, Jan
2010-01-01
In this paper we investigate the steady-state performance of semisupervised regression models adjusted using a modified RLS-like algorithm, identifying the situations where the new algorithm is expected to outperform standard RLS. By using an adaptive combination of the supervised and semisupervi......In this paper we investigate the steady-state performance of semisupervised regression models adjusted using a modified RLS-like algorithm, identifying the situations where the new algorithm is expected to outperform standard RLS. By using an adaptive combination of the supervised...
Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm
Ulbrich, Norbert Manfred
2013-01-01
A new regression model search algorithm was developed in 2011 that may be used to analyze both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The new algorithm is a simplified version of a more complex search algorithm that was originally developed at the NASA Ames Balance Calibration Laboratory. The new algorithm has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression models. Therefore, the simplified search algorithm is not intended to replace the original search algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm either fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new regression model search algorithm.
Keat, Sim Chong; Chun, Beh Boon; San, Lim Hwee; Jafri, Mohd Zubir Mat
2015-04-01
Climate change due to carbon dioxide (CO2) emissions is one of the most complex challenges threatening our planet. This issue considered as a great and international concern that primary attributed from different fossil fuels. In this paper, regression model is used for analyzing the causal relationship among CO2 emissions based on the energy consumption in Malaysia using time series data for the period of 1980-2010. The equations were developed using regression model based on the eight major sources that contribute to the CO2 emissions such as non energy, Liquefied Petroleum Gas (LPG), diesel, kerosene, refinery gas, Aviation Turbine Fuel (ATF) and Aviation Gasoline (AV Gas), fuel oil and motor petrol. The related data partly used for predict the regression model (1980-2000) and partly used for validate the regression model (2001-2010). The results of the prediction model with the measured data showed a high correlation coefficient (R2=0.9544), indicating the model's accuracy and efficiency. These results are accurate and can be used in early warning of the population to comply with air quality standards.
Feng, Yongjiu; Tong, Xiaohua
2017-09-22
Defining transition rules is an important issue in cellular automaton (CA)-based land use modeling because these models incorporate highly correlated driving factors. Multicollinearity among correlated driving factors may produce negative effects that must be eliminated from the modeling. Using exploratory regression under pre-defined criteria, we identified all possible combinations of factors from the candidate factors affecting land use change. Three combinations that incorporate five driving factors meeting pre-defined criteria were assessed. With the selected combinations of factors, three logistic regression-based CA models were built to simulate dynamic land use change in Shanghai, China, from 2000 to 2015. For comparative purposes, a CA model with all candidate factors was also applied to simulate the land use change. Simulations using three CA models with multicollinearity eliminated performed better (with accuracy improvements about 3.6%) than the model incorporating all candidate factors. Our results showed that not all candidate factors are necessary for accurate CA modeling and the simulations were not sensitive to changes in statistically non-significant driving factors. We conclude that exploratory regression is an effective method to search for the optimal combinations of driving factors, leading to better land use change models that are devoid of multicollinearity. We suggest identification of dominant factors and elimination of multicollinearity before building land change models, making it possible to simulate more realistic outcomes.
Bruno, Delia Evelina; Barca, Emanuele; Goncalves, Rodrigo Mikosz; de Araujo Queiroz, Heithor Alexandre; Berardi, Luigi; Passarella, Giuseppe
2018-01-01
In this paper, the Evolutionary Polynomial Regression data modelling strategy has been applied to study small scale, short-term coastal morphodynamics, given its capability for treating a wide database of known information, non-linearly. Simple linear and multilinear regression models were also applied to achieve a balance between the computational load and reliability of estimations of the three models. In fact, even though it is easy to imagine that the more complex the model, the more the prediction improves, sometimes a "slight" worsening of estimations can be accepted in exchange for the time saved in data organization and computational load. The models' outcomes were validated through a detailed statistical, error analysis, which revealed a slightly better estimation of the polynomial model with respect to the multilinear model, as expected. On the other hand, even though the data organization was identical for the two models, the multilinear one required a simpler simulation setting and a faster run time. Finally, the most reliable evolutionary polynomial regression model was used in order to make some conjecture about the uncertainty increase with the extension of extrapolation time of the estimation. The overlapping rate between the confidence band of the mean of the known coast position and the prediction band of the estimated position can be a good index of the weakness in producing reliable estimations when the extrapolation time increases too much. The proposed models and tests have been applied to a coastal sector located nearby Torre Colimena in the Apulia region, south Italy.
Accounting for spatial effects in land use regression for urban air pollution modeling.
Bertazzon, Stefania; Johnson, Markey; Eccles, Kristin; Kaplan, Gilaad G
2015-01-01
In order to accurately assess air pollution risks, health studies require spatially resolved pollution concentrations. Land-use regression (LUR) models estimate ambient concentrations at a fine spatial scale. However, spatial effects such as spatial non-stationarity and spatial autocorrelation can reduce the accuracy of LUR estimates by increasing regression errors and uncertainty; and statistical methods for resolving these effects--e.g., spatially autoregressive (SAR) and geographically weighted regression (GWR) models--may be difficult to apply simultaneously. We used an alternate approach to address spatial non-stationarity and spatial autocorrelation in LUR models for nitrogen dioxide. Traditional models were re-specified to include a variable capturing wind speed and direction, and re-fit as GWR models. Mean R(2) values for the resulting GWR-wind models (summer: 0.86, winter: 0.73) showed a 10-20% improvement over traditional LUR models. GWR-wind models effectively addressed both spatial effects and produced meaningful predictive models. These results suggest a useful method for improving spatially explicit models. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
Kondaki, Katerina; Grammatikaki, Evangelia; Jiménez-Pavón, David; De Henauw, Stefaan; González-Gross, Marcela; Sjöstrom, Michael; Gottrand, Frédéric; Molnar, Dénes; Moreno, Luis A; Kafatos, Anthony; Gilbert, Chantal; Kersting, Mathilde; Manios, Yannis
2013-03-01
The present study aimed to evaluate the relationship between the consumption of selected food groups and insulin resistance, with an emphasis on sugar-sweetened beverages (SSB). The present research is a large multicentre European study in adolescents, the HELENA-CSS (Healthy Lifestyle in Europe by Nutrition in Adolescence Cross-Sectional Study). Homeostasis model assessment-insulin resistance index (HOMA-IR) was calculated. Several anthropometric and lifestyle characteristics were recorded. Dietary assessment was conducted by using a short FFQ. The participants were a subset of the original sample (n 546) with complete data on glucose, insulin and FFQ. All participants were recruited at schools. Median (25th, 75th percentile) HOMA-IR was 0.62 (0.44, 0.87). Mean HOMA-IR was significantly higher among adolescents consuming brown bread ≤1 time/week than among those consuming 2-6 times/week (P = 0·011). Mean values of HOMA-IR were also higher in adolescents consuming SSB >5 times/week compared with those consuming less frequently, although a statistically significant difference was detected between those consuming SSB 5-6 times/week and 2-4 times/week (P = 0.049). Multiple linear regression analysis showed that only the frequency of SSB consumption was significantly associated with HOMA-IR after controlling for potential confounders. In particular, it was found that HOMA-IR levels were higher among adolescents consuming SSB 5-6 times/week and ≥1 time/d compared with those consuming ≤1 time/week by 0.281 and 0.191 units, respectively (P = 0.009 and 0.046, respectively). The present study revealed that daily consumption of SSB was related with increased HOMA-IR in adolescents.
Jovanovic, Milos; Radovanovic, Sandro; Vukicevic, Milan; Van Poucke, Sven; Delibasic, Boris
2016-09-01
Quantification and early identification of unplanned readmission risk have the potential to improve the quality of care during hospitalization and after discharge. However, high dimensionality, sparsity, and class imbalance of electronic health data and the complexity of risk quantification, challenge the development of accurate predictive models. Predictive models require a certain level of interpretability in order to be applicable in real settings and create actionable insights. This paper aims to develop accurate and interpretable predictive models for readmission in a general pediatric patient population, by integrating a data-driven model (sparse logistic regression) and domain knowledge based on the international classification of diseases 9th-revision clinical modification (ICD-9-CM) hierarchy of diseases. Additionally, we propose a way to quantify the interpretability of a model and inspect the stability of alternative solutions. The analysis was conducted on >66,000 pediatric hospital discharge records from California, State Inpatient Databases, Healthcare Cost and Utilization Project between 2009 and 2011. We incorporated domain knowledge based on the ICD-9-CM hierarchy in a data driven, Tree-Lasso regularized logistic regression model, providing the framework for model interpretation. This approach was compared with traditional Lasso logistic regression resulting in models that are easier to interpret by fewer high-level diagnoses, with comparable prediction accuracy. The results revealed that the use of a Tree-Lasso model was as competitive in terms of accuracy (measured by area under the receiver operating characteristic curve-AUC) as the traditional Lasso logistic regression, but integration with the ICD-9-CM hierarchy of diseases provided more interpretable models in terms of high-level diagnoses. Additionally, interpretations of models are in accordance with existing medical understanding of pediatric readmission. Best performing models have
Wilson, Barry T.; Knight, Joseph F.; McRoberts, Ronald E.
2018-03-01
Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several methods have previously been developed for use with finer temporal resolution imagery (e.g. AVHRR and MODIS), including image compositing and harmonic regression using Fourier series. The manuscript presents a study, using Minnesota, USA during the years 2009-2013 as the study area and timeframe. The study examined the relative predictive power of land cover models, in particular those related to tree cover, using predictor variables based solely on composite imagery versus those using estimated harmonic regression coefficients. The study used two common non-parametric modeling approaches (i.e. k-nearest neighbors and random forests) for fitting classification and regression models of multiple attributes measured on USFS Forest Inventory and Analysis plots using all available Landsat imagery for the study area and timeframe. The estimated Fourier coefficients developed by harmonic regression of tasseled cap transformation time series data were shown to be correlated with land cover, including tree cover. Regression models using estimated Fourier coefficients as predictor variables showed a two- to threefold increase in explained variance for a small set of continuous response variables, relative to comparable models using monthly image composites. Similarly, the overall accuracies of classification models using the estimated Fourier coefficients were approximately 10-20 percentage points higher than the models using the image composites, with corresponding individual class accuracies between six and 45 percentage points higher.
Accounting for Zero Inflation of Mussel Parasite Counts Using Discrete Regression Models
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Emel Çankaya
2017-06-01
Full Text Available In many ecological applications, the absences of species are inevitable due to either detection faults in samples or uninhabitable conditions for their existence, resulting in high number of zero counts or abundance. Usual practice for modelling such data is regression modelling of log(abundance+1 and it is well know that resulting model is inadequate for prediction purposes. New discrete models accounting for zero abundances, namely zero-inflated regression (ZIP and ZINB, Hurdle-Poisson (HP and Hurdle-Negative Binomial (HNB amongst others are widely preferred to the classical regression models. Due to the fact that mussels are one of the economically most important aquatic products of Turkey, the purpose of this study is therefore to examine the performances of these four models in determination of the significant biotic and abiotic factors on the occurrences of Nematopsis legeri parasite harming the existence of Mediterranean mussels (Mytilus galloprovincialis L.. The data collected from the three coastal regions of Sinop city in Turkey showed more than 50% of parasite counts on the average are zero-valued and model comparisons were based on information criterion. The results showed that the probability of the occurrence of this parasite is here best formulated by ZINB or HNB models and influential factors of models were found to be correspondent with ecological differences of the regions.
Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen
2014-01-01
It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models. PMID:24574916
Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen
2014-01-01
It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models.
International Nuclear Information System (INIS)
Alvarez R, J.T.; Morales P, R.
1992-06-01
The absorbed dose for equivalent soft tissue is determined,it is imparted by ophthalmologic applicators, ( 90 Sr/ 90 Y, 1850 MBq) using an extrapolation chamber of variable electrodes; when estimating the slope of the extrapolation curve using a simple lineal regression model is observed that the dose values are underestimated from 17.7 percent up to a 20.4 percent in relation to the estimate of this dose by means of a regression model polynomial two grade, at the same time are observed an improvement in the standard error for the quadratic model until in 50%. Finally the global uncertainty of the dose is presented, taking into account the reproducibility of the experimental arrangement. As conclusion it can infers that in experimental arrangements where the source is to contact with the extrapolation chamber, it was recommended to substitute the lineal regression model by the quadratic regression model, in the determination of the slope of the extrapolation curve, for more exact and accurate measurements of the absorbed dose. (Author)
Directory of Open Access Journals (Sweden)
Wen-Cheng Wang
2014-01-01
Full Text Available It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models.
Replica analysis of overfitting in regression models for time-to-event data
Coolen, A. C. C.; Barrett, J. E.; Paga, P.; Perez-Vicente, C. J.
2017-09-01
Overfitting, which happens when the number of parameters in a model is too large compared to the number of data points available for determining these parameters, is a serious and growing problem in survival analysis. While modern medicine presents us with data of unprecedented dimensionality, these data cannot yet be used effectively for clinical outcome prediction. Standard error measures in maximum likelihood regression, such as p-values and z-scores, are blind to overfitting, and even for Cox’s proportional hazards model (the main tool of medical statisticians), one finds in literature only rules of thumb on the number of samples required to avoid overfitting. In this paper we present a mathematical theory of overfitting in regression models for time-to-event data, which aims to increase our quantitative understanding of the problem and provide practical tools with which to correct regression outcomes for the impact of overfitting. It is based on the replica method, a statistical mechanical technique for the analysis of heterogeneous many-variable systems that has been used successfully for several decades in physics, biology, and computer science, but not yet in medical statistics. We develop the theory initially for arbitrary regression models for time-to-event data, and verify its predictions in detail for the popular Cox model.
Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors
Directory of Open Access Journals (Sweden)
Xibin Zhang
2016-04-01
Full Text Available This paper develops a sampling algorithm for bandwidth estimation in a nonparametric regression model with continuous and discrete regressors under an unknown error density. The error density is approximated by the kernel density estimator of the unobserved errors, while the regression function is estimated using the Nadaraya-Watson estimator admitting continuous and discrete regressors. We derive an approximate likelihood and posterior for bandwidth parameters, followed by a sampling algorithm. Simulation results show that the proposed approach typically leads to better accuracy of the resulting estimates than cross-validation, particularly for smaller sample sizes. This bandwidth estimation approach is applied to nonparametric regression model of the Australian All Ordinaries returns and the kernel density estimation of gross domestic product (GDP growth rates among the organisation for economic co-operation and development (OECD and non-OECD countries.
INVESTIGATION OF E-MAIL TRAFFIC BY USING ZERO-INFLATED REGRESSION MODELS
Directory of Open Access Journals (Sweden)
Yılmaz KAYA
2012-06-01
Full Text Available Based on count data obtained with a value of zero may be greater than anticipated. These types of data sets should be used to analyze by regression methods taking into account zero values. Zero- Inflated Poisson (ZIP, Zero-Inflated negative binomial (ZINB, Poisson Hurdle (PH, negative binomial Hurdle (NBH are more common approaches in modeling more zero value possessing dependent variables than expected. In the present study, the e-mail traffic of Yüzüncü Yıl University in 2009 spring semester was investigated. ZIP and ZINB, PH and NBH regression methods were applied on the data set because more zeros counting (78.9% were found in data set than expected. ZINB and NBH regression considered zero dispersion and overdispersion were found to be more accurate results due to overdispersion and zero dispersion in sending e-mail. ZINB is determined to be best model accordingto Vuong statistics and information criteria.
Directory of Open Access Journals (Sweden)
Anke Hüls
2017-05-01
Full Text Available Antimicrobial resistance in livestock is a matter of general concern. To develop hygiene measures and methods for resistance prevention and control, epidemiological studies on a population level are needed to detect factors associated with antimicrobial resistance in livestock holdings. In general, regression models are used to describe these relationships between environmental factors and resistance outcome. Besides the study design, the correlation structures of the different outcomes of antibiotic resistance and structural zero measurements on the resistance outcome as well as on the exposure side are challenges for the epidemiological model building process. The use of appropriate regression models that acknowledge these complexities is essential to assure valid epidemiological interpretations. The aims of this paper are (i to explain the model building process comparing several competing models for count data (negative binomial model, quasi-Poisson model, zero-inflated model, and hurdle model and (ii to compare these models using data from a cross-sectional study on antibiotic resistance in animal husbandry. These goals are essential to evaluate which model is most suitable to identify potential prevention measures. The dataset used as an example in our analyses was generated initially to study the prevalence and associated factors for the appearance of cefotaxime-resistant Escherichia coli in 48 German fattening pig farms. For each farm, the outcome was the count of samples with resistant bacteria. There was almost no overdispersion and only moderate evidence of excess zeros in the data. Our analyses show that it is essential to evaluate regression models in studies analyzing the relationship between environmental factors and antibiotic resistances in livestock. After model comparison based on evaluation of model predictions, Akaike information criterion, and Pearson residuals, here the hurdle model was judged to be the most appropriate
Yao, Longbiao; Heuser-Baker, Janet; Herlea-Pana, Oana; Iida, Ryuji; Wang, Qilong; Zou, Ming-Hui; Barlic-Dicen, Jana
2012-01-01
The major event initiating atherosclerosis is hypercholesterolemia-induced disruption of vascular endothelium integrity. In settings of endothelial damage, endothelial progenitor cells (EPCs) are mobilized from bone marrow into circulation and home to sites of vascular injury where they aid endothelial regeneration. Given the beneficial effects of EPCs in vascular repair, we hypothesized that these cells play a pivotal role in atherosclerosis regression. We tested our hypothesis in the atherosclerosis-prone mouse model in which hypercholesterolemia, one of the main factors affecting EPC homeostasis, is reversible (Reversa mice). In these mice normalization of plasma lipids decreased atherosclerotic burden; however, plaque regression was incomplete. To explore whether endothelial progenitors contribute to atherosclerosis regression, bone marrow EPCs from a transgenic strain expressing green fluorescent protein under the control of endothelial cell-specific Tie2 promoter (Tie2-GFP+) were isolated. These cells were then adoptively transferred into atheroregressing Reversa recipients where they augmented plaque regression induced by reversal of hypercholesterolemia. Advanced plaque regression correlated with engraftment of Tie2-GFP+ EPCs into endothelium and resulted in an increase in atheroprotective nitric oxide and improved vascular relaxation. Similarly augmented plaque regression was also detected in regressing Reversa mice treated with the stem cell mobilizer AMD3100 which also mobilizes EPCs to peripheral blood. We conclude that correction of hypercholesterolemia in Reversa mice leads to partial plaque regression that can be augmented by AMD3100 treatment or by adoptive transfer of EPCs. This suggests that direct cell therapy or indirect progenitor cell mobilization therapy may be used in combination with statins to treat atherosclerosis. PMID:23081735
Zhu, K; Lou, Z; Zhou, J; Ballester, N; Kong, N; Parikh, P
2015-01-01
This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". Hospital readmissions raise healthcare costs and cause significant distress to providers and patients. It is, therefore, of great interest to healthcare organizations to predict what patients are at risk to be readmitted to their hospitals. However, current logistic regression based risk prediction models have limited prediction power when applied to hospital administrative data. Meanwhile, although decision trees and random forests have been applied, they tend to be too complex to understand among the hospital practitioners. Explore the use of conditional logistic regression to increase the prediction accuracy. We analyzed an HCUP statewide inpatient discharge record dataset, which includes patient demographics, clinical and care utilization data from California. We extracted records of heart failure Medicare beneficiaries who had inpatient experience during an 11-month period. We corrected the data imbalance issue with under-sampling. In our study, we first applied standard logistic regression and decision tree to obtain influential variables and derive practically meaning decision rules. We then stratified the original data set accordingly and applied logistic regression on each data stratum. We further explored the effect of interacting variables in the logistic regression modeling. We conducted cross validation to assess the overall prediction performance of conditional logistic regression (CLR) and compared it with standard classification models. The developed CLR models outperformed several standard classification models (e.g., straightforward logistic regression, stepwise logistic regression, random forest, support vector machine). For example, the best CLR model improved the classification accuracy by nearly 20% over the straightforward logistic regression model. Furthermore, the developed CLR models tend to achieve better sensitivity of
Application of Soft Computing Techniques and Multiple Regression Models for CBR prediction of Soils
Directory of Open Access Journals (Sweden)
Fatimah Khaleel Ibrahim
2017-08-01
Full Text Available The techniques of soft computing technique such as Artificial Neutral Network (ANN have improved the predicting capability and have actually discovered application in Geotechnical engineering. The aim of this research is to utilize the soft computing technique and Multiple Regression Models (MLR for forecasting the California bearing ratio CBR( of soil from its index properties. The indicator of CBR for soil could be predicted from various soils characterizing parameters with the assist of MLR and ANN methods. The data base that collected from the laboratory by conducting tests on 86 soil samples that gathered from different projects in Basrah districts. Data gained from the experimental result were used in the regression models and soft computing techniques by using artificial neural network. The liquid limit, plastic index , modified compaction test and the CBR test have been determined. In this work, different ANN and MLR models were formulated with the different collection of inputs to be able to recognize their significance in the prediction of CBR. The strengths of the models that were developed been examined in terms of regression coefficient (R2, relative error (RE% and mean square error (MSE values. From the results of this paper, it absolutely was noticed that all the proposed ANN models perform better than that of MLR model. In a specific ANN model with all input parameters reveals better outcomes than other ANN models.
Goodness-of-fit tests and model diagnostics for negative binomial regression of RNA sequencing data.
Mi, Gu; Di, Yanming; Schafer, Daniel W
2015-01-01
This work is about assessing model adequacy for negative binomial (NB) regression, particularly (1) assessing the adequacy of the NB assumption, and (2) assessing the appropriateness of models for NB dispersion parameters. Tools for the first are appropriate for NB regression generally; those for the second are primarily intended for RNA sequencing (RNA-Seq) data analysis. The typically small number of biological samples and large number of genes in RNA-Seq analysis motivate us to address the trade-offs between robustness and statistical power using NB regression models. One widely-used power-saving strategy, for example, is to assume some commonalities of NB dispersion parameters across genes via simple models relating them to mean expression rates, and many such models have been proposed. As RNA-Seq analysis is becoming ever more popular, it is appropriate to make more thorough investigations into power and robustness of the resulting methods, and into practical tools for model assessment. In this article, we propose simulation-based statistical tests and diagnostic graphics to address model adequacy. We provide simulated and real data examples to illustrate that our proposed methods are effective for detecting the misspecification of the NB mean-variance relationship as well as judging the adequacy of fit of several NB dispersion models.
Schmidtmann, I; Elsäßer, A; Weinmann, A; Binder, H
2014-12-30
For determining a manageable set of covariates potentially influential with respect to a time-to-event endpoint, Cox proportional hazards models can be combined with variable selection techniques, such as stepwise forward selection or backward elimination based on p-values, or regularized regression techniques such as component-wise boosting. Cox regression models have also been adapted for dealing with more complex event patterns, for example, for competing risks settings with separate, cause-specific hazard models for each event type, or for determining the prognostic effect pattern of a variable over different landmark times, with one conditional survival model for each landmark. Motivated by a clinical cancer registry application, where complex event patterns have to be dealt with and variable selection is needed at the same time, we propose a general approach for linking variable selection between several Cox models. Specifically, we combine score statistics for each covariate across models by Fisher's method as a basis for variable selection. This principle is implemented for a stepwise forward selection approach as well as for a regularized regression technique. In an application to data from hepatocellular carcinoma patients, the coupled stepwise approach is seen to facilitate joint interpretation of the different cause-specific Cox models. In conditional survival models at landmark times, which address updates of prediction as time progresses and both treatment and other potential explanatory variables may change, the coupled regularized regression approach identifies potentially important, stably selected covariates together with their effect time pattern, despite having only a small number of events. These results highlight the promise of the proposed approach for coupling variable selection between Cox models, which is particularly relevant for modeling for clinical cancer registries with their complex event patterns. Copyright © 2014 John Wiley & Sons
Significance tests to determine the direction of effects in linear regression models.
Wiedermann, Wolfgang; Hagmann, Michael; von Eye, Alexander
2015-02-01
Previous studies have discussed asymmetric interpretations of the Pearson correlation coefficient and have shown that higher moments can be used to decide on the direction of dependence in the bivariate linear regression setting. The current study extends this approach by illustrating that the third moment of regression residuals may also be used to derive conclusions concerning the direction of effects. Assuming non-normally distributed variables, it is shown that the distribution of residuals of the correctly specified regression model (e.g., Y is regressed on X) is more symmetric than the distribution of residuals of the competing model (i.e., X is regressed on Y). Based on this result, 4 one-sample tests are discussed which can be used to decide which variable is more likely to be the response and which one is more likely to be the explanatory variable. A fifth significance test is proposed based on the differences of skewness estimates, which leads to a more direct test of a hypothesis that is compatible with direction of dependence. A Monte Carlo simulation study was performed to examine the behaviour of the procedures under various degrees of associations, sample sizes, and distributional properties of the underlying population. An empirical example is given which illustrates the application of the tests in practice. © 2014 The British Psychological Society.
Suzuki, Makoto; Sugimura, Yuko; Yamada, Sumio; Omori, Yoshitsugu; Miyamoto, Masaaki; Yamamoto, Jun-ichi
2013-01-01
Cognitive disorders in the acute stage of stroke are common and are important independent predictors of adverse outcome in the long term. Despite the impact of cognitive disorders on both patients and their families, it is still difficult to predict the extent or duration of cognitive impairments. The objective of the present study was, therefore, to provide data on predicting the recovery of cognitive function soon after stroke by differential modeling with logarithmic and linear regression. This study included two rounds of data collection comprising 57 stroke patients enrolled in the first round for the purpose of identifying the time course of cognitive recovery in the early-phase group data, and 43 stroke patients in the second round for the purpose of ensuring that the correlation of the early-phase group data applied to the prediction of each individual's degree of cognitive recovery. In the first round, Mini-Mental State Examination (MMSE) scores were assessed 3 times during hospitalization, and the scores were regressed on the logarithm and linear of time. In the second round, calculations of MMSE scores were made for the first two scoring times after admission to tailor the structures of logarithmic and linear regression formulae to fit an individual's degree of functional recovery. The time course of early-phase recovery for cognitive functions resembled both logarithmic and linear functions. However, MMSE scores sampled at two baseline points based on logarithmic regression modeling could estimate prediction of cognitive recovery more accurately than could linear regression modeling (logarithmic modeling, R(2) = 0.676, PLogarithmic modeling based on MMSE scores could accurately predict the recovery of cognitive function soon after the occurrence of stroke. This logarithmic modeling with mathematical procedures is simple enough to be adopted in daily clinical practice.
Climate Impacts on Chinese Corn Yields: A Fractional Polynomial Regression Model
Kooten, van G.C.; Sun, Baojing
2012-01-01
In this study, we examine the effect of climate on corn yields in northern China using data from ten districts in Inner Mongolia and two in Shaanxi province. A regression model with a flexible functional form is specified, with explanatory variables that include seasonal growing degree days,
Estimation of Panel Data Regression Models with Two-Sided Censoring or Truncation
DEFF Research Database (Denmark)
Alan, Sule; Honore, Bo E.; Hu, Luojia
2014-01-01
This paper constructs estimators for panel data regression models with individual speci…fic heterogeneity and two–sided censoring and truncation. Following Powell (1986) the estimation strategy is based on moment conditions constructed from re–censored or re–truncated residuals. While these moment...
Bianca N.I. Eskelson; Hailemariam Temesgen; Tara M. Barrett
2009-01-01
Cavity tree and snag abundance data are highly variable and contain many zero observations. We predict cavity tree and snag abundance from variables that are readily available from forest cover maps or remotely sensed data using negative binomial (NB), zero-inflated NB, and zero-altered NB (ZANB) regression models as well as nearest neighbor (NN) imputation methods....
A random regression model in analysis of litter size in pigs | Lukovi& ...
African Journals Online (AJOL)
Dispersion parameters for number of piglets born alive (NBA) were estimated using a random regression model (RRM). Two data sets of litter records from the Nemščak farm in Slovenia were used for analyses. The first dataset (DS1) included records from the first to the sixth parity. The second dataset (DS2) was extended ...
Digital Repository Service at National Institute of Oceanography (India)
Tripathy, G.R.; Das, Anirban.
used methods, the Least Square Regression (LSR) and Inverse Modeling (IM), to determine the contributions of (i) solutes from different sources to global river water, and (ii) various rocks to a glacial till. The purpose of this exercise is to compare...
Ling, Ru; Liu, Jiawang
2011-12-01
To construct prediction model for health workforce and hospital beds in county hospitals of Hunan by multiple linear regression. We surveyed 16 counties in Hunan with stratified random sampling according to uniform questionnaires,and multiple linear regression analysis with 20 quotas selected by literature view was done. Independent variables in the multiple linear regression model on medical personnels in county hospitals included the counties' urban residents' income, crude death rate, medical beds, business occupancy, professional equipment value, the number of devices valued above 10 000 yuan, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, and utilization rate of hospital beds. Independent variables in the multiple linear regression model on county hospital beds included the the population of aged 65 and above in the counties, disposable income of urban residents, medical personnel of medical institutions in county area, business occupancy, the total value of professional equipment, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, utilization rate of hospital beds, and length of hospitalization. The prediction model shows good explanatory and fitting, and may be used for short- and mid-term forecasting.
Clinical trials: odds ratios and multiple regression models--why and how to assess them
Sobh, Mohamad; Cleophas, Ton J.; Hadj-Chaib, Amel; Zwinderman, Aeilko H.
2008-01-01
Odds ratios (ORs), unlike chi2 tests, provide direct insight into the strength of the relationship between treatment modalities and treatment effects. Multiple regression models can reduce the data spread due to certain patient characteristics and thus improve the precision of the treatment
Susan L. King
2003-01-01
The performance of two classifiers, logistic regression and neural networks, are compared for modeling noncatastrophic individual tree mortality for 21 species of trees in West Virginia. The output of the classifier is usually a continuous number between 0 and 1. A threshold is selected between 0 and 1 and all of the trees below the threshold are classified as...
Fidalgo, Angel M.; Alavi, Seyed Mohammad; Amirian, Seyed Mohammad Reza
2014-01-01
This study examines three controversial aspects in differential item functioning (DIF) detection by logistic regression (LR) models: first, the relative effectiveness of different analytical strategies for detecting DIF; second, the suitability of the Wald statistic for determining the statistical significance of the parameters of interest; and…
Reduction of the number of parameters needed for a polynomial random regression test-day model
Pool, M.H.; Meuwissen, T.H.E.
2000-01-01
Legendre polynomials were used to describe the (co)variance matrix within a random regression test day model. The goodness of fit depended on the polynomial order of fit, i.e., number of parameters to be estimated per animal but is limited by computing capacity. Two aspects: incomplete lactation
Li, Spencer D.
2011-01-01
Mediation analysis in child and adolescent development research is possible using large secondary data sets. This article provides an overview of two statistical methods commonly used to test mediated effects in secondary analysis: multiple regression and structural equation modeling (SEM). Two empirical studies are presented to illustrate the…
Walter, G.M.; Augustin, Th.; Kneib, Thomas; Tutz, Gerhard
2010-01-01
The paper is concerned with Bayesian analysis under prior-data conflict, i.e. the situation when observed data are rather unexpected under the prior (and the sample size is not large enough to eliminate the influence of the prior). Two approaches for Bayesian linear regression modeling based on
DEFF Research Database (Denmark)
Petersen, Jørgen Holm
2016-01-01
This paper describes a new approach to the estimation in a logistic regression model with two crossed random effects where special interest is in estimating the variance of one of the effects while not making distributional assumptions about the other effect. A composite likelihood is studied...
The Development and Demonstration of Multiple Regression Models for Operant Conditioning Questions.
Fanning, Fred; Newman, Isadore
Based on the assumption that inferential statistics can make the operant conditioner more sensitive to possible significant relationships, regressions models were developed to test the statistical significance between slopes and Y intercepts of the experimental and control group subjects. These results were then compared to the traditional operant…
Pivotal statistics for testing subsets of structural parameters in the IV Regression Model
Kleibergen, F.R.
2000-01-01
We construct a novel statistic to test hypothezes on subsets of the structural parameters in anInstrumental Variables (IV) regression model. We derive the chi squared limiting distribution of thestatistic and show that it has a degrees of freedom parameter that is equal to the number ofstructural
The prediction of intelligence in preschool children using alternative models to regression.
Finch, W Holmes; Chang, Mei; Davis, Andrew S; Holden, Jocelyn E; Rothlisberg, Barbara A; McIntosh, David E
2011-12-01
Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of preinjury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically, these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has shown conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The present study had two goals: (1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but have not been frequently explored in the social sciences and (2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Predictions of Stanford-Binet 5 FSIQ scores for preschool-aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression trees provided more accurate predictions of FSIQ scores than does the more traditional regression approach. Implications of these results are discussed.
Kleijnen, J.P.C.
2006-01-01
Classic linear regression models and their concomitant statistical designs assume a univariate response and white noise.By definition, white noise is normally, independently, and identically distributed with zero mean.This survey tries to answer the following questions: (i) How realistic are these
de Peinder, P.; Visser, T.; Wagemans, R.W.P.; Blomberg, J.; Chaabani, H.; Soulimani, F.; Weckhuysen, B.M.
2013-01-01
Research has been carried out to determine the feasibility of partial least-squares regression (PLS) modeling of infrared (IR) spectra of crude oils as a tool for fast sulfur speciation. The study is a continuation of a previously developed method to predict long and short residue properties of
Multiple linear regression models are often used to predict levels of fecal indicator bacteria (FIB) in recreational swimming waters based on independent variables (IVs) such as meteorologic, hydrodynamic, and water-quality measures. The IVs used for these analyses are traditiona...
Regression-based model of skin diffuse reflectance for skin color analysis
Tsumura, Norimichi; Kawazoe, Daisuke; Nakaguchi, Toshiya; Ojima, Nobutoshi; Miyake, Yoichi
2008-11-01
A simple regression-based model of skin diffuse reflectance is developed based on reflectance samples calculated by Monte Carlo simulation of light transport in a two-layered skin model. This reflectance model includes the values of spectral reflectance in the visible spectra for Japanese women. The modified Lambert Beer law holds in the proposed model with a modified mean free path length in non-linear density space. The averaged RMS and maximum errors of the proposed model were 1.1 and 3.1%, respectively, in the above range.
Directory of Open Access Journals (Sweden)
Menon Carlo
2011-09-01
Full Text Available Abstract Background Several regression models have been proposed for estimation of isometric joint torque using surface electromyography (SEMG signals. Common issues related to torque estimation models are degradation of model accuracy with passage of time, electrode displacement, and alteration of limb posture. This work compares the performance of the most commonly used regression models under these circumstances, in order to assist researchers with identifying the most appropriate model for a specific biomedical application. Methods Eleven healthy volunteers participated in this study. A custom-built rig, equipped with a torque sensor, was used to measure isometric torque as each volunteer flexed and extended his wrist. SEMG signals from eight forearm muscles, in addition to wrist joint torque data were gathered during the experiment. Additional data were gathered one hour and twenty-four hours following the completion of the first data gathering session, for the purpose of evaluating the effects of passage of time and electrode displacement on accuracy of models. Acquired SEMG signals were filtered, rectified, normalized and then fed to models for training. Results It was shown that mean adjusted coefficient of determination (Ra2 values decrease between 20%-35% for different models after one hour while altering arm posture decreased mean Ra2 values between 64% to 74% for different models. Conclusions Model estimation accuracy drops significantly with passage of time, electrode displacement, and alteration of limb posture. Therefore model retraining is crucial for preserving estimation accuracy. Data resampling can significantly reduce model training time without losing estimation accuracy. Among the models compared, ordinary least squares linear regression model (OLS was shown to have high isometric torque estimation accuracy combined with very short training times.
Modeling of chemical exergy of agricultural biomass using improved general regression neural network
International Nuclear Information System (INIS)
Huang, Y.W.; Chen, M.Q.; Li, Y.; Guo, J.
2016-01-01
A comprehensive evaluation for energy potential contained in agricultural biomass was a vital step for energy utilization of agricultural biomass. The chemical exergy of typical agricultural biomass was evaluated based on the second law of thermodynamics. The chemical exergy was significantly influenced by C and O elements rather than H element. The standard entropy of the samples also was examined based on their element compositions. Two predicted models of the chemical exergy were developed, which referred to a general regression neural network model based upon the element composition, and a linear model based upon the high heat value. An auto-refinement algorithm was firstly developed to improve the performance of regression neural network model. The developed general regression neural network model with K-fold cross-validation had a better ability for predicting the chemical exergy than the linear model, which had lower predicted errors (±1.5%). - Highlights: • Chemical exergies of agricultural biomass were evaluated based upon fifty samples. • Values for the standard entropy of agricultural biomass samples were calculated. • A linear relationship between chemical exergy and HHV of samples was detected. • An improved GRNN prediction model for the chemical exergy of biomass was developed.
Random regression models for daily feed intake in Danish Duroc pigs
DEFF Research Database (Denmark)
Strathe, Anders Bjerring; Mark, Thomas; Jensen, Just
The objective of this study was to develop random regression models and estimate covariance functions for daily feed intake (DFI) in Danish Duroc pigs. A total of 476201 DFI records were available on 6542 Duroc boars between 70 to 160 days of age. The data originated from the National test station......-year-season, permanent, and animal genetic effects. The functional form was based on Legendre polynomials. A total of 64 models for random regressions were initially ranked by BIC to identify the approximate order for the Legendre polynomials using AI-REML. The parsimonious model included Legendre polynomials of 2nd...... order for genetic and permanent environmental curves and a heterogeneous residual variance, allowing the daily residual variance to change along the age trajectory due to scale effects. The parameters of the model were estimated in a Bayesian framework, using the RJMC module of the DMU package, where...
Use of a Regression Model to Study Host-Genomic Determinants of Phage Susceptibility in MRSA
DEFF Research Database (Denmark)
Zschach, Henrike; Larsen, Mette V; Hasman, Henrik
2018-01-01
strains to 12 (nine monovalent) different therapeutic phage preparations and subsequently employed linear regression models to estimate the influence of individual host gene families on resistance to phages. Specifically, we used a two-step regression model setup with a preselection step based on gene...... family enrichment. We show that our models are robust and capture the data's underlying signal by comparing their performance to that of models build on randomized data. In doing so, we have identified 167 gene families that govern phage resistance in our strain set and performed functional analysis...... on them. This revealed genes of possible prophage or mobile genetic element origin, along with genes involved in restriction-modification and transcription regulators, though the majority were genes of unknown function. This study is a step in the direction of understanding the intricate host...
International Nuclear Information System (INIS)
Hong, W.-C.
2009-01-01
Accurate forecasting of electric load has always been the most important issues in the electricity industry, particularly for developing countries. Due to the various influences, electric load forecasting reveals highly nonlinear characteristics. Recently, support vector regression (SVR), with nonlinear mapping capabilities of forecasting, has been successfully employed to solve nonlinear regression and time series problems. However, it is still lack of systematic approaches to determine appropriate parameter combination for a SVR model. This investigation elucidates the feasibility of applying chaotic particle swarm optimization (CPSO) algorithm to choose the suitable parameter combination for a SVR model. The empirical results reveal that the proposed model outperforms the other two models applying other algorithms, genetic algorithm (GA) and simulated annealing algorithm (SA). Finally, it also provides the theoretical exploration of the electric load forecasting support system (ELFSS)
Directory of Open Access Journals (Sweden)
BUDIMAN
2012-01-01
Full Text Available Budiman, Arisoesilaningsih E. 2012. Predictive model of Amorphophallus muelleri growth in some agroforestry in East Java by multiple regression analysis. Biodiversitas 13: 18-22. The aims of this research was to determine the multiple regression models of vegetative and corm growth of Amorphophallus muelleri Blume in some age variations and habitat conditions of agroforestry in East Java. Descriptive exploratory research method was conducted by systematic random sampling at five agroforestries on four plantations in East Java: Saradan, Bojonegoro, Nganjuk and Blitar. In each agroforestry, we observed A. muelleri vegetative and corm growth on four growing age (1, 2, 3 and 4 years old respectively as well as environmental variables such as altitude, vegetation, climate and soil conditions. Data were analyzed using descriptive statistics to compare A. muelleri habitat in five agroforestries. Meanwhile, the influence and contribution of each environmental variable to the growth of A. muelleri vegetative and corm were determined using multiple regression analysis of SPSS 17.0. The multiple regression models of A. muelleri vegetative and corm growth were generated based on some characteristics of agroforestries and age showed high validity with R2 = 88-99%. Regression model showed that age, monthly temperatures, percentage of radiation and soil calcium (Ca content either simultaneously or partially determined the growth of A. muelleri vegetative and corm. Based on these models, the A. muelleri corm reached the optimal growth after four years of cultivation and they will be ready to be harvested. Additionally, the soil Ca content should reach 25.3 me.hg-1 as Sugihwaras agroforestry, with the maximal radiation of 60%.
Akkus, Zeki; Camdeviren, Handan; Celik, Fatma; Gur, Ali; Nas, Kemal
2005-09-01
To determine the risk factors of osteoporosis using a multiple binary logistic regression method and to assess the risk variables for osteoporosis, which is a major and growing health problem in many countries. We presented a case-control study, consisting of 126 postmenopausal healthy women as control group and 225 postmenopausal osteoporotic women as the case group. The study was carried out in the Department of Physical Medicine and Rehabilitation, Dicle University, Diyarbakir, Turkey between 1999-2002. The data from the 351 participants were collected using a standard questionnaire that contains 43 variables. A multiple logistic regression model was then used to evaluate the data and to find the best regression model. We classified 80.1% (281/351) of the participants using the regression model. Furthermore, the specificity value of the model was 67% (84/126) of the control group while the sensitivity value was 88% (197/225) of the case group. We found the distribution of residual values standardized for final model to be exponential using the Kolmogorow-Smirnow test (p=0.193). The receiver operating characteristic curve was found successful to predict patients with risk for osteoporosis. This study suggests that low levels of dietary calcium intake, physical activity, education, and longer duration of menopause are independent predictors of the risk of low bone density in our population. Adequate dietary calcium intake in combination with maintaining a daily physical activity, increasing educational level, decreasing birth rate, and duration of breast-feeding may contribute to healthy bones and play a role in practical prevention of osteoporosis in Southeast Anatolia. In addition, the findings of the present study indicate that the use of multivariate statistical method as a multiple logistic regression in osteoporosis, which maybe influenced by many variables, is better than univariate statistical evaluation.
A brief introduction to regression designs and mixed-effects modelling by a recent convert
DEFF Research Database (Denmark)
Balling, Laura Winther
2008-01-01
This article discusses the advantages of multiple regression designs over the factorial designs traditionally used in many psycholinguistic experiments. It is shown that regression designs are typically more informative, statistically more powerful and better suited to the analysis of naturalistic...... tasks. The advantages of including both fixed and random effects are demonstrated with reference to linear mixed-effects models, and problems of collinearity, variable distribution and variable selection are discussed. The advantages of these techniques are exemplified in an analysis of a word...
Liu, Pudong; Shi, Runhe; Wang, Hong; Bai, Kaixu; Gao, Wei
2014-10-01
Leaf pigments are key elements for plant photosynthesis and growth. Traditional manual sampling of these pigments is labor-intensive and costly, which also has the difficulty in capturing their temporal and spatial characteristics. The aim of this work is to estimate photosynthetic pigments at large scale by remote sensing. For this purpose, inverse model were proposed with the aid of stepwise multiple linear regression (SMLR) analysis. Furthermore, a leaf radiative transfer model (i.e. PROSPECT model) was employed to simulate the leaf reflectance where wavelength varies from 400 to 780 nm at 1 nm interval, and then these values were treated as the data from remote sensing observations. Meanwhile, simulated chlorophyll concentration (Cab), carotenoid concentration (Car) and their ratio (Cab/Car) were taken as target to build the regression model respectively. In this study, a total of 4000 samples were simulated via PROSPECT with different Cab, Car and leaf mesophyll structures as 70% of these samples were applied for training while the last 30% for model validation. Reflectance (r) and its mathematic transformations (1/r and log (1/r)) were all employed to build regression model respectively. Results showed fair agreements between pigments and simulated reflectance with all adjusted coefficients of determination (R2) larger than 0.8 as 6 wavebands were selected to build the SMLR model. The largest value of R2 for Cab, Car and Cab/Car are 0.8845, 0.876 and 0.8765, respectively. Meanwhile, mathematic transformations of reflectance showed little influence on regression accuracy. We concluded that it was feasible to estimate the chlorophyll and carotenoids and their ratio based on statistical model with leaf reflectance data.
James W. Hardin; Henrik Schmeidiche; Raymond J. Carroll
2003-01-01
This paper discusses and illustrates the method of regression calibration. This is a straightforward technique for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). Discussion will include specified measurement error, measurement error estimated by replicate error-prone proxies, and measurement error estimated by instrumental variables. The discussion focuses on s...
Estimating transmitted waves of floating breakwater using support vector regression model
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Hegde, A.V.; Kumar, V.; Patil, S.G.
is first mapped onto an m-dimensional feature space using some fixed (nonlinear) mapping, and then a linear model is constructed in this feature space (Ivanciuc Ovidiu 2007). Using mathematical notation, the linear model in the feature space f(x, w... regressive vector machines, Ocean Engineering Journal, Vol – 36, pp 339 – 347, 2009. 3. Ivanciuc Ovidiu, Applications of support vector machines in chemistry, Review in Computational Chemistry, Eds K. B. Lipkouitz and T. R. Cundari, Vol – 23...
Nobuoki, Eshima; Minoru, Tabata; Geng, Zhi; Department of Medical Information Analysis, Faculty of Medicine, Oita Medical University; Department of Applied Mathematics, Faculty of Engineering, Kobe University; Department of Probability and Statistics, Peking University
2001-01-01
This paper discusses path analysis of categorical variables with logistic regression models. The total, direct and indirect effects in fully recursive causal systems are considered by using model parameters. These effects can be explained in terms of log odds ratios, uncertainty differences, and an inner product of explanatory variables and a response variable. A study on food choice of alligators as a numerical exampleis reanalysed to illustrate the present approach.
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Samsuri Abdullah
2016-07-01
Full Text Available Air pollution in Peninsular Malaysia is dominated by particulate matter which is demonstrated by having the highest Air Pollution Index (API value compared to the other pollutants at most part of the country. Particulate Matter (PM10 forecasting models development is crucial because it allows the authority and citizens of a community to take necessary actions to limit their exposure to harmful levels of particulates pollution and implement protection measures to significantly improve air quality on designated locations. This study aims in improving the ability of MLR using PCs inputs for PM10 concentrations forecasting. Daily observations for PM10 in Kuala Terengganu, Malaysia from January 2003 till December 2011 were utilized to forecast PM10 concentration levels. MLR and PCR (using PCs input models were developed and the performance was evaluated using RMSE, NAE and IA. Results revealed that PCR performed better than MLR due to the implementation of PCA which reduce intricacy and eliminate data multi-collinearity.
A hydrologic regression sediment-yield model for two ungaged watershed outlet stations in Africa
International Nuclear Information System (INIS)
Moussa, O.M.; Smith, S.E.; Shrestha, R.L.
1991-01-01
A hydrologic regression sediment-yield model was established to determine the relationship between water discharge and suspended sediment discharge at the Blue Nile and the Atbara River outlet stations during the flood season. The model consisted of two main submodels: (1) a suspended sediment discharge model, which was used to determine suspended sediment discharge for each basin outlet; and (2) a sediment rating model, which related water discharge and suspended sediment discharge for each outlet station. Due to the absence of suspended sediment concentration measurements at or near the outlet stations, a minimum norm solution, which is based on the minimization of the unknowns rather than the residuals, was used to determine the suspended sediment discharges at the stations. In addition, the sediment rating submodel was regressed by using an observation equations procedure. Verification analyses on the model were carried out and the mean percentage errors were found to be +12.59 and -12.39, respectively, for the Blue Nile and Atbara. The hydrologic regression model was found to be most sensitive to the relative weight matrix, moderately sensitive to the mean water discharge ratio, and slightly sensitive to the concentration variation along the River Nile's course
Cross-validation pitfalls when selecting and assessing regression and classification models.
Krstajic, Damjan; Buturovic, Ljubomir J; Leahy, David E; Thomas, Simon
2014-03-29
We address the problem of selecting and assessing classification and regression models using cross-validation. Current state-of-the-art methods can yield models with high variance, rendering them unsuitable for a number of practical applications including QSAR. In this paper we describe and evaluate best practices which improve reliability and increase confidence in selected models. A key operational component of the proposed methods is cloud computing which enables routine use of previously infeasible approaches. We describe in detail an algorithm for repeated grid-search V-fold cross-validation for parameter tuning in classification and regression, and we define a repeated nested cross-validation algorithm for model assessment. As regards variable selection and parameter tuning we define two algorithms (repeated grid-search cross-validation and double cross-validation), and provide arguments for using the repeated grid-search in the general case. We show results of our algorithms on seven QSAR datasets. The variation of the prediction performance, which is the result of choosing different splits of the dataset in V-fold cross-validation, needs to be taken into account when selecting and assessing classification and regression models. We demonstrate the importance of repeating cross-validation when selecting an optimal model, as well as the importance of repeating nested cross-validation when assessing a prediction error.
Support vector regression model based predictive control of water level of U-tube steam generators
Energy Technology Data Exchange (ETDEWEB)
Kavaklioglu, Kadir, E-mail: kadir.kavaklioglu@pau.edu.tr
2014-10-15
Highlights: • Water level of U-tube steam generators was controlled in a model predictive fashion. • Models for steam generator water level were built using support vector regression. • Cost function minimization for future optimal controls was performed by using the steepest descent method. • The results indicated the feasibility of the proposed method. - Abstract: A predictive control algorithm using support vector regression based models was proposed for controlling the water level of U-tube steam generators of pressurized water reactors. Steam generator data were obtained using a transfer function model of U-tube steam generators. Support vector regression based models were built using a time series type model structure for five different operating powers. Feedwater flow controls were calculated by minimizing a cost function that includes the level error, the feedwater change and the mismatch between feedwater and steam flow rates. Proposed algorithm was applied for a scenario consisting of a level setpoint change and a steam flow disturbance. The results showed that steam generator level can be controlled at all powers effectively by the proposed method.
Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling.
Kawashima, Issaku; Kumano, Hiroaki
2017-01-01
Mind-wandering (MW), task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG) variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR) to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.
Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling
Directory of Open Access Journals (Sweden)
Issaku Kawashima
2017-07-01
Full Text Available Mind-wandering (MW, task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.
Exploratory regression analysis: a tool for selecting models and determining predictor importance.
Braun, Michael T; Oswald, Frederick L
2011-06-01
Linear regression analysis is one of the most important tools in a researcher's toolbox for creating and testing predictive models. Although linear regression analysis indicates how strongly a set of predictor variables, taken together, will predict a relevant criterion (i.e., the multiple R), the analysis cannot indicate which predictors are the most important. Although there is no definitive or unambiguous method for establishing predictor variable importance, there are several accepted methods. This article reviews those methods for establishing predictor importance and provides a program (in Excel) for implementing them (available for direct download at http://dl.dropbox.com/u/2480715/ERA.xlsm?dl=1) . The program investigates all 2(p) - 1 submodels and produces several indices of predictor importance. This exploratory approach to linear regression, similar to other exploratory data analysis techniques, has the potential to yield both theoretical and practical benefits.
Directory of Open Access Journals (Sweden)
Yuanyuan Yu
2017-12-01
Full Text Available Abstract Background Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Methods Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM were compared. The “do-calculus” was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Results Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal
Yu, Yuanyuan; Li, Hongkai; Sun, Xiaoru; Su, Ping; Wang, Tingting; Liu, Yi; Yuan, Zhongshang; Liu, Yanxun; Xue, Fuzhong
2017-12-28
Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM) were compared. The "do-calculus" was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal strategy was to adjust for the parent nodes of outcome, which
Improving the Prediction of Total Surgical Procedure Time Using Linear Regression Modeling.
Edelman, Eric R; van Kuijk, Sander M J; Hamaekers, Ankie E W; de Korte, Marcel J M; van Merode, Godefridus G; Buhre, Wolfgang F F A
2017-01-01
For efficient utilization of operating rooms (ORs), accurate schedules of assigned block time and sequences of patient cases need to be made. The quality of these planning tools is dependent on the accurate prediction of total procedure time (TPT) per case. In this paper, we attempt to improve the accuracy of TPT predictions by using linear regression models based on estimated surgeon-controlled time (eSCT) and other variables relevant to TPT. We extracted data from a Dutch benchmarking database of all surgeries performed in six academic hospitals in The Netherlands from 2012 till 2016. The final dataset consisted of 79,983 records, describing 199,772 h of total OR time. Potential predictors of TPT that were included in the subsequent analysis were eSCT, patient age, type of operation, American Society of Anesthesiologists (ASA) physical status classification, and type of anesthesia used. First, we computed the predicted TPT based on a previously described fixed ratio model for each record, multiplying eSCT by 1.33. This number is based on the research performed by van Veen-Berkx et al., which showed that 33% of SCT is generally a good approximation of anesthesia-controlled time (ACT). We then systematically tested all possible linear regression models to predict TPT using eSCT in combination with the other available independent variables. In addition, all regression models were again tested without eSCT as a predictor to predict ACT separately (which leads to TPT by adding SCT). TPT was most accurately predicted using a linear regression model based on the independent variables eSCT, type of operation, ASA classification, and type of anesthesia. This model performed significantly better than the fixed ratio model and the method of predicting ACT separately. Making use of these more accurate predictions in planning and sequencing algorithms may enable an increase in utilization of ORs, leading to significant financial and productivity related benefits.
Improving the Prediction of Total Surgical Procedure Time Using Linear Regression Modeling
Directory of Open Access Journals (Sweden)
Eric R. Edelman
2017-06-01
Full Text Available For efficient utilization of operating rooms (ORs, accurate schedules of assigned block time and sequences of patient cases need to be made. The quality of these planning tools is dependent on the accurate prediction of total procedure time (TPT per case. In this paper, we attempt to improve the accuracy of TPT predictions by using linear regression models based on estimated surgeon-controlled time (eSCT and other variables relevant to TPT. We extracted data from a Dutch benchmarking database of all surgeries performed in six academic hospitals in The Netherlands from 2012 till 2016. The final dataset consisted of 79,983 records, describing 199,772 h of total OR time. Potential predictors of TPT that were included in the subsequent analysis were eSCT, patient age, type of operation, American Society of Anesthesiologists (ASA physical status classification, and type of anesthesia used. First, we computed the predicted TPT based on a previously described fixed ratio model for each record, multiplying eSCT by 1.33. This number is based on the research performed by van Veen-Berkx et al., which showed that 33% of SCT is generally a good approximation of anesthesia-controlled time (ACT. We then systematically tested all possible linear regression models to predict TPT using eSCT in combination with the other available independent variables. In addition, all regression models were again tested without eSCT as a predictor to predict ACT separately (which leads to TPT by adding SCT. TPT was most accurately predicted using a linear regression model based on the independent variables eSCT, type of operation, ASA classification, and type of anesthesia. This model performed significantly better than the fixed ratio model and the method of predicting ACT separately. Making use of these more accurate predictions in planning and sequencing algorithms may enable an increase in utilization of ORs, leading to significant financial and productivity related
Suchetana, Bihu; Rajagopalan, Balaji; Silverstein, JoAnn
2017-11-15
A regression tree-based diagnostic approach is developed to evaluate factors affecting US wastewater treatment plant compliance with ammonia discharge permit limits using Discharge Monthly Report (DMR) data from a sample of 106 municipal treatment plants for the period of 2004-2008. Predictor variables used to fit the regression tree are selected using random forests, and consist of the previous month's effluent ammonia, influent flow rates and plant capacity utilization. The tree models are first used to evaluate compliance with existing ammonia discharge standards at each facility and then applied assuming more stringent discharge limits, under consideration in many states. The model predicts that the ability to meet both current and future limits depends primarily on the previous month's treatment performance. With more stringent discharge limits predicted ammonia concentration relative to the discharge limit, increases. In-sample validation shows that the regression trees can provide a median classification accuracy of >70%. The regression tree model is validated using ammonia discharge data from an operating wastewater treatment plant and is able to accurately predict the observed ammonia discharge category approximately 80% of the time, indicating that the regression tree model can be applied to predict compliance for individual treatment plants providing practical guidance for utilities and regulators with an interest in controlling ammonia discharges. The proposed methodology is also used to demonstrate how to delineate reliable sources of demand and supply in a point source-to-point source nutrient credit trading scheme, as well as how planners and decision makers can set reasonable discharge limits in future. Copyright © 2017 Elsevier B.V. All rights reserved.
A review of a priori regression models for warfarin maintenance dose prediction.
Directory of Open Access Journals (Sweden)
Ben Francis
Full Text Available A number of a priori warfarin dosing algorithms, derived using linear regression methods, have been proposed. Although these dosing algorithms may have been validated using patients derived from the same centre, rarely have they been validated using a patient cohort recruited from another centre. In order to undertake external validation, two cohorts were utilised. One cohort formed by patients from a prospective trial and the second formed by patients in the control arm of the EU-PACT trial. Of these, 641 patients were identified as having attained stable dosing and formed the dataset used for validation. Predicted maintenance doses from six criterion fulfilling regression models were then compared to individual patient stable warfarin dose. Predictive ability was assessed with reference to several statistics including the R-square and mean absolute error. The six regression models explained different amounts of variability in the stable maintenance warfarin dose requirements of the patients in the two validation cohorts; adjusted R-squared values ranged from 24.2% to 68.6%. An overview of the summary statistics demonstrated that no one dosing algorithm could be considered optimal. The larger validation cohort from the prospective trial produced more consistent statistics across the six dosing algorithms. The study found that all the regression models performed worse in the validation cohort when compared to the derivation cohort. Further, there was little difference between regression models that contained pharmacogenetic coefficients and algorithms containing just non-pharmacogenetic coefficients. The inconsistency of results between the validation cohorts suggests that unaccounted population specific factors cause variability in dosing algorithm performance. Better methods for dosing that take into account inter- and intra-individual variability, at the initiation and maintenance phases of warfarin treatment, are needed.
A review of a priori regression models for warfarin maintenance dose prediction.
Francis, Ben; Lane, Steven; Pirmohamed, Munir; Jorgensen, Andrea
2014-01-01
A number of a priori warfarin dosing algorithms, derived using linear regression methods, have been proposed. Although these dosing algorithms may have been validated using patients derived from the same centre, rarely have they been validated using a patient cohort recruited from another centre. In order to undertake external validation, two cohorts were utilised. One cohort formed by patients from a prospective trial and the second formed by patients in the control arm of the EU-PACT trial. Of these, 641 patients were identified as having attained stable dosing and formed the dataset used for validation. Predicted maintenance doses from six criterion fulfilling regression models were then compared to individual patient stable warfarin dose. Predictive ability was assessed with reference to several statistics including the R-square and mean absolute error. The six regression models explained different amounts of variability in the stable maintenance warfarin dose requirements of the patients in the two validation cohorts; adjusted R-squared values ranged from 24.2% to 68.6%. An overview of the summary statistics demonstrated that no one dosing algorithm could be considered optimal. The larger validation cohort from the prospective trial produced more consistent statistics across the six dosing algorithms. The study found that all the regression models performed worse in the validation cohort when compared to the derivation cohort. Further, there was little difference between regression models that contained pharmacogenetic coefficients and algorithms containing just non-pharmacogenetic coefficients. The inconsistency of results between the validation cohorts suggests that unaccounted population specific factors cause variability in dosing algorithm performance. Better methods for dosing that take into account inter- and intra-individual variability, at the initiation and maintenance phases of warfarin treatment, are needed.
Directory of Open Access Journals (Sweden)
Jingyi Zhang
2018-06-01
Full Text Available This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF method to estimate ground PM2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM2.5 analysis and prediction.
Zhang, Jingyi; Li, Bin; Chen, Yumin; Chen, Meijie; Fang, Tao; Liu, Yongfeng
2018-06-11
This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM 2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR) models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM 2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM 2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM 2.5 analysis and prediction.
International Nuclear Information System (INIS)
Urrutia, J D; Bautista, L A; Baccay, E B
2014-01-01
The aim of this study was to develop mathematical models for estimating earthquake casualties such as death, number of injured persons, affected families and total cost of damage. To quantify the direct damages from earthquakes to human beings and properties given the magnitude, intensity, depth of focus, location of epicentre and time duration, the regression models were made. The researchers formulated models through regression analysis using matrices and used α = 0.01. The study considered thirty destructive earthquakes that hit the Philippines from the inclusive years 1968 to 2012. Relevant data about these said earthquakes were obtained from Philippine Institute of Volcanology and Seismology. Data on damages and casualties were gathered from the records of National Disaster Risk Reduction and Management Council. This study will be of great value in emergency planning, initiating and updating programs for earthquake hazard reduction in the Philippines, which is an earthquake-prone country.