Diagnostic profiles of acute abdominal pain with multinomial logistic regression
Ohmann, Christian; Franke, Claus; Yang, Qin; Decker, Franz; Verde, Pablo E
2007-01-01
Purpose: Application of multinomial logistic regression for diagnostic support of acute abdominal pain, a diagnostic problem with many differential diagnoses. Methods: The analysis is based on a prospective data base with 2280 patients with acute abdominal pain, characterized by 87 variables from history and clinical examination and 12 differential diagnoses. Associations between single variables from history and clinical examination and the final diagnoses were investigated with multinomial ...
Diagnostic profiles of acute abdominal pain with multinomial logistic regression
Ohmann, Christian
2007-07-01
Full Text Available Purpose: Application of multinomial logistic regression for diagnostic support of acute abdominal pain, a diagnostic problem with many differential diagnoses. Methods: The analysis is based on a prospective data base with 2280 patients with acute abdominal pain, characterized by 87 variables from history and clinical examination and 12 differential diagnoses. Associations between single variables from history and clinical examination and the final diagnoses were investigated with multinomial logistic regression. Results: Exemplarily, the results are presented for the variable rigidity. A statistical significant association was observed for generalized rigidity and the diagnoses appendicitis, bowel obstruction, pancreatitis, perforated ulcer, multiple and other diagnoses and for localized rigidity and appendicitis, diverticulitis, biliary disease and perforated ulcer. Diagnostic profiles were generated by summarizing the statistical significant associations. As an example the diagnostic profile of acute appendicitis is presented. Conclusions: Compared to alternative approaches (e.g. independent Bayes, loglinear model there are advantages for multinomial logistic regression to support complex differential diagnostic problems, provided potential traps are avoided (e.g. α-error, interpretation of odds ratio.
[Language regression to the mother tongue in polyglot patients with acute psychosis].
Heinemann, F; Assion, H J
1996-07-01
Three bilingual patients with schizophrenia are presented, who spoke almost exclusively in their native language during acute episodes of psychosis. Normal use of the foreign language, German, was again possible after remission of the acute symptoms. This phenomenon of regression is similar to speech disorders in patients with aphasia and is discussed with reference to recent biological findings. PMID:8927199
Quantin, C.; Billard, L.; Touati, M.; Andreu, N; Cottin, Y; Zeller, M.; Afonso, F.; Battaglia, G.; Seck, D.; Le Teuff, G; Diday, E.
2011-01-01
International audience Cardiologists are interested in determining whether the type of hospital pathway followed by a patient is predictive of survival. The study objective was to determine whether accounting for hospital pathways in the selection of prognostic factors of one-year survival after acute myocardial infarction AMI provided a more informative analysis than that obtained by the use of a standard regression tree analysis CART method . Information on AMI was collected for 1095 hos...
2009-01-01
In this paper, we study the local asymptotic behavior of the regression spline estimator in the framework of marginal semiparametric model. Similarly to Zhu, Fung and He (2008), we give explicit expression for the asymptotic bias of regression spline estimator for nonparametric function f. Our results also show that the asymptotic bias of the regression spline estimator does not depend on the working covariance matrix, which distinguishes the regression splines from the smoothing splines and the seemingly unrelated kernel. To understand the local bias result of the regression spline estimator, we show that the regression spline estimator can be obtained iteratively by applying the standard weighted least squares regression spline estimator to pseudo-observations. At each iteration, the bias of the estimator is unchanged and only the variance is updated.
Analysis for Regression Model Behavior by Sampling Strategy for Annual Pollutant Load Estimation.
Park, Youn Shik; Engel, Bernie A
2015-11-01
Water quality data are typically collected less frequently than streamflow data due to the cost of collection and analysis, and therefore water quality data may need to be estimated for additional days. Regression models are applicable to interpolate water quality data associated with streamflow data and have come to be extensively used, requiring relatively small amounts of data. There is a need to evaluate how well the regression models represent pollutant loads from intermittent water quality data sets. Both the specific regression model and water quality data frequency are important factors in pollutant load estimation. In this study, nine regression models from the Load Estimator (LOADEST) and one regression model from the Web-based Load Interpolation Tool (LOADIN) were evaluated with subsampled water quality data sets from daily measured water quality data sets for N, P, and sediment. Each water quality parameter had different correlations with streamflow, and the subsampled water quality data sets had various proportions of storm samples. The behaviors of the regression models differed not only by water quality parameter but also by proportion of storm samples. The regression models from LOADEST provided accurate and precise annual sediment and P load estimates using the water quality data of 20 to 40% storm samples. LOADIN provided more accurate and precise annual N load estimates than LOADEST. In addition, the results indicate that avoidance of water quality data extrapolation and availability of water quality data from storm events were crucial in annual pollutant load estimation using pollutant regression models. PMID:26641336
Green, Michael; Björk, Jonas; Hansen, Jakob; Ekelund, Ulf; Edenbrandt, Lars; Ohlsson, Mattias
2006-01-01
Summary Objective Patients with suspicion of acute coronary syndrome (ACS) are difficult to diagnose and they represent a very heterogeneous group. Some require immediate treatment while others, with only minor disorders, may be sent home. Detecting ACS patients using a machine learning approach would be advantageous in many situations. Methods and materials Artificial neural network (ANN) ensembles and logistic regression models were trained on data from 634 patients pres...
Purpose: Diagnosis of right ventricular dysfunction in patients with acute pulmonary embolism (PE) is known to be associated with increased risk of mortality. The aim of the study was to calculate a logistic regression model for reliable identification of right ventricular dysfunction (RVD) in patients diagnosed with computed tomography pulmonary angiography. Material and methods: Ninety-seven consecutive patients with acute pulmonary embolism were divided into groups with and without RVD basing upon echocardiographic measurement of pulmonary artery systolic pressure (PASP). PE severity was graded with the pulmonary obstruction score. CT measurements of heart chambers and mediastinal vessels were performed; position of interventricular septum and presence of contrast reflux into the inferior vena cava were also recorded. The logistic regression model was prepared by means of stepwise logistic regression. Results: Among the used parameters, the final model consisted of pulmonary obstruction score, short axis diameter of right ventricle and diameter of inferior vena cava. The calculated model is characterized by 79% sensitivity and 81% specificity, and its performance was significantly better than single CT-based measurements. Conclusion: Logistic regression model identifies RVD significantly better, than single CT-based measurements
Staskiewicz, Grzegorz, E-mail: grzegorz.staskiewicz@gmail.com [1st Department of Radiology, Medical University of Lublin, Lublin (Poland); Department of Human Anatomy, Medical University of Lublin, Lublin (Poland); Czekajska-Chehab, Elżbieta, E-mail: czekajska@gazeta.pl [1st Department of Radiology, Medical University of Lublin, Lublin (Poland); Uhlig, Sebastian, E-mail: uhligs@eranet.pl [1st Department of Radiology, Medical University of Lublin, Lublin (Poland); Przegalinski, Jerzy, E-mail: jerzy.przegalinski@umlub.pl [Department of Cardiology, Medical University of Lublin, Lublin (Poland); Maciejewski, Ryszard, E-mail: maciejewski.r@gmail.com [Department of Human Anatomy, Medical University of Lublin, Lublin (Poland); Drop, Andrzej, E-mail: andrzej.drop@umlub.pl [1st Department of Radiology, Medical University of Lublin, Lublin (Poland)
2013-08-15
Purpose: Diagnosis of right ventricular dysfunction in patients with acute pulmonary embolism (PE) is known to be associated with increased risk of mortality. The aim of the study was to calculate a logistic regression model for reliable identification of right ventricular dysfunction (RVD) in patients diagnosed with computed tomography pulmonary angiography. Material and methods: Ninety-seven consecutive patients with acute pulmonary embolism were divided into groups with and without RVD basing upon echocardiographic measurement of pulmonary artery systolic pressure (PASP). PE severity was graded with the pulmonary obstruction score. CT measurements of heart chambers and mediastinal vessels were performed; position of interventricular septum and presence of contrast reflux into the inferior vena cava were also recorded. The logistic regression model was prepared by means of stepwise logistic regression. Results: Among the used parameters, the final model consisted of pulmonary obstruction score, short axis diameter of right ventricle and diameter of inferior vena cava. The calculated model is characterized by 79% sensitivity and 81% specificity, and its performance was significantly better than single CT-based measurements. Conclusion: Logistic regression model identifies RVD significantly better, than single CT-based measurements.
Regression rate behaviors of HTPB-based propellant combinations for hybrid rocket motor
Sun, Xingliang; Tian, Hui; Li, Yuelong; Yu, Nanjia; Cai, Guobiao
2016-02-01
The purpose of this paper is to characterize the regression rate behavior of hybrid rocket motor propellant combinations, using hydrogen peroxide (HP), gaseous oxygen (GOX), nitrous oxide (N2O) as the oxidizer and hydroxyl-terminated poly-butadiene (HTPB) as the based fuel. In order to complete this research by experiment and simulation, a hybrid rocket motor test system and a numerical simulation model are established. Series of hybrid rocket motor firing tests are conducted burning different propellant combinations, and several of those are used as references for numerical simulations. The numerical simulation model is developed by combining the Navies-Stokes equations with the turbulence model, one-step global reaction model, and solid-gas coupling model. The distribution of regression rate along the axis is determined by applying simulation mode to predict the combustion process and heat transfer inside the hybrid rocket motor. The time-space averaged regression rate has a good agreement between the numerical value and experimental data. The results indicate that the N2O/HTPB and GOX/HTPB propellant combinations have a higher regression rate, since the enhancement effect of latter is significant due to its higher flame temperature. Furthermore, the containing of aluminum (Al) and/or ammonium perchlorate(AP) in the grain does enhance the regression rate, mainly due to the more energy released inside the chamber and heat feedback to the grain surface by the aluminum combustion.
Maternal heavy alcohol use and toddler behavior problems: a fixed effects regression analysis
Knudsen, Ann Kristin; Ystrøm, Eivind; Skogen, Jens Christoffer; Torgersen, Leila
2015-01-01
Using data from the longitudinal Norwegian Mother and Child Cohort Study, the aims of the current study were to examine associations between postnatal maternal heavy alcohol use and toddler behavior problems, taking both observed and unobserved confounding factors into account by employing fixed effects regression models. Postnatal maternal heavy alcohol use (defined as drinking alcohol 4 or more times a week, or drinking 7 units or more per alcohol use episode) and toddler internalizing and ...
Mogensen, T; Scott, N B; Lund, Claus; Bigler, D; Hjortsø, N C; Kehlet, Henrik
1988-01-01
-point scale) were assessed hourly for 16 hours during continuous epidural infusion of 0.5% plain bupivacaine (8 ml/hr) in 12 patients with chronic nonsurgical pain and in 30 patients after major abdominal surgery performed under combined bupivacaine and halothane--N2O general anesthesia. No opiates were given......The purpose of this study was to investigate whether regression of sensory analgesia during constant epidural bupivacaine infusion was different in postoperative patients with acute pain than in patients with chronic nonsurgical pain. Sensory levels of analgesia (to pinprick) and pain (on a five...... than 0.01). Mean duration of sensory blockade was significantly longer (P less than 0.005) in the patients with chronic pain than in surgical patients (13.1 +/- 1.2 and 8.5 +/- 0.7 hours, respectively). Thus, surgical injury hastens regression of sensory analgesia during continuous epidural bupivacaine...
Welten, Carlijn C M; Koeter, Maarten W J; Wohlfarth, Tamar D; Storosum, Jitschak G; van den Brink, Wim; Gispen-de Wied, Christine C; Leufkens, Hubert G M; Denys, Damiaan A J P
2016-02-01
Patients having an acute manic episode of bipolar disorder often lack insight into their condition. Because little is known about the possible effect of insight on treatment efficacy, we examined whether insight at the start of treatment affects the efficacy of antipsychotic treatment in patients with acute mania. We used individual patient data from 7 randomized, double-blind, placebo-controlled registration studies of 4 antipsychotics in patients with acute mania (N = 1904). Insight was measured with item 11 of the Young Mania Rating Scale (YMRS) at baseline and study endpoint 3 weeks later. Treatment outcome was defined by (a) mean change score, (b) response defined as 50% or more improvement on YMRS, and (c) remission defined as YMRS score less than 8 at study endpoint. We used multilevel mixed effect linear (or logistic) regression analyses of individual patient data to assess the interaction between baseline insight and treatment outcomes. At treatment initiation, 1207 (63.5%) patients had impaired or no insight into their condition. Level of insight significantly modified the efficacy of treatment by mean change score (P = 0.039), response rate (P = 0.033), and remission rate (P = 0.043), with greater improvement in patients with more impaired insight. We therefore recommend that patients experiencing acute mania should be treated immediately and not be delayed until patients regain insight. PMID:26647231
Cohen, Ira L; Liu, Xudong; Hudson, Melissa; Gillis, Jennifer; Cavalari, Rachel N S; Romanczyk, Raymond G; Karmel, Bernard Z; Gardner, Judith M
2016-09-01
In order to improve discrimination accuracy between Autism Spectrum Disorder (ASD) and similar neurodevelopmental disorders, a data mining procedure, Classification and Regression Trees (CART), was used on a large multi-site sample of PDD Behavior Inventory (PDDBI) forms on children with and without ASD. Discrimination accuracy exceeded 80 %, generalized to an independent validation set, and generalized across age groups and sites, and agreed well with ADOS classifications. Parent PDDBIs yielded better results than teacher PDDBIs but, when CART predictions agreed across informants, sensitivity increased. Results also revealed three subtypes of ASD: minimally verbal, verbal, and atypical; and two, relatively common subtypes of non-ASD children: social pragmatic problems and good social skills. These subgroups corresponded to differences in behavior profiles and associated bio-medical findings. PMID:27318809
Fluoxetine and diazepam acutely modulate stress induced-behavior.
Giacomini, Ana Cristina V V; Abreu, Murilo S; Giacomini, Luidia V; Siebel, Anna M; Zimerman, Fernanda F; Rambo, Cassiano L; Mocelin, Ricieri; Bonan, Carla D; Piato, Angelo L; Barcellos, Leonardo J G
2016-01-01
Drug residue contamination in aquatic ecosystems has been studied extensively, but the behavioral effects exerted by the presence of these drugs are not well known. Here, we investigated the effects of acute stress on anxiety, memory, social interaction, and aggressiveness in zebrafish exposed to fluoxetine and diazepam at concentrations that disrupt the hypothalamic-pituitary-interrenal (HPI) axis. Stress increased the locomotor activity and time spent in the bottom area of the tank (novel tank). Fluoxetine and diazepam prevented these behaviors. We also observed that stress and fluoxetine and diazepam exposures decreased social interaction. Stress also increased aggressive behavior, which was not reversed by fluoxetine or diazepam. These data suggest that the presence of these drugs in aquatic ecosystems causes significant behavioral alterations in fish. PMID:26403161
PSYCHOLOGICAL REACTIONS AND HEALTH BEHAVIOR FOLLOWING ACUTE MYOCARDIAL INFARCTION
Tatjana Milenković
2011-06-01
Full Text Available Psychological reactions, risk health behavior and cardiac parameters can influence rehospitalization after acute myocardial infarction.The aim of the paper was to determine the presence of psychological reactions and risk health behavior in patients with acute myocardial infarction on admission as well as the differences after six months.The research included thirty-trhee patients of both sexes, who were consecutively hospitalized due to acute myocardial infarction. A prospective clinical investigation involved the following: semi-structured interview, Mini International Neuropsychiatric Interview (M.I.N.I for pcychiatric disorders, Beck Anxiety Inventory (BAI for measuring the severity of anxiety, Beck Depression Inventory (BDI for measuring the severity of depression, KON-6 sigma test for aggression, Holms-Rahe Scale (H-R for exposure to stressful events, and Health Behavior Questionnaire: alcohol consumption, cigarette smoking, lack of physical activity. Measurement of the same parameters was done on admission and after six months. The differences were assessed using the t-test and chi-square test for p<0.05.On admission, anxiety (BAI=8.15±4.37 and depression (BDI=8.67±3.94 were mild without significant difference after six months in the group of examinees. Aggression was elevated and significantly lowered after six monts (KON-6 sigma =53,26±9, 58:41,42±7.67, t=2,13 for p<0.05. Exposure to stressful events in this period decreased (H-R=113.19±67.37:91,65±63,81, t=3,14 for p<0.05; distribution of physical activity was significantly higher compared to admission values (54.83%: 84.84%. χ2=5.07 for p<0.01.In the group of examinees with acute myocardial infarction in the period of six months, anxiety and depression remained mildly icreased, while the levels of aggression and exposure to stressful events were lowered. Risk health behavior was maintained, except for the improvement in physical activity. In the integrative therapy and
Understanding prehospital delay behavior in acute myocardial infarction in women.
Waller, Cynthia G
2006-12-01
Studies demonstrate that acute myocardial infarction (AMI) mortality can be reduced if reperfusion therapy is initiated within 1 hour of AMI symptom onset. However, a considerable number of men and women arrive at the emergency department outside of the time frame for thrombolytic and angioplasty effectiveness. This is especially true for women who have been shown to delay longer than men due to their prehospital decision-making process utilized. With a mean total delay time greater than 4 hours, the time interval from symptom onset to transport activation to the hospital consumes the majority of the prehospital phase of emergency cardiac care. The health belief model, self-regulation model, theory of reasoned action, and theory of planned behavior have all been used to describe the prehospital decision-making process of both men and women with an AMI and the variables that impact that process. These models have identified the importance of symptom attribution to cardiac-related causes as a target variable for research and interventions related to care-seeking behavior. PMID:18340239
George, Steven Z.; Zeppieri, Giorgio; Cere, Anthony L.; Cere, Melissa R.; Borut, Michael S.; Hodges, Michael J.; Reed, Dalton M.; Valencia, Carolina; Robinson, Michael E.
2008-01-01
Psychological factors consistent with fear-avoidance models are associated with the development of chronic low back pain (LBP). As a result, graded activity (GA) and graded exposure (GX) have been suggested as behavioral treatment options. This clinical trial compared the effectiveness of treatment based classification (TBC) physical therapy alone, to TBC augmented with GA or GX for patients with acute and sub-acute LBP. Our primary hypothesis was that GX would be most effective for those wit...
The limiting behavior of the estimated parameters in a misspecified random field regression model
Dahl, Christian Møller; Qin, Yu
nonlinear functions and it has the added advantage that there is no "curse of dimensionality."Contrary to existing literature on the asymptotic properties of the estimated parameters in random field models our results do not require that the explanatory variables are sampled on a grid. However, as a...... convenient new uniform convergence results that we propose. This theory may have applications beyond those presented here. Our results indicate that classical statistical inference techniques, in general, works very well for random field regression models in finite samples and that these models succesfully...
Mahani, Mohamad Khayatzadeh; Chaloosi, Marzieh; Maragheh, Mohamad Ghanadi; Khanchi, Ali Reza; Afzali, Daryoush
2007-09-01
The oral acute in vivo toxicity of 32 amine and amide drugs was related to their structural-dependent properties. Genetic algorithm-partial least-squares and stepwise variable selection was applied to select of meaningful descriptors. Multiple linear regression (MLR), artificial neural network (ANN) and partial least square (PLS) models were created with selected descriptors. The predictive ability of all three models was evaluated and compared on a set of five drugs, which were not used in modeling steps. Average errors of 0.168, 0.169 and 0.259 were obtained for MLR, ANN and PLS, respectively. PMID:17878584
Acute and Chronic Effects of Cocaine on the Spontaneous Behavior of Pigeons
Pinkston, Jonathan W.; Branch, Marc N.
2010-01-01
The present experiment examined the effects of acute and daily cocaine on spontaneous behavior patterns of pigeons. After determining the acute effects of a range of doses, 9 pigeons were divided into three groups that received one of three doses of cocaine daily, either 1.0, 3.0, or 10.0 mg/kg cocaine. Measures were taken of spontaneous…
Welten, Carlijn C M; Koeter, Maarten W J; Wohlfarth, Tamar D; Storosum, Jitschak G; van den Brink, Wim; Gispen-de Wied, Christine C; Leufkens, Hubert G M; Denys, D.
2016-01-01
Patients having an acute manic episode of bipolar disorder often lack insight into their condition. Because little is known about the possible effect of insight on treatment efficacy, we examined whether insight at the start of treatment affects the efficacy of antipsychotic treatment in patients wi
Naohisa Nakajima
2014-06-01
Conclusions: Additional LDL-C reduction with combination therapy tended to reduce more plaque regression compared to a statin alone in patients with ACS. In diabetic patients, further reduction of LDL-C was associated with a significantly greater reduction in PV.
ACUTE BEHAVIORAL CHANGES IN THE GUPPY (Poecilia reticulata) EXPOSED TO TEMEPHOS
SELVİ, Mahmut; SARIKAYA, Rabia; Erkoç, Figen
2010-01-01
ABSTRACT Temephos is an organophosphorus insecticide used to control mosquito, midge and black fly larvae. This study was aimed to determine the acute toxicity of temephos on behavior of the guppy (Poecilia reticulata). Guppy fish (Poecilia reticulata) were selected for the bioassay experiments. Behavioral changes at each temephos concentration were recorded. The experiments were repeated 3 times. The 96 h acute toxicity range of temephos to adult male guppies was within 10 ...
Selma KARABAŞ
2012-06-01
Full Text Available The purpose of this study is to determine consumer behavior towards organic products in Samsun city center and to determine the factors affacting their preferences of organic products. The study was interviewed with 478 consumers living the city center of Samsun. According of the findings, ease of accesibility of organic products, spouse’s educational level, paying extra for the organic produce, aware of food health benefits, considering the harmful effects of conventional produce, having complete knowledge of organic farming and one unit increase household number income to result increase consumption of organic produce. Consumers were not aware of the certification and control process. As a result, easy of accesibility of organic produces in the supermarkets need to be improved.
Madadi, Mahboubeh; Zhang, Shengfan; Yeary, Karen H Kim; Henderson, Louise M
2014-02-01
We examined the factors associated with screening mammography adherence behaviors and influencing factors on women's attitudes toward mammography in non-adherent women. Design-based logistic regression models were developed to characterize the influencing factors, including socio-demographic, health related, behavioral characteristics, and knowledge of breast cancer/mammography, on women's compliance with and attitudes toward mammography using the 2003 Health Information National Trends Survey data. Findings indicate significant associations among adherence to mammography and marital status, income, health coverage, being advised by a doctor to have a mammogram, having had Pap smear before, perception of chance of getting breast cancer, and knowledge of mammography (frequency of doing mammogram) in both women younger than 65 and women aged 65 and older. However, number of visits to a healthcare provider per year and lifetime number of smoked cigarettes are only significant for women younger than 65. Factors significantly associated with attitudes toward mammography in non-adherent women are age, being advised by a doctor to have a mammogram, and seeking cancer information. To enhance adherence to mammography programs, physicians need to continue to advise their patients to obtain mammograms. In addition, increasing women's knowledge about the frequency and starting age for screening mammography may improve women's adherence. Financially related factors such as income and insurance are also shown to be significant factors. Hence, healthcare policies aimed at providing breast cancer screening services to underserved women will likely enhance mammography participation. PMID:24510010
Executive Function, Coping, and Behavior in Survivors of Childhood Acute Lymphocytic Leukemia*
Campbell, Laura K.; Scaduto, Mary; Van Slyke, Deborah; Niarhos, Frances; Whitlock, James A.; Compas, Bruce E.
2008-01-01
Objective To examine the role of executive function in coping and behavioral outcomes in childhood acute lymphocytic leukemia (ALL) survivors. Methods We examined associations among several domains of executive function (working memory, behavioral inhibition, cognitive flexibility, and self-monitoring), coping, and emotional/behavioral problems in 30 children and adolescents ages 10- to 20-years old who completed treatment for ALL and 30 healthy controls matched on age and sex. Results We fou...
Yulinar Wusanani
2013-05-01
Full Text Available Background Delayed health care-seeking behavior is a cause of high mortality in children due to acute respiratory infections (ARIs. Factors that may affect health care-seeking behavior are socioeconomic status, maternal age, maternal education, parents’ perception of illness, child’s age, number of children under five years of age in the family, and occurrence of natural disasters. The 2006 Central Java earthquake damaged homes and health care facilities, and led to increased poverty among the residents. Objective To assess the relationship between socioeconomic status and mother’s health care-seeking behavior for children under five years of age with ARIs in a post-earthquake setting. Methods This cross-sectional study used secondary data obtained from the Child Health Need Assessment (CHNA survey. Logistic regression test was used to analyze variables that may affect mother’s health care-seeking behavior for children under five years of age with ARIs. Results Of the 665 infants surveyed, 442 infants (66.5% had ARIs. Health care-seeking behavior was good (81.7% in the majority of mothers. We observed that socioeconomic status did not affect maternal health care-seeking behavior for children under five with ARIs (OR 1.33; 95%CI 0.79 to 2.24; P=0.26. Maternal age, maternal education, child’s age and gender, number of children under five in the family, parents’ perceptions of illness and severity of house damage caused by the earthquake also had no effect on maternal health care-seeking behavior for children with ARIs. Conclusion After the 2006 earthquake, we find that socioeconomic status, maternal age, maternal education, child age, child gender, number of children under five in the family, parents’ perceptions of illness, and severity of house damage have no effect on mother’s health care-seeking behavior for their children with ARIs. [Paediatr Indones. 2013;53:144-9.].
Jaime Lynn Speiser
Full Text Available Assessing prognosis for acetaminophen-induced acute liver failure (APAP-ALF patients often presents significant challenges. King's College (KCC has been validated on hospital admission, but little has been published on later phases of illness. We aimed to improve determinations of prognosis both at the time of and following admission for APAP-ALF using Classification and Regression Tree (CART models.CART models were applied to US ALFSG registry data to predict 21-day death or liver transplant early (on admission and post-admission (days 3-7 for 803 APAP-ALF patients enrolled 01/1998-09/2013. Accuracy in prediction of outcome (AC, sensitivity (SN, specificity (SP, and area under receiver-operating curve (AUROC were compared between 3 models: KCC (INR, creatinine, coma grade, pH, CART analysis using only KCC variables (KCC-CART and a CART model using new variables (NEW-CART.Traditional KCC yielded 69% AC, 90% SP, 27% SN, and 0.58 AUROC on admission, with similar performance post-admission. KCC-CART at admission offered predictive 66% AC, 65% SP, 67% SN, and 0.74 AUROC. Post-admission, KCC-CART had predictive 82% AC, 86% SP, 46% SN and 0.81 AUROC. NEW-CART models using MELD (Model for end stage liver disease, lactate and mechanical ventilation on admission yielded predictive 72% AC, 71% SP, 77% SN and AUROC 0.79. For later stages, NEW-CART (MELD, lactate, coma grade offered predictive AC 86%, SP 91%, SN 46%, AUROC 0.73.CARTs offer simple prognostic models for APAP-ALF patients, which have higher AUROC and SN than KCC, with similar AC and negligibly worse SP. Admission and post-admission predictions were developed.• Prognostication in acetaminophen-induced acute liver failure (APAP-ALF is challenging beyond admission • Little has been published regarding the use of King's College Criteria (KCC beyond admission and KCC has shown limited sensitivity in subsequent studies • Classification and Regression Tree (CART methodology allows the
Harris, Breanna N; Perea-Rodriguez, Juan Pablo; Saltzman, Wendy
2011-11-01
Glucocorticoids are thought to mediate the disruption of parental behavior in response to acute and chronic stress. Previous research supports their role in chronic stress; however, no study has experimentally tested the effects of acute glucocorticoid elevation on paternal behavior. We tested the prediction that acute corticosterone (CORT) increases would decrease paternal behavior in California mouse fathers and would lead to longer-term effects on reproductive success, as even short-term increases in CORT have been shown to produce lasting effects on the hypothalamic-pituitary-adrenal axis. First-time fathers were injected with 30 mg/kg CORT, 60 mg/kg CORT or vehicle, or left unmanipulated. Interactions between the male and its pup(s) were recorded 1.5-2h after injection and scored for paternal and non-paternal behavior. Treatment groups were combined into control (unmanipulated + vehicle, n = 15) and CORT (30 mg/kg + 60 mg/kg, n = 16) for analysis based on resulting plasma CORT concentrations. CORT treatment did not alter paternal or non-paternal behaviors or any long-term measures (male body mass or temperature, pup growth rate, pup survival, interbirth interval, number or mass of pups born in the second litter). Fathers showed a significant rise in body mass at day 30 postpartum, followed by a decrease in body mass after the birth of the second litter; however, this pattern did not differ between the CORT and control groups. In summary, acute elevation of plasma CORT did not alter direct paternal behavior, body mass, or reproductive outcomes, suggesting that acute CORT elevation alone does not overtly disrupt paternal care in this biparental mammal. PMID:21939660
Sandro Secutti
2009-09-01
Full Text Available The troglobitic armored catfish, Ancistrus cryptophthalmus (Loricariidae, Ancistrinae is known from four caves in the São Domingos karst area, upper rio Tocantins basin, Central Brazil. These populations differ in general body shape and degree of reduction of eyes and of pigmentation. The small Passa Três population (around 1,000 individuals presents the most reduced eyes, which are not externally visible in adults. A small group of Passa Três catfish, one male and three females, reproduced spontaneously thrice in laboratory, at the end of summertime in 2000, 2003 and 2004. Herein we describe the reproductive behavior during the 2003 event, as well as the early development of the 2003 and 2004 offsprings, with focus on body growth and ontogenetic regression of eyes. The parental care by the male, which includes defense of the rock shelter where the egg clutch is laid, cleaning and oxygenation of eggs, is typical of many loricariids. On the other hand, the slow development, including delayed eye degeneration, low body growth rates and high estimated longevity (15 years or more are characteristic of precocial, or K-selected, life cycles. In the absence of comparable data for close epigean relatives (Ancistrus spp., it is not possible to establish whether these features are an autapomorphic specialization of the troglobitic A. cryptophthalmus or a plesiomorphic trait already present in the epigean ancestor, possibly favoring the adoption of the life in the food-poor cave environment. We briefly discuss the current hypotheses on eye regression in troglobitic vertebrates.O cascudo troglóbio (exclusivamente subterrâneo, Ancistrus cryptophthalmus (Loricariidae, Ancistrinae é conhecido de quatro cavernas na área cárstica de São Domingos, bacia do alto rio Tocantins, Goiás. Estas populações diferem quanto ao formato geral do corpo e grau de redução dos olhos e da pigmentação cutânea. A pequena população encontrada na caverna Passa Tr
OSO paradigm--A rapid behavioral screening method for acute psychosocial stress reactivity in mice.
Brzózka, M M; Unterbarnscheidt, T; Schwab, M H; Rossner, M J
2016-02-01
Chronic psychosocial stress is an important environmental risk factor for the development of psychiatric diseases. However, studying the impact of chronic psychosocial stress in mice is time consuming and thus not optimally suited to 'screen' increasing numbers of genetically manipulated mouse models for psychiatric endophenotypes. Moreover, many studies focus on restraint stress, a strong physical stressor with limited relevance for psychiatric disorders. Here, we describe a simple and a rapid method based on the resident-intruder paradigm to examine acute effects of mild psychosocial stress in mice. The OSO paradigm (open field--social defeat--open field) compares behavioral consequences on locomotor activity, anxiety and curiosity before and after exposure to acute social defeat stress. We first evaluated OSO in male C57Bl/6 wildtype mice where a single episode of social defeat reduced locomotor activity, increased anxiety and diminished exploratory behavior. Subsequently, we applied the OSO paradigm to mouse models of two schizophrenia (SZ) risk genes. Transgenic mice with neuronal overexpression of Neuregulin-1 (Nrg1) type III showed increased risk-taking behavior after acute stress exposure suggesting that NRG1 dysfunction is associated with altered affective behavior. In contrast, Tcf4 transgenic mice displayed a normal stress response which is in line with the postulated predominant contribution of TCF4 to cognitive deficits of SZ. In conclusion, the OSO paradigm allows for rapid screening of selected psychosocial stress-induced behavioral endophenotypes in mouse models of psychiatric diseases. PMID:26628400
Hao, Lingxin
2007-01-01
Quantile Regression, the first book of Hao and Naiman's two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao
HE Wei-ping; HU Jin-hua; ZHAO Jun; TONG Jing-jing; DING Jin-biao; LIN Fang; WANG Hui-fen
2012-01-01
Background Acute-on-chronic hepatitis B liver failure (ACLF-HBV) is a clinically severe disease associated with major life-threatening complications including hepatic encephalopathy and hepatorenal syndrome.The aim of this study was to evaluate the short-term prognostic predictability of the model for end-stage liver disease (MELD),MELD-based indices,and their dynamic changes in patients with ACLF-HBV,and to establish a new model for predicting the prognosis of ACLF-HBV.Methods A total of 172 patients with ACLF-HBV who stayed in the hospital for more than 2 weeks were retrospectively recruited.The predictive accuracy of MELD,MELD-based indices,and their dynamic change (△) were compared using the area under the receiver operating characteristic curve method.The associations between mortality and patient characteristics were studied by univariate and multivariate analyses.Results The 3-month mortality was 43.6％.The largest concordance (c) statistic predicting 3-month mortality was the MELD score at the end of 2 weeks of admission (0.8),followed by the MELD:sodium ratio (MESO) (0.796) and integrated MELD (iMELD) (0.758) scores,△MELD (0.752),△MESO (0.729),and MELD plus sodium (MELD-Na) (0.728) scores.In multivariate Logistic regression analysis,the independent factors predicting prognosis were hepatic encephalopathy (OR=-3.466),serum creatinine,international normalized ratio (INR),and total bilirubin at the end of 2 weeks of admission (OR=10.302,6.063,5.208,respectively),and cholinesterase on admission (OR=0.255).This regression model had a greater prognostic value (c=0.85,95％ Cl 0.791-0.909) compared to the MELD score at the end of 2 weeks of admission (Z=4.9851,P=-0.0256).Conclusions MELD score at the end of 2 weeks of admission is a useful predictor for 3-month mortality in ACLF-HBV patients.Hepatic encephalopathy,serum creatinine,international normalized ratio,and total bilirubin at the end of 2 weeks of admission and cholinesterase on admission are
Acute Systemic Infusion of Bupropion Decrease Formalin Induced Pain Behavior in Rat
Naderi, Somayyeh; Ghaderi Pakdel, Firouz; Ashrafi Osalou, Mostafa; Cankurt, Ulker
2014-01-01
Background The chronic pain can disturb physical, psychological, and social performances. Analgesic agents are widely used but some antidepressants (ADs) showed analgesia also. Bupropion is using for smoke cessation but it can change morphine withdrawal signs such as pain. This study tested the acute systemic effect of bupropion on formalin induced pain behavior in rats. Methods Wistar male healthy rats were divided into 7 groups (control, sham, and 5 treated groups with 10, 30, 90, 120, and ...
ACUTE PRENATAL EXPOSURE TO ETHANOL AND SOCIAL BEHAVIOR: EFFECT OF AGE, SEX, AND TIMING OF EXPOSURE
Mooney, Sandra M.; Varlinskaya, Elena I.
2010-01-01
During development of the central nervous system, neurons pass through critical periods of vulnerability to environmental factors. Exposure to ethanol during gastrulation or during neuronal generation results in a permanent reduction in the number of neurons in trigeminal-associated cranial nerve nuclei. Normal functioning of the trigeminal system is required for social behavior, the present study examined the effects of acute prenatal exposure to ethanol on social interactions across ontogen...
Acute and chronic alcohol dose: population differences in behavior and neurochemistry of zebrafish
Gerlai, R.; Chatterjee, D.; Pereira, T.; Sawashima, T.; Krishnannair, R.
2009-01-01
The zebrafish has been in the forefront of developmental genetics for decades and has also been gaining attention in neurobehavioral genetics. It has been proposed to model alcohol-induced changes in human brain function and behavior. Here, adult zebrafish populations, AB and SF (short-fin wild type), were exposed to chronic treatment (several days in 0.00% or 0.50% alcohol v/v) and a subsequent acute treatment (1 h in 0.00%, 0.25%, 0.50% or 1.00% alcohol). Behavioral responses of zebrafish t...
Kahane, Leo H
2007-01-01
Using a friendly, nontechnical approach, the Second Edition of Regression Basics introduces readers to the fundamentals of regression. Accessible to anyone with an introductory statistics background, this book builds from a simple two-variable model to a model of greater complexity. Author Leo H. Kahane weaves four engaging examples throughout the text to illustrate not only the techniques of regression but also how this empirical tool can be applied in creative ways to consider a broad array of topics. New to the Second Edition Offers greater coverage of simple panel-data estimation:
CONTRASTING BEHAVIORAL EFFECTS OF ACUTE NICOTINE AND CHRONIC SMOKING IN DETOXIFIED ALCOHOLICS
Boissoneault, Jeff; Gilbertson, Rebecca; Prather, Robert; Nixon, Sara Jo
2011-01-01
Background Current literature suggests that acute nicotine administration provides a compensatory mechanism by which alcoholics might alleviate attentional deficits. In contrast, chronic smoking is increasingly recognized as negatively affecting neurobehavioral integrity. These opposing effects have not been simultaneously examined. Thus, we sought to a) extend previous work by exploring the effects of acute nicotine effects on vigilance components of attention and replicate previous findings suggesting that treatment-seeking alcoholics experience benefit to a greater extent than do other groups; and b) to examine the impact of chronic smoking on these tasks and across subgroups. Methods Substance abusing participants (N=86) were recruited and subgrouped on the basis of dependency criteria as either alcoholics, alcoholics with co-morbid stimulant dependence, or stimulant dependent individuals. Groups of cigarette-smoking (N=17) and non-smoking (N=22) community controls were recruited as comparison groups. Smoking subjects were assigned a placebo, low, or high dose nicotine patch in a double-blind placebo controlled fashion. Non-smoking controls were administered either a placebo or low dose. Testing occurred after dose stabilization. Results General linear models indicated greater sensitivity to acute nicotine administration among alcoholics than other groups when controlling for the effect of intensity of smoking history, as reflected by pack-years. Pack-years correlated negatively with performance measures in alcoholics but not stimulant abusing subgroups or smoking controls. Finally, regression analyses demonstrated that pack-years predicted poorer performance only for the alcoholic subgroup. Conclusions These results support previous work finding a compensatory effect of acute nicotine administration on attentional performance in alcoholics and reinforce the consideration of recent nicotine use as a confound in neurocognitive studies of alcoholics. Of
Grégoire, G.
2014-12-01
The logistic regression originally is intended to explain the relationship between the probability of an event and a set of covariables. The model's coefficients can be interpreted via the odds and odds ratio, which are presented in introduction of the chapter. The observations are possibly got individually, then we speak of binary logistic regression. When they are grouped, the logistic regression is said binomial. In our presentation we mainly focus on the binary case. For statistical inference the main tool is the maximum likelihood methodology: we present the Wald, Rao and likelihoods ratio results and their use to compare nested models. The problems we intend to deal with are essentially the same as in multiple linear regression: testing global effect, individual effect, selection of variables to build a model, measure of the fitness of the model, prediction of new values… . The methods are demonstrated on data sets using R. Finally we briefly consider the binomial case and the situation where we are interested in several events, that is the polytomous (multinomial) logistic regression and the particular case of ordinal logistic regression.
Matitaishvili, T; Domianidze, T; Emukhvari, N; Khananashvili, M
2016-03-01
The aim of our research was to study behavioral indices of rats standing on various hierarchical level in the conditions of acute informational stress as well as their resistance to stress taking into account their social status. The Animal's behavior has been studied in conflict and agonist conditions against the background of high food and thirst motivation. After determination of hierarchical relations the stressing procedure of two active avoidance reactions was performed simultaneously during one trial (14 days). During the experiment, behavioral indices of rats induced by stressing procedure were registered. We used "open field" test in order to assess animals' emotional state. The studies performed by us demonstrated behavioral characteristics of animals standing on various hierarchical level. The obtained results showed that after stressing all the animals of the group under stressogenic influence of equal strength, behavior of rats did nor reliably differ in conflict situations. Dominants standing on high hierarchical level remained active in both conflict situations. The impact of stress on their behavior was less detected. Dominant animal maintained its hierarchical status. Submissive rats were more greatly influenced by stress. The obtained results confirmed that dominant animals were characterized with more comprehensively developed self-regulating mechanisms of brain. PMID:27119838
Acute Synthesis of CPEB Is Required for Plasticity of Visual Avoidance Behavior in Xenopus
Wanhua Shen
2014-02-01
Full Text Available Neural plasticity requires protein synthesis, but the identity of newly synthesized proteins generated in response to plasticity-inducing stimuli remains unclear. We used in vivo bio-orthogonal noncanonical amino acid tagging (BONCAT with the methionine analog azidohomoalanine (AHA combined with the multidimensional protein identification technique (MudPIT to identify proteins that are synthesized in the tadpole brain over 24 hr. We induced conditioning-dependent plasticity of visual avoidance behavior, which required N-methyl-D-aspartate (NMDA and Ca2+-permeable α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA receptors, αCaMKII, and rapid protein synthesis. Combining BONCAT with western blots revealed that proteins including αCaMKII, MEK1, CPEB, and GAD65 are synthesized during conditioning. Acute synthesis of CPEB during conditioning is required for behavioral plasticity as well as conditioning-induced synaptic and structural plasticity in the tectal circuit. We outline a signaling pathway that regulates protein-synthesis-dependent behavioral plasticity in intact animals, identify newly synthesized proteins induced by visual experience, and demonstrate a requirement for acute synthesis of CPEB in plasticity.
Acute behavioral effects of nicotine in male and female HINT1 knockout mice.
Jackson, K J; Wang, J B; Barbier, E; Chen, X; Damaj, M I
2012-11-01
Human genetic association and brain expression studies, and mouse behavioral and molecular studies implicate a role for the histidine triad nucleotide-binding protein 1 (HINT1) in schizophrenia, bipolar disorder, depression and anxiety. The high comorbidity between smoking and psychiatric disorders, schizophrenia in particular, is well established. Associations with schizophrenia and HINT1 are also sex specific, with effects more predominant in males; however, it is unknown if sex differences associated with the gene extend to other phenotypes. Thus, in this study, using a battery of behavioral tests, we elucidated the role of HINT1 in acute nicotine-mediated behaviors using male and female HINT1 wild-type (+/+) and knockout (-/-) mice. The results show that male HINT1 -/- mice were less sensitive to acute nicotine-induced antinociception in the tail-flick, but not hot-plate test. At low nicotine doses, male and female HINT1 -/- mice were less sensitive to nicotine-induced hypomotility, although the effect was more pronounced in females. Baseline differences in locomotor activity observed in male HINT1 +/+ and -/- mice were absent in females. Nicotine did not produce an anxiolytic effect in male HINT1 -/- mice, but rather an anxiogenic response. Diazepam also failed to induce an anxiolytic response in these mice, suggesting a general anxiety phenotype not specific to nicotine. Differences in anxiety-like behavior were not observed in female mice. These results further support a role for HINT1 in nicotine-mediated behaviors and suggest that alterations in the gene may have differential effects on phenotype in males and females. PMID:22827509
YILDIRIM, Yrd.Doç.Dr. Figen; BAŞAR, Yrd.Doç.Dr. Özlem Deniz
2013-01-01
The purpose of this study is to determine the impact of consumer self-confidence and personal shopping value dimensions on fashion purchase behavior involving retail practices and information sharing across demographic profiles by comparing with different shopping time period preferences through the season. In this paper, understanding of the shoppers’ reasons for the different shopping time periods of the season is important to synchronize the amount of purchase during the season. For this p...
Behavioral economic analysis of stress effects on acute motivation for alcohol.
Owens, Max M; Ray, Lara A; MacKillop, James
2015-01-01
Due to issues of definition and measurement, the heavy emphasis on subjective craving in the measurement of acute motivation for alcohol and other drugs remains controversial. Behavioral economic approaches have increasingly been applied to better understand acute drug motivation, particularly using demand curve modeling via purchase tasks to characterize the perceived reinforcing value of the drug. This approach has focused on using putatively more objective indices of motivation, such as units of consumption, monetary expenditure, and price sensitivity. To extend this line of research, the current study used an alcohol purchase task to determine if, compared to a neutral induction, a personalized stress induction would increase alcohol demand in a sample of heavy drinkers. The stress induction significantly increased multiple measures of the reinforcing value of alcohol to the individual, including consumption at zero price (intensity), the maximum total amount of money spent on alcohol (Omax), the first price where consumption was reduced to zero (breakpoint), and the general responsiveness of consumption to increases in price (elasticity). These measures correlated only modestly with craving and mood. Self-reported income was largely unrelated to demand but moderated the influence of stress on Omax. Moderation based on CRH-BP genotype (rs10055255) was present for Omax, with T allele homozygotes exhibiting more pronounced increases in response to stress. These results provide further support for a behavioral economic approach to measuring acute drug motivation. The findings also highlight the potential relevance of income and genetic factors in understanding state effects on the perceived reinforcing value of alcohol. PMID:25413719
Ellis, Jason; Cushing, Toby; Germain, Anne
2015-01-01
Study Objectives Despite considerable evidence supporting cognitive behavioral therapy for insomnia (CBT-I) for chronic insomnia, it remains untested within the context of acute insomnia. This study examined the efficacy of a single session of CBT-I, with an accompanying self-help pamphlet, for individuals with acute insomnia. Design A pragmatic parallel group randomized controlled trial. Participants Forty adults (mean age 32.9 + 13.72 y) with Diagnostic and Statistical Manu...
Huang, Dong; Cabral, Ricardo; De la Torre, Fernando
2016-02-01
Discriminative methods (e.g., kernel regression, SVM) have been extensively used to solve problems such as object recognition, image alignment and pose estimation from images. These methods typically map image features ( X) to continuous (e.g., pose) or discrete (e.g., object category) values. A major drawback of existing discriminative methods is that samples are directly projected onto a subspace and hence fail to account for outliers common in realistic training sets due to occlusion, specular reflections or noise. It is important to notice that existing discriminative approaches assume the input variables X to be noise free. Thus, discriminative methods experience significant performance degradation when gross outliers are present. Despite its obvious importance, the problem of robust discriminative learning has been relatively unexplored in computer vision. This paper develops the theory of robust regression (RR) and presents an effective convex approach that uses recent advances on rank minimization. The framework applies to a variety of problems in computer vision including robust linear discriminant analysis, regression with missing data, and multi-label classification. Several synthetic and real examples with applications to head pose estimation from images, image and video classification and facial attribute classification with missing data are used to illustrate the benefits of RR. PMID:26761740
Niedobitek Gerald
2010-03-01
Full Text Available Abstract Epstein-Barr virus (EBV-associated B-cell post-transplantation lymphoproliferative disorder (PTLD is a severe complication following stem cell transplantation. This is believed to occur as a result of iatrogenic immunosuppression leading to a relaxation of T-cell control of EBV infection and thus allowing viral reactivation and proliferation of EBV-infected B-lymphocytes. In support of this notion, reduction of immunosuppressive therapy may lead to regression of PTLD. We present a case of an 18-year-old male developing a monomorphic B-cell PTLD 2 months after receiving an allogenic stem cell transplant for acute lymphoblastic leukemia. Reduction of immunosuppressive therapy led to regression of lymphadenopathy. Nevertheless, the patient died 3 months afterwards due to extensive graft-vs.-host-disease and sepsis. As a diagnostic lymph node biopsy was performed only after reduction of immunosuppressive therapy, we are able to study the histopathological changes characterizing PTLD regression. We observed extensive apoptosis of blast cells, accompanied by an abundant infiltrate comprising predominantly CD8-positive, Granzyme B-positive T-cells. This observation supports the idea that regression of PTLD is mediated by cytotoxic T-cells and is in keeping with the observation that T-cell depletion, represents a major risk factor for the development of PTLD.
Friedman, Alexander; Frankel, Michael; Flaumenhaft, Yakov; Merenlender, Avia; Pinhasov, Albert; Feder, Yuval; Taler, Michal; Gil-Ad, Irit; Abeles, Moshe; Yadid, Gal
2009-03-01
Depressive disorders affect approximately 5% of the population in any given year. Antidepressants may require several weeks to produce their clinical effects. Despite progress being made in this area there is still room and a need to explore additional therapeutic modes to increase treatment effectiveness and responsiveness. Herein, we examined a new method for intervention in depressive states based on deep brain stimulation of the ventral tegmental area (VTA) as a source of incentive motivation and hedonia, in comparison to chemical antidepressants. The pattern of stimulation was fashioned to mimic the firing pattern of VTA neurons in the normal rat. Behavioral manifestations of depression were then monitored weekly using a battery of behavioral tests. The results suggest that treatment with programmed acute electrical stimulation of the VTA substantially alleviates depressive behavior, as compared to chemical antidepressants or electroconvulsive therapy, both in onset time and longitudinal effect. These results were also highly correlated with increases in brain-derived neurotrophic factor mRNA levels in the prefrontal cortex. PMID:18843267
Mesiri, Pavlina
2015-01-01
The objective of this report is to assess the diagnostic value of signs and symptoms and added value of bio markers, e.g. C-Reactive Protein, of adult patients from 16 primary care networks from 12 European countries who presented to primary care with acute cough for GRACE studies in the diagnosis of pneumonia, influenza and obstructive pulmonary diseases treating the diagnoses in parallel rather than in series.
Yulinar Wusanani; Djauhar Ismail; Rina Triasih
2013-01-01
Background Delayed health care-seeking behavior is a cause of high mortality in children due to acute respiratory infections (ARIs). Factors that may affect health care-seeking behavior are socioeconomic status, maternal age, maternal education, parents’ perception of illness, child’s age, number of children under five years of age in the family, and occurrence of natural disasters. The 2006 Central Java earthquake damaged homes and health care facilities, and led to increased poverty among t...
Laura A . León
2010-02-01
Full Text Available In order to study the effect of behavioral or pharmacologically enhanced anxiety on the acquisition of contextual fear conditioning, thirty two Wistar rats (275±25 gm were divided in two groups (behavioral restriction and control. Half of each group received saline solution (ig.; 0.9% or fluoxetine(ig.; 4mg/Kg before the fear conditioning procedure. The two way ANOVA showed significant differences for treatment (F[1,28] = 25.261; P < 0.001. Student Newman-Keuls showed that subjects treated with fluoxetine had lower freezing times. There were no significant differences nor for restriction neither for the interaction between the factors (F[1,28] = 0.115; P = 0.737 y F[1,28] = 0.016; P = 0.899. Thus, the restriction procedure used did not modify the acquisition of the conditioned fear response suggesting that the putative 5-HT enhancement induced is not comparable to that induced by fluoxetine. Acute fluoxetine disrupted the acquisition of the conditioned fear response, suggesting that the mechanism by means of which anxiety disrupts learning could be serotonergic in nature.
Mechanic, Mindy B.; Weaver, Terri L.; Resick, Patricia A.
2000-01-01
The aims of this study were to provide descriptive data on stalking in a sample of acutely battered women and to assess the interrelationship between constructs of emotional abuse, physical violence, and stalking in battered women. We recruited a sample of 114 battered women from shelters, agencies, and from the community at large. Results support the growing consensus that violent and harassing stalking behaviors occur with alarming frequency among physically battered women, both while they ...
Stephenson, Jennifer L.; Maluf, Katrina S
2010-01-01
This study investigated the effects of acute psychosocial stress on trapezius single motor unit discharge behaviors. Twenty-one healthy women performed feedback-controlled isometric contractions under conditions of low and high psychosocial stress in the same experimental session. Psychosocial stress was manipulated using a verbal math task combined with social evaluative threat which significantly increased perceived anxiety, heart rate, and blood pressure (P0.121, N=103) and derecruitment (...
Varlinskaya, Elena I.; Spear, Linda P.
2011-01-01
Repeated exposure to stressors has been found to increase anxiety-like behavior in laboratory rodents, with the social anxiety induced by repeated restraint being extremely sensitive to anxiolytic effects of ethanol in both adolescent and adult rats. No studies, however, have compared social anxiogenic effects of acute stress or the capacity of ethanol to reverse this anxiety in adolescent and adult animals. Therefore, the present study was designed to investigate whether adolescent [postnata...
Central nervous system prophylactic therapy used in the treatment of acute lymphoblastic leukemia can reduce intelligence quotient scores and impair memory and attention in children. Cranial irradiation, intrathecal methotrexate, and steroids are commonly utilized in acute lymphoblastic leukemia therapy. How they induce neurotoxicity is unknown. This study employs an animal model to explore the induction of neurotoxicity. Male and female Sprague-Dawley rats at 17 and 18 days of age were administered 18 mg/kg prednisolone, 2 mg/kg methotrexate, and 1000 cGy cranial irradiation. Another 18-day-old group was administered 1000 cGy cranial irradiation but no drugs. Matching controls received saline and/or a sham exposure to radiation. All animals at 6 weeks and 4 months of age were tested for alterations in spontaneous behavior. A computer pattern recognition system automatically recorded and classified individual behavioral acts displayed during exploration of a novel environment. Measures of behavioral initiations, total time, and time structure were used to compare treated and control animals. A permanent sex-specific change in the time structure of behavior was induced by the prednisolone, methotrexate, and radiation treatment but not by radiation alone. Unlike hyperactivity, the effect consisted of abnormal clustering and dispersion of acts in a pattern indicative of disrupted development of sexually dimorphic behavior. This study demonstrates the feasibility of an animal model delineating the agent/agents responsible for the neurotoxicity of central nervous system prophylactic therapy
Mullenix, P.J.; Kernan, W.J.; Tassinari, M.S.; Schunior, A.; Waber, D.P.; Howes, A.; Tarbell, N.J. (Forsyth Research Institute, Boston, MA (USA))
1990-10-15
Central nervous system prophylactic therapy used in the treatment of acute lymphoblastic leukemia can reduce intelligence quotient scores and impair memory and attention in children. Cranial irradiation, intrathecal methotrexate, and steroids are commonly utilized in acute lymphoblastic leukemia therapy. How they induce neurotoxicity is unknown. This study employs an animal model to explore the induction of neurotoxicity. Male and female Sprague-Dawley rats at 17 and 18 days of age were administered 18 mg/kg prednisolone, 2 mg/kg methotrexate, and 1000 cGy cranial irradiation. Another 18-day-old group was administered 1000 cGy cranial irradiation but no drugs. Matching controls received saline and/or a sham exposure to radiation. All animals at 6 weeks and 4 months of age were tested for alterations in spontaneous behavior. A computer pattern recognition system automatically recorded and classified individual behavioral acts displayed during exploration of a novel environment. Measures of behavioral initiations, total time, and time structure were used to compare treated and control animals. A permanent sex-specific change in the time structure of behavior was induced by the prednisolone, methotrexate, and radiation treatment but not by radiation alone. Unlike hyperactivity, the effect consisted of abnormal clustering and dispersion of acts in a pattern indicative of disrupted development of sexually dimorphic behavior. This study demonstrates the feasibility of an animal model delineating the agent/agents responsible for the neurotoxicity of central nervous system prophylactic therapy.
Pedrini, D. T.; Pedrini, Bonnie C.
Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…
Shawn K Acheson
Full Text Available Ethanol is well known to adversely affect frontal executive functioning, which continues to develop throughout adolescence and into young adulthood. This is also a developmental window in which ethanol is misused by a significant number of adolescents. We examined the effects of acute and chronic ethanol exposure during adolescence on behavioral inhibition and efficiency using a modified water maze task. During acquisition, rats were trained to find a stable visible platform onto which they could escape. During the test phase, the stable platform was converted to a visible floating platform (providing no escape and a new hidden platform was added in the opposite quadrant. The hidden platform was the only means of escape during the test phase. In experiment 1, adolescent animals received ethanol (1.0 g/kg 30 min before each session during the test phase. In experiment 2, adolescent animals received chronic intermittent ethanol (5.0 g/kg for 16 days (PND30 To PND46 prior to any training in the maze. At PND72, training was initiated in the same modified water maze task. Results from experiment 1 indicated that acute ethanol promoted behavioral disinhibition and inefficiency. Experiment 2 showed that chronic intermittent ethanol during adolescence appeared to have no lasting effect on behavioral disinhibition or new spatial learning during adulthood. However, chronic ethanol did promote behavioral inefficiency. In summary, results indicate that ethanol-induced promotion of perseverative behavior may contribute to the many adverse behavioral sequelae of alcohol intoxication in adolescents and young adults. Moreover, the long-term effect of adolescent chronic ethanol exposure on behavioral efficiency is similar to that observed after chronic exposure in humans.
Calfee, Robin D.; Puglis, Holly J.; Little, Edward E.; Brumbaugh, William G.; Mebane, Christopher A.
2016-01-01
Behavioral responses of aquatic organisms to environmental contaminants can be precursors of other effects such as survival, growth, or reproduction. However, these responses may be subtle, and measurement can be challenging. Using juvenile white sturgeon (Acipenser transmontanus) with copper exposures, this paper illustrates techniques used for quantifying behavioral responses using computer assisted video and digital image analysis. In previous studies severe impairments in swimming behavior were observed among early life stage white sturgeon during acute and chronic exposures to copper. Sturgeon behavior was rapidly impaired and to the extent that survival in the field would be jeopardized, as fish would be swept downstream, or readily captured by predators. The objectives of this investigation were to illustrate protocols to quantify swimming activity during a series of acute copper exposures to determine time to effect during early lifestage development, and to understand the significance of these responses relative to survival of these vulnerable early lifestage fish. With mortality being on a time continuum, determining when copper first affects swimming ability helps us to understand the implications for population level effects. The techniques used are readily adaptable to experimental designs with other organisms and stressors.
Abelaira, Helena M; Réus, Gislaine Z; Ribeiro, Karine F; Zappellini, Giovanni; Ferreira, Gabriela K; Gomes, Lara M; Carvalho-Silva, Milena; Luciano, Thais F; Marques, Scherolin O; Streck, Emilio L; Souza, Cláudio T; Quevedo, João
2011-12-01
The present study was aimed to investigate the behavioral and molecular effects of lamotrigine. To this aim, Wistar rats were treated with lamotrigine (10 and 20 mg/kg) or imipramine (30 mg/kg) acutely and chronically. The behavior was assessed using forced swimming test. Brain-derived neurotrophic factor (BDNF), nerve growth factor (NGF), Proteina Kinase B (PKB, AKT), glycogen synthase kinase 3 (GSK-3) and B-cell lymphoma 2 (Bcl-2) levels, citrate synthase, creatine kinase and mitochondrial chain (I, II, II-III and IV) activities were assessed in the brain. The results showed that both treatments reduced the immobility time. The BDNF were increased in the prefrontal after acute treatment with lamotrigine (20 mg/kg), and the BDNF and NGF were increased in the prefrontal after chronic treatment with lamotrigine in all doses. The AKT increased and Bcl-2 and GSK-3 decreased after both treatments in all brain areas. The citrate synthase and creatine kinase increased in the amygdala after acute treatment with imipramine. Chronic treatment with imipramine and lamotrigine (10 mg/kg) increased the creatine kinase in the hippocampus. The complex I was reduced and the complex II, II-III and IV were increased, but related with treatment and brain area. In conclusion, lamotrigine exerted antidepressant-like, which can be attributed to its effects on pathways related to depression, such as neurotrophins, metabolism energy and signaling cascade. PMID:22044672
Denesiuk, E V
2015-01-01
The study involved 23 men after acute myocardial infarction (AMI) with comorbid arterial hypertension (AH). Mean age of patients was 56.7 years. Recurrent myocardial infarction was determined in 38.4%, cardiac failure I-III functional classes--100% of the cases. All patients underwent clinical examination, electrocardiography and echocardiography, blood lipid profile. Standard comprehensive treatment for two years included an perindopril 5-10 mg/day, beta-blocker bisoprolol--5-10 mg/day, antisclerotic drug atorvastatin--20 mg/day and aspirin--75 mg/day. The patients after treatment was determined by a gradual increase towards the target of AT at 3, 6 and 12 to 24 months. Concentric left ventricular hypertrophy (LVH) before treatment was determined in 47.8%, eccentric--in 52.2% of patients. In the study of degrees of LVH I (initial) the extent to treatment was determined by 4.3%, II (moderate)--26.1%, III (large)--at 69.6%, indicating the development of cardiac remodeling. After the treatment was determined by marked reduction III (large) degree and transfer it in the II (moderate) and I (small) degree of left ventricular hypertrophy due to more or less pronounced changes remodeling left ventricular. The obtained data allow a more detailed and adequately assess the structural and functional outcome variables and determine the regression of myocardial hypertrophy in the background to achieve target blood pressure, which is important in practical cardiology. PMID:27491146
Johansen, Søren
2008-01-01
The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...... eigenvalues and eigenvectors. We give a number of different applications to regression and time series analysis, and show how the reduced rank regression estimator can be derived as a Gaussian maximum likelihood estimator. We briefly mention asymptotic results...
Nourse, Rosemary; Reade, Cynthia; Stoltzfus, Jill; Mittal, Vikrant
2014-01-01
Objective: Aggressive patients are not uncommon in acute inpatient behavioral health units of general hospitals. Prior research identifies various predictors associated with aggressive inpatient behavior. This prospective observational study examines the demographic and clinical characteristics of aggressive inpatients and the routine medications these patients were receiving at discharge.
Lee, Myung Hee; Liu, Yufeng
2013-12-01
The continuum regression technique provides an appealing regression framework connecting ordinary least squares, partial least squares and principal component regression in one family. It offers some insight on the underlying regression model for a given application. Moreover, it helps to provide deep understanding of various regression techniques. Despite the useful framework, however, the current development on continuum regression is only for linear regression. In many applications, nonlinear regression is necessary. The extension of continuum regression from linear models to nonlinear models using kernel learning is considered. The proposed kernel continuum regression technique is quite general and can handle very flexible regression model estimation. An efficient algorithm is developed for fast implementation. Numerical examples have demonstrated the usefulness of the proposed technique. PMID:24058224
Rasmussen, Rune Skovgaard; Overgaard, Karsten; Kristiansen, Uffe;
2011-01-01
OBJECTIVES: The objective of this study was to examine the effects of d-amphetamine (amph) upon recovery after embolic stroke in rats. METHODS: Ninety-three rats were embolized in the right middle cerebral artery and assigned to: (1) controls; (2) combination (acute amph and later amph-facilitate...
Scopinho, América A; Lisboa, Sabrina F S; Guimarães, Francisco S; Corrêa, Fernando M A; Resstel, Leonardo B M; Joca, Sâmia R L
2013-01-01
Recent evidence has suggested that the dorsal (DH) and the ventral (VH) poles of the hippocampus are structurally, molecularly and functionally different regions. While the DH is preferentially involved in the modulation of spatial learning and memory, the VH modulates defensive behaviors related to anxiety. Acute restraint is an unavoidable stress situation that evokes marked and sustained autonomic changes, which are characterized by elevated blood pressure (BP), intense heart rate (HR) increases, skeletal muscle vasodilatation and cutaneous vasoconstriction, which are accompanied by a rapid skin temperature drop followed by body temperature increases. In addition to those autonomic responses, animals submitted to restraint also present behavioral changes, such as reduced exploration of the open arms of an elevated plus-maze (EPM), an anxiogenic-like effect. In the present work, we report a comparison between the effects of pharmacological inhibition of DH and VH neurotransmission on autonomic and behavioral responses evoked by acute restraint stress in rats. Bilateral microinjection of the unspeciﬁc synaptic blocker cobalt chloride (CoCl2, 1mM) into the DH or VH attenuated BP and HR responses, as well as the decrease in the skin temperature, elicited by restraint stress exposure. Moreover, DH or VH inhibition before restraint did not change the delayed increased anxiety behavior observed 24 h later in the EPM. The present results demonstrate for the ﬁrst time that both DH and VH mediate stress-induced autonomic responses to restraint but they are not involved in the modulation of the delayed emotional consequences elicited by such stress. PMID:24147071
Effects of an acute and a sub-chronic 900 MHz GSM exposure on brain activity and behaviors of rats
Elsa Brillaud; Aleksandra Piotrowski; Anthony Lecomte; Franck Robidel; Rene de Seze [Toxicology Unit, INERIS, Verneuil en Halatte (France)
2006-07-01
Radio frequencies are suspected to produce health effects. Concerning the mobile phone technology, according to position during use (close to the head), possible effects of radio frequencies on the central nervous system have to be evaluated. Previous works showed contradictory results, possibly due to experimental design diversity. In the framework of R.A.M.P. 2001 project, we evaluated possible effect of a 900 MHz GSM exposure on the central nervous system of rat at a structural, a functional and a behavioral level after acute or sub-chronic exposures. Rats were exposed using a loop antenna system to different S.A.R. levels and durations, according to results of the French C.O.M.O.B.I.O. 2001 project. A functional effect was found (modification of the cerebral activity and increase of the glia surface) after an acute exposure, even at a low level of brain averaged S.A.R. (1.5 W/kg). No cumulative effect was observed after a sub-chronic exposure (same amplitude of the effect). No structural or behavioral consequence was noted. We do not conclude on the neurotoxicity of the 900 MHz GSM exposure on the rat brain. Our results do not indicate any health risk. (authors)
Effects of an acute and a sub-chronic 900 MHz GSM exposure on brain activity and behaviors of rats
Radio frequencies are suspected to produce health effects. Concerning the mobile phone technology, according to position during use (close to the head), possible effects of radio frequencies on the central nervous system have to be evaluated. Previous works showed contradictory results, possibly due to experimental design diversity. In the framework of R.A.M.P. 2001 project, we evaluated possible effect of a 900 MHz GSM exposure on the central nervous system of rat at a structural, a functional and a behavioral level after acute or sub-chronic exposures. Rats were exposed using a loop antenna system to different S.A.R. levels and durations, according to results of the French C.O.M.O.B.I.O. 2001 project. A functional effect was found (modification of the cerebral activity and increase of the glia surface) after an acute exposure, even at a low level of brain averaged S.A.R. (1.5 W/kg). No cumulative effect was observed after a sub-chronic exposure (same amplitude of the effect). No structural or behavioral consequence was noted. We do not conclude on the neurotoxicity of the 900 MHz GSM exposure on the rat brain. Our results do not indicate any health risk. (authors)
Diaz, Marvin R; Mooney, Sandra M; Varlinskaya, Elena I
2016-09-01
Our previous research has shown that in Long Evans rats acute prenatal exposure to a high dose of ethanol on gestational day (G) 12 produces social deficits in male offspring and elicits substantial decreases in social preference relative to controls, in late adolescents and adults regardless of sex. In order to generalize the observed detrimental effects of ethanol exposure on G12, pregnant female Sprague Dawley rats were exposed to ethanol or saline and their offspring were assessed in a modified social interaction (SI) test as early adolescents, late adolescents, or young adults. Anxiety-like behavior was also assessed in adults using the elevated plus maze (EPM) or the light/dark box (LDB) test. Age- and sex-dependent social alterations were evident in ethanol-exposed animals. Ethanol-exposed males showed deficits in social investigation at all ages and age-dependent alterations in social preference. Play fighting was not affected in males. In contrast, ethanol-exposed early adolescent females showed no changes in social interactions, whereas older females demonstrated social deficits and social indifference. In adulthood, anxiety-like behavior was decreased in males and females prenatally exposed to ethanol in the EPM, but not the LDB. These findings suggest that social alterations associated with acute exposure to ethanol on G12 are not strain-specific, although they are more pronounced in Long Evans males and Sprague Dawley females. Furthermore, given that anxiety-like behaviors were attenuated in a test-specific manner, this study indicates that early ethanol exposure can have differential effects on different forms of anxiety. PMID:27154534
Kılıç, Selim
2013-01-01
Linear regression is an approach to modeling the association between a numeric dependent variable y and one or more independent variables denoted X. The case of one explanatory variable in regression model is called simple linear regression. For more than one explanatory variable, then the model is called multiple linear regression. The dependent variable should be a numeric variable in linear regression. It is recommended at least 10 times as many cases as the number of independent variables...
Averaged extreme regression quantile
Jureckova, Jana
2015-01-01
Various events in the nature, economics and in other areas force us to combine the study of extremes with regression and other methods. A useful tool for reducing the role of nuisance regression, while we are interested in the shape or tails of the basic distribution, is provided by the averaged regression quantile and namely by the average extreme regression quantile. Both are weighted means of regression quantile components, with weights depending on the regressors. Our primary interest is ...
Effects of acute and chronic ethanol exposure on the behavior of adult zebrafish (Danio rerio)
Gerlai, Robert; Lee, Vallent; Blaser, Rachel
2006-01-01
The zebrafish has been a popular subject of embryology and genetic research for the past three decades. Recently, however, the interest in its neurobiology and behavior has also increased. Nevertheless, compared to other model organisms, e.g., rodents, zebrafish behavior is understudied and very few behavioral paradigms exist for mutation or drug screening purposes. Alcoholism is one of the biggest and costliest diseases whose mechanisms are not well understood. Model organisms such as the ze...
Acute and developmental behavioral effects of flame retardants and related chemicals in zebrafish
Jarema, Kimberly A; Hunter, Deborah L.; Shaffer, Rachel M.; Behl, Mamta; Padilla, Stephanie
2015-01-01
As polybrominated diphenyl ethers are phased out, numerous compounds are emerging as potential replacement flame retardants for use in consumer and electronic products. Little is known, however, about the neurobehavioral toxicity of these replacements. This study evaluated the neurobehavioral effects of acute or developmental exposure to t-butylphenyl diphenyl phosphate (BPDP), 2-ethylhexyl diphenyl phosphate (EHDP), isodecyl diphenyl phosphate (IDDP), isopropylated phenyl phosphate (IPP), tr...
Remage-Healey, Luke
2012-01-01
Although estrogens are widely considered circulating ‘sex steroid hormones’ typically associated with female reproduction, recent evidence suggests that estrogens can act as local modulators of brain circuits in both males and females. Functional implications of this newly-characterized estrogen signaling system have begun to emerge. This essay summarizes evidence in support of the hypothesis that the rapid production of estrogens in brain circuits can drive acute changes in both the producti...
Acute maternal alcohol consumption disrupts behavioral state organization in the near-term fetus
Mulder, EJH; Morssink, LP; Van der Schee, T; Visser, GHA
1998-01-01
Disturbed sleep regulation is often observed in neonates of women who drank heavily during pregnancy. It is unknown if (and how) an occasional drink affects fetal sleeping behavior. In 28 near-term pregnant women we examined the effects on fetal behavioral state organization of two glasses of wine (
Vikrant Sudan
2014-03-01
Full Text Available Toxoplasma gondii, an apicomplexan parasite, is capable of infecting a broad range of intermediate warm-blooded hosts including humans. The parasite seems to be capable of altering the natural behavior of the host to favor its transmission in the environment. The aim of this study was to evaluate the course, alterations in behavior along with normal kinetics of the abnormally induced experimental acute toxoplasmosis in murine models.Ten Swiss albino mice were intraperitoneally inoculated with 100 virulent RH strain tachyzoites and finally, the alterations in behavior were described and compared with other known alterations in humans and animals.The behavior and the other symptoms of the acute toxoplasmosis were recorded. Such mice showed typical symptoms like normal coat, severe ascites with pendulous abdomen and tachypnoea exhibited by resting fore legs either on walls of the cage, or nozzle of water bottle or other resting mice and yielded a creamy colored cloudy natured peritoneal fluid on aspiration.Finally the alterations in behavior were described and compared with other known alterations in humans and animals. The study has generated some important data related to possible causes of behavioral alterations and generation of suitable strategies for control of these alterations in behavior vis-à-vis better understanding of the effect of acute infection of parasite on normal behavior of infected intermediate host.
Secondary Prevention in Acute Myocardial Infarction
IRMAK, Yrd.Doç.Dr. Zöhre; FESCİ, Doç.Dr. Hatice
2005-01-01
Recent studies on patients who had an acute myocardial infarction have shown that risk factors are decreased, atherosclerosis regressed, and re-infarction and mortality rates are reduced as a result of drug therapy in combination with the changes in the lifestyle. This treatment called as secondary prevention, requires a behavioral change in the lifestyle that includes stopping smoking, making healthy food choices, and increasing physical activity. Risk factors related with lifestyle, wh...
Posterior leukoencephalopathy syndrome in poststretococcal acute glomerulonephritis
Reversible posterior leukoencephalopathy (LEPR) is a clinical entity that affects radiation usually the white matter of the cerebral hemispheres. It is frequently associated with acute arterial hypertension and immunosuppressive therapy, among other causes. The clinical presentation is varied, with headache, nausea, vomiting, impaired consciousness and abnormal behavior, seizures and visual disturbances, symptoms that often regress. Computed tomography (CT) and magnetic resonance imaging (MRI) images show white matter edema predominantly in posterior regions of the brain. We present a 10 year old boy with leprosy in the course of a nephrotic syndrome secondary to acute diffuse glomerunefritis (GNDA) poststreptococcal. (author)
Acute Stress Promotes Aggressive-Like Behavior in Rats Made Allergic to Tree Pollen
Tonelli, Leonardo H.; Hoshino, Akina; Katz, Morgan; Teodor T. Postolache
2008-01-01
It has been reported that allergies are associated with depression and possibly suicide in women. Aggression is an important behavioral component that predisposes depressed individuals to suicidal acts. In the present study we examined the relationship between allergies and aggression to determine a potential contribution of allergies in factors of risk for suicidal behavior. Because stress plays a critical role in the manifestation of clinical symptoms of allergies and also in suicidal behav...
Regression analysis by example
Chatterjee, Samprit
2012-01-01
Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded
Eberly, Lynn E
2007-01-01
This chapter describes multiple linear regression, a statistical approach used to describe the simultaneous associations of several variables with one continuous outcome. Important steps in using this approach include estimation and inference, variable selection in model building, and assessing model fit. The special cases of regression with interactions among the variables, polynomial regression, regressions with categorical (grouping) variables, and separate slopes models are also covered. Examples in microbiology are used throughout. PMID:18450050
Fitzenberger, Bernd; Wilke, Ralf Andreas
2015-01-01
Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights by m...... treatment of the topic is based on the perspective of applied researchers using quantile regression in their empirical work....
Coping with an acute psychosocial challenge: behavioral and physiological responses in young women.
Carolina Villada
Full Text Available Despite the relevance of behavior in understanding individual differences in the strategies used to cope with stressors, behavioral responses and their relationships with psychobiological changes have received little attention. In this study on young women, we aimed at analyzing the associations among different components of the stress response and behavioral coping using a laboratory psychosocial stressor. The Ethological Coding System for Interviews, as well as neuroendocrine, autonomic and mood parameters, were used to measure the stress response in 34 young women (17 free-cycling women in their early follicular phase and 17 oral contraceptive users subjected to the Trier Social Stress Test (TSST and a control condition in a crossover design. No significant differences in cardiac autonomic, negative mood and anxiety responses to the stressor were observed between the two groups of women. However, women in the follicular phase showed a higher cortisol response and a larger decrease in positive mood during the social stress episode, as well as greater anxiety overall. Interestingly, the amount of displacement behavior exhibited during the speaking task of the TSST was positively related to anxiety levels preceding the test, but negatively related to baseline and stress response values of heart rate. Moreover, the amount of submissive behavior was negatively related to basal cortisol levels. Finally, eye contact and low-aggressiveness behaviors were associated with a worsening in mood. Overall, these findings emphasize the close relationship between coping behavior and psychobiological reactions, as well as the role of individual variations in the strategy of coping with a psychosocial stressor.
Bennett, Amanda M; Longhi, Jessica N; Chin, Eunice H; Burness, Gary; Kerr, Leslie R; Murray, Dennis L
2016-04-01
Anuran larvae exhibit behavioral and morphological plasticity in response to perceived predation risk, although response type and magnitude varies through ontogeny. Increased baseline corticosterone is related to morphological response to predation risk, whereas the mechanism behind behavioral plasticity remains enigmatic. Since tadpoles alter behavioral responses to risk immediately upon exposure to predator cues, we characterized changes in whole body corticosterone at an acute (habituation, although the magnitude of increase was markedly diminished when compared to younger tadpoles (GS25). These experiments represent the first assessment of tadpole hormonal responses to predation risk at the acute timescale. Further research is required to establish causality between hormonal responses and behavioral changes, and to examine how and why responsiveness changes over ontogeny and with chronic exposure to risk. PMID:26944484
程昌志; 赵东海; 李全岳; 曲海燕; 陈伯成; 林舟丹
2011-01-01
Objective To explore the risk factors of complication of acute renal failure (ARF) in war injuries of limbs. Methods The clinical data of 352 patients with limb injuries admitted to 303 Hospital of PLA from 1968 to 2002 were retrospectively analyzed. The patients were divided into ARF group (n=9) and non-ARF group ( n=343) according to the occurrence of ARF, and the case-control study was carried out. Ten factors which might lead to death were analyzed by logistic regression to screen the risk factors for ARF,including causes of trauma, shock after injury, time of admission to hospital after injury, injured sites, combined trauma, number of surgical procedures, presence of foreign matters, features of fractures, amputation, and tourniquet time. Results Fifteen of the 352 patients died (4.3％) , among them 7 patients (46.7％) died of ARF, 3 (20.0％) of pulmonary embolism, 3 (20.0 ％) of gas gangrene,and 2 (13.3％) of multiple organ failure. Univariate analysis revealed that the shock, time before admitted to hospital, amputation and tourniquet time were the risk factors for ARF in the wounded with limb injuries, while the logistic regression analysis showed only amputation was the risk factor for ARF ( P＜0.05). Conclusion ARF is the primary cause-of-death in the wounded with limb injury.Prompt and accurate treatment and optimal time for amputation may be beneficial to decreasing the incidence and mortality of ARF in the wounded with severe limb injury and ischemic necrosis.%目的 探讨四肢战创伤并发急性肾衰竭(ARF)的危险因素.方法 回顾性分析1968-2002年收治的352例四肢战创伤患者,根据是否发生ARF将患者分为ARF组(9例)和非ARF组(343例)并进行病例对照研究,选择可能影响患者死亡的10个因素(致伤物、伤后是否休克、伤后入院时间、受伤部位、有无合并伤、手术次数、有否异物存留、骨折性质、是否截肢、止血带时间)进行logistic回归分析,筛
Naghshpour, Shahdad
2012-01-01
Regression analysis is the most commonly used statistical method in the world. Although few would characterize this technique as simple, regression is in fact both simple and elegant. The complexity that many attribute to regression analysis is often a reflection of their lack of familiarity with the language of mathematics. But regression analysis can be understood even without a mastery of sophisticated mathematical concepts. This book provides the foundation and will help demystify regression analysis using examples from economics and with real data to show the applications of the method. T
Brainstem infarcts predict REM sleep behavior disorder in acute ischemic stroke
Tang, Wai Kwong; Hermann, Dirk M.; Chen, Yang Kun; Liang, Hua Jun; Liu, Xiang Xin; Chu, Winnie Chui Wing; Anil T. Ahuja; Abrigo, Jill; Mok, Vincent; Ungvari, Gabor S; Wong, Ka Sing
2014-01-01
Background Rapid eye movement (REM) sleep behavior disorder (RBD) is a sleep disturbance in which patients enact their dreams while in REM sleep. The behavior is typically violent in association with violent dream content, so serious harm can be done to the patient or the bed partner. The prevalence of RBD is well-known in Parkinson’s disease, Lewy body dementia, and multiple systems atrophy. However, its prevalence and causes in stroke remained unclear. The aim of this study was to determine...
Influence of acute static stretching on the behavior of maximum muscle strength
Carmen Lúcia Borges Bastos
2014-06-01
Full Text Available The aim of this study was to compare the influence of acute static stretching on maximal muscle strength (1RM. The non-probabilistic sample consisted of 30 subjects split into two groups: static stretching (SS= 15 and without stretching group (WS= 15. Muscle strength evaluation (1RM was conducted with a Dynamometer model 32527pp400 Pound push / pull devices coupled in knee extension (KE and bench press (BP. The Wilcoxon test for intragroup comparisons and the Kruskal-Wallis test for comparisons between groups (p< 0.05 were selected. There were no significant differences (p> 0.05 between the SS and WS in exercise KE and BP. Therefore, it can be concluded that there was no reduction in the performance of 1RM performing the exercises KE and BP when preceded by static stretching.
Numerical calculation on behavior of fuel regression in hybrid rocket motor%混合火箭发动机燃料退移特性的数值计算
单繁立; 侯凌云; 朴英
2011-01-01
混合火箭发动机在航天推进领域优势明显,但由于氧化剂和燃料相态不同,燃料退移的机理和特性比较复杂.采用自行编写的混合火箭发动机程序(HRM)模拟了这种发动机的非稳态工作过程.通过该程序实时数值求解了从氧化剂注入端到尾喷管的全部物理化学过程,并基于燃料表面上气固间的质量和能量耦合,运用燃料表面动态退移和两步计算方法,模拟了燃料退移.在与发动机推力和燃料退移量等实验数据对比的基础上,给出了燃料退移速率方程和燃料退移速率随燃烧室直径的变化规律,确定并分析了影响混合火箭发动机尺度效应的因素.%Hybrid rocket motor has many advantages for space propulsion applications. The mechanism and behavior of the fuel regression in the motor are complicated due to different phase states of the oxidizer and the fuel. The hybrid rocket motor code ( HRM) for the unsteady simulation of motor operation has been programmed. This code can calculate physical and chemical processes from the oxidizer injector to the nozzle during motor operation. The mass and energy coupling between gas and solid phases as well as a dynamic fuel surface regression technique with a two-step calculation method is applied to simulate the fuel regression. The calculated motor thrust and fuel regression are compared to the experimental data for the code validation. The fuel regression rate equation and the relation between fuel regression rate and chamber diameter have been derived. The reasons for scale effect in hybrid rocket motor have been determined and analyzed.
Cardel, M I; Johnson, S L; Beck, J; Dhurandhar, E; Keita, A D; Tomczik, A C; Pavela, G; Huo, T; Janicke, D M; Muller, K; Piff, P K; Peters, J C; Hill, J O; Allison, D B
2016-08-01
Both subjective and objectively measured social status has been associated with multiple health outcomes, including weight status, but the mechanism for this relationship remains unclear. Experimental studies may help identify the causal mechanisms underlying low social standing as a pathway for obesity. Our objective was to investigate the effects of experimentally manipulated social status on ad libitum acute dietary intakes and stress-related outcomes as potential mechanisms relating social status and weight. This was a pilot feasibility, randomized, crossover study in Hispanic young adults (n=9; age 19-25; 67% female; BMI ≥18.5 and ≤30kg/m(2)). At visit 1, participants consumed a standardized breakfast and were randomized to a high social status position (HIGH) or low social status position (LOW) in a rigged game of Monopoly™. The rules for the game differed substantially in terms of degree of 'privilege' depending on randomization to HIGH or LOW. Following Monopoly™, participants were given an ad libitum buffet meal and energy intakes (kcal) were estimated by pre- and post-weighing foods consumed. Stress-related markers were measured at baseline, after the game of Monopoly™, and after lunch. Visit 2 used the same standardized protocol; however, participants were exposed to the opposite social status condition. When compared to HIGH, participants in LOW consumed 130 more calories (p=0.07) and a significantly higher proportion of their daily calorie needs in the ad libitum buffet meal (39% in LOW versus 31% in HIGH; p=0.04). In LOW, participants reported decreased feelings of pride and powerfulness following Monopoly™ (p=0.05) and after their lunch meal (p=0.08). Relative to HIGH, participants in LOW demonstrated higher heart rates following Monopoly™ (p=0.06), but this relationship was not significant once lunch was consumed (p=0.31). Our pilot data suggest a possible causal relationship between experimentally manipulated low social status and
Aldrich, John
2005-01-01
In 1922 R. A. Fisher introduced the modern regression model, synthesizing the regression theory of Pearson and Yule and the least squares theory of Gauss. The innovation was based on Fisher’s realization that the distribution associated with the regression coefficient was unaffected by the distribution of X. Subsequently Fisher interpreted the fixed X assumption in terms of his notion of ancillarity. This paper considers these developments against the background of the development of statisti...
Visualisation of Regression Trees
Brunsdon, Chris
2007-01-01
he regression tree [1] has been used as a tool for exploring multivariate data sets for some time. As in multiple linear regression, the technique is applied to a data set consisting of a contin- uous response variable y and a set of predictor variables { x 1 ,x 2 ,...,x k } which may be continuous or categorical. However, instead of modelling y as a linear function of the predictors, regression trees model y as a series of ...
Understanding logistic regression analysis
Sperandei, Sandro
2014-01-01
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using ex...
David F. Hendry; Krolzig, Hans Martin
2004-01-01
The controversy over the selection of "growth regressions" was precipitated by some remarkably numerous "estimation" strategies, including two million regressions by Sala-i-Martin [American Economic Review (1997b) Vol. 87, pp. 178-183]. Only one regression is really needed, namely the general unrestricted model, appropriately reduced to a parsimonious encompassing, congruent representation. We corroborate the findings of Hoover and Perez [Oxford Bulletin of Economics and Statistics (2004) Vol...
Weisberg, Sanford
2005-01-01
Master linear regression techniques with a new edition of a classic text Reviews of the Second Edition: ""I found it enjoyable reading and so full of interesting material that even the well-informed reader will probably find something new . . . a necessity for all of those who do linear regression."" -Technometrics, February 1987 ""Overall, I feel that the book is a valuable addition to the now considerable list of texts on applied linear regression. It should be a strong contender as the leading text for a first serious course in regression analysis."" -American Scientist, May-June 1987
Introduction to regression graphics
Cook, R Dennis
2009-01-01
Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava
Flexible survival regression modelling
Cortese, Giuliana; Scheike, Thomas H; Martinussen, Torben
2009-01-01
time-varying effects. The introduced models are all applied to data on breast cancer from the Norwegian cancer registry, and these analyses clearly reveal the shortcomings of Cox's regression model and the need for other supplementary analyses with models such as those we present here.......Regression analysis of survival data, and more generally event history data, is typically based on Cox's regression model. We here review some recent methodology, focusing on the limitations of Cox's regression model. The key limitation is that the model is not well suited to represent time...
刘弘; 罗宝章; 吴春峰; 陆冬磊; 邢之慧
2012-01-01
Objective To study acute diarrhea status and risk factors of dietary behavior in Shanghai. Methods A stratified multi-stage cluster random household sampling was used in this cross-sectional survey. Results The incidence rate of acute diarrhea among Shanghai residents was 0. 68 episodes per person-year. It was 0. 75 episodes per person-year for males and 0.62 episodes per person-year for females. It was estimated to be 0.96, 0.54, 0.71, 0.71 and 0.64 episodes per person-year for age groups of 0 - 7 , 8 - 17 , 18 - 33 , 34 - 59 and ≥60, respectively. 24. 34% of respondents suspected their illness was due to contaminated food. 40. 03% of respondents had visited doctor. Single variable and logistic regression analysis showed that odds ratio of acute diarrhea were 1.37 ( 95 % confidence interval, 1. 13 - 1. 67 ) , 0.66 (95% confidence interval, 0.49 -0.88) and 0.76 (95% confidence interval, 0.62 -0.94) for the respondents who had the behavior of eating delicatessen, keeping food at low temperature and salty taste. Conclusion Acute diarrhea is a common illness among residents in Shanghai. The incidence of acute diarrhea was slightly higher in males. After the age of eight, the incidence declined, but increased again in adult and then declined above 60. The behavior of eating delicatessen was a risk factor for acute diarrhea. The habit of keeping the food at low temperature and salty taste might be the protective factors for acute diarrhea.%目的 了解上海市急性腹泻现况及饮食行为危险因素.方法 采用多阶段随机抽样,进行横断面入户问卷调查.结果 上海市居民急性腹泻年发生率0.68次/人年.男性0.75次/人年、女性0.62次/人年,0～7岁、8 ～17岁、18 ～33岁、34 ～59岁、≥60岁分别为0.96、0.54、0.71、0.71、0.64次/人年.有24.34％人自诉有可疑食物史、40.03％的人就诊.单因素及Logistic回归多因素分析显示:食用散装熟食者的急性腹泻OR值为1.37、95％CI (1.13～1.67),
Abdullah, Mishal; Mahowald, Maren L; Frizelle, Sandra P; Dorman, Christopher W; Funkenbusch, Sonia C; Krug, Hollis E
2016-01-01
Arthritis is the most common cause of disability in the US, and the primary manifestation of arthritis is joint pain that leads to progressive physical limitation, disability, morbidity, and increased health care utilization. Capsaicin (CAP) is a vanilloid agonist that causes substance P depletion by interacting with vanilloid receptor transient receptor potential V1 on small unmyelinated C fibers. It has been used topically for analgesia in osteoarthritis with variable success. Resiniferatoxin (RTX) is an ultra potent CAP analog. The aim of this study was to measure the analgesic effects of intra-articular (IA) administration of CAP and RTX in experimental acute inflammatory arthritis in mice. Evoked pain score (EPS) and a dynamic weight bearing (DWB) device were used to measure nociceptive behaviors in a murine model of acute inflammatory monoarthritis. A total of 56 C57B16 male mice underwent EPS and DWB testing – 24 nonarthritic controls and 32 mice with carrageenan-induced arthritis. The effects of pretreatment with 0.1% CAP, 0.0003% RTX, or 0.001% RTX were measured. Nociception was reproducibly demonstrated by increased EPS and reduced DWB measures in the affected limb of arthritic mice. Pretreatment with 0.001% RTX resulted in statistically significant improvement in EPS and DWB measures when compared with those observed in carrageenan-induced arthritis animals. Pretreatment with IA 0.0003% RTX and IA 0.01% CAP resulted in improvement in some but not all of these measures. The remaining 24 mice underwent evaluation following treatment with 0.1% CAP, 0.0003% RTX, or 0.001% RTX, and the results obtained were similar to that of naïve, nonarthritic mice. PMID:27574462
Spiacci, A; Sergio, T de Oliveira; da Silva, G S F; Glass, M L; Schenberg, L C; Garcia-Cairasco, N; Zangrossi, H
2015-10-29
It has been proposed that spontaneous panic attacks are the outcome of the misfiring of an evolved suffocation alarm system. Evidence gathered in the last years is suggestive that the dorsal periaqueductal gray (dPAG) in the midbrain harbors a hypoxia-sensitive suffocation alarm system. We here investigated whether facilitation of 5-HT-mediated neurotransmission within the dPAG changes panic-like defensive reactions expressed by male Wistar rats submitted to a hypoxia challenge (7% O2), as observed in other animal models of panic. Intra-dPAG injection of 5-HT (20 nmol), (±)-8-hydroxy-2-(di-n-propylamino) tetralin hydrobromide (8-OH-DPAT) (8 nmol), a 5-HT1A receptor agonist, or (±)-2,5-dimethoxy-4-iodo amphetamine hydrochloride (DOI) (16 nmol), a preferential 5-HT2A agonist, reduced the number of upward jumps directed to the border of the experimental chamber during hypoxia, interpreted as escape attempts, without affecting the rats' locomotion. These effects were similar to those caused by chronic, but not acute, intraperitoneal administration of the antidepressant fluoxetine (5-15 mg/kg), or acute systemic administration of the benzodiazepine receptor agonist alprazolam (1-4 mg/kg), both drugs clinically used in the treatment of panic disorder. Our findings strengthen the view that the dPAG is a key encephalic area involved in the defensive behaviors triggered by activation of the suffocation alarm system. They also support the use of hypoxia-evoked escape as a model of respiratory-type panic attacks. PMID:26319117
Effects of Acute Amphetamine Exposure on Two Kinds of Pavlovian Approach Behavior
Holden, John Michael; Peoples, Laura L.
2009-01-01
Two kinds of Pavlovian conditioned approach behavior are possible: approach of the CS (sign-tracking) and approach of the US (goal-tracking). We hypothesized that administration of AMP would increase sign-tracking and decrease goal-tracking. However, increasing doses of AMP (up to 2.0 mg/kg) decreased measures of sign-tracking while simultaneously increasing measures of goal-tracking. Administration of AMP may shift responding from cues distant from the CS to cues closer to the CS.
Mishra, Rachana; Manchanda, Shaffi; Gupta, Muskan; Kaur, Taranjeet; Saini, Vedangana; Sharma, Anuradha; Kaur, Gurcharan
2016-01-01
Sleep deprivation (SD) leads to the spectrum of mood disorders like anxiety, cognitive dysfunctions and motor coordination impairment in many individuals. However, there is no effective pharmacological remedy to negate the effects of SD. The current study examined whether 50% ethanolic extract of Tinospora cordifolia (TCE) can attenuate these negative effects of SD. Three groups of adult Wistar female rats - (1) vehicle treated-sleep undisturbed (VUD), (2) vehicle treated-sleep deprived (VSD) and (3) TCE treated-sleep deprived (TSD) animals were tested behaviorally for cognitive functions, anxiety and motor coordination. TSD animals showed improved behavioral response in EPM and NOR tests for anxiety and cognitive functions, respectively as compared to VSD animals. TCE pretreatment modulated the stress induced-expression of plasticity markers PSA-NCAM, NCAM and GAP-43 along with proteins involved in the maintenance of LTP i.e., CamKII-α and calcineurin (CaN) in hippocampus and PC regions of the brain. Interestingly, contrary to VSD animals, TSD animals showed downregulated expression of inflammatory markers such as CD11b/c, MHC-1 and cytokines along with inhibition of apoptotic markers. This data suggests that TCE alone or in combination with other memory enhancing agents may help in managing sleep deprivation associated stress and improving cognitive functions. PMID:27146164
Saeid Shahbazi Naserabad
2014-12-01
Full Text Available Background: Pesticides are widely used in agriculture. Excessive use of pesticides has health risk for human and threatens non-target organisms. This research aimed to determine lethal concentrations of malathion and Hinosan for Carassius auratus (5±1 gr [mean ± SD]. Methods: Experiments were performed according to O.E.C.D for 4 days (96 h and concentrations of 0, 1, 2, 4, 8 mg L-1 Hinosan and 0, 1, 2, 4, 16 mg L-1 malathion with three replicates. LC1, LC10, LC30, LC50, LC70, LC90 and LC99 for 24, 48, 72 and 96 h were determined using a probit analysis. Results: The results indicated that the 96 h LC50 value of Hinosan and malathion for Gold fish was 4.02 and 4.71 mg/L, respectively. Fishes exhibited irregular, erratic and darting swimming movements, hyper excitability, bruise in the caudal section, loss of equilibrium and sinking to the bottom. Conclusion: Malathion and Hinosan have medium toxicity for C. auratus and could cause irreversible harm and behavioral changes.
Praptiningsih, Catharina Y; Lafond, Kathryn E; Wahyuningrum, Yunita; Storms, Aaron D; Mangiri, Amalya; Iuliano, Angela D; Samaan, Gina; Titaley, Christiana R; Yelda, Fitra; Kreslake, Jennifer; Storey, Douglas; Uyeki, Timothy M
2016-06-01
Understanding healthcare-seeking patterns for respiratory illness can help improve estimations of disease burden and inform public health interventions to control acute respiratory disease in Indonesia. The objectives of this study were to describe healthcare-seeking behaviors for respiratory illnesses in one rural and one urban community in Western Java, and to explore the factors that affect care seeking. From February 8, 2012 to March 1, 2012, a survey was conducted in 2520 households in the East Jakarta and Bogor districts to identify reported recent respiratory illnesses, as well as all hospitalizations from the previous 12-month period. We found that 4% (10% of those less than 5years) of people had respiratory disease resulting in a visit to a healthcare provider in the past 2weeks; these episodes were most commonly treated at government (33%) or private (44%) clinics. Forty-five people (0.4% of those surveyed) had respiratory hospitalizations in the past year, and just over half of these (24/45, 53%) occurred at a public hospital. Public health programs targeting respiratory disease in this region should account for care at private hospitals and clinics, as well as illnesses that are treated at home, in order to capture the true burden of illness in these communities. PMID:26930154
Spontaneous regression of osteochondromas
Hoshi, Manabu; Takami, Masatsugu; Hashimoto, Ryouji; Okamoto, Takashi; Yanagida, Ikuhisa; Matsumura, Akira; Noguchi, Kazuko [Yodogawa Christian Hospital, Department of Orthopaedic Surgery, Osaka (Japan)
2007-06-15
Spontaneous regression of an osteochondroma is an infrequent event. In this report, two cases with spontaneous regression of osteochondromas are presented. The first case was a solitary osteochondroma of the pedunculated type involving the right proximal humerus in a 7-year-old boy. This lesion resolved over 15 months of observation. The second case was a 3-year-old girl with multiple osteochondromatosis, in whom sessile osteochondromas of the right tibia and left fibula regressed over 33 months.The mechanism of this phenomenon is discussed with a review of previous reports. Regarding treatment, careful observation may be acceptable for typical osteochondromas, especially in young children. (orig.)
Hosmer, David W; Sturdivant, Rodney X
2013-01-01
A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-
Weisberg, Sanford
2013-01-01
Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus
Seyed Ali Akbar Hedayati
2015-09-01
Full Text Available Background: Diazinon is an organophosphorous pesticide which widely found in municipal, agricultural, and urban storm water discharges. The present study was conducted to achieve lethal concentration (LC50 and behavioral changes of Rutilus rutilus caspius and Hypophthal-micthys molitrix after exposure to lethal concentration of diazinon. Methods: The experiment was carried out in static conditions, based on instructions of OECD in 4 days under controlled water physicochemical conditions with pH of 7.2±0.2, oxygen of 7±0.3 mg/l, total hardness of 180 mg CaCo3 and temperature of 24±1 C°. All fishes were accli-matized in 400 L aquaria for 10 days. Treated aquaria had concentrations of 0.5, 1, 2, 4, 8, 10, 20, 40, 60, and 80 ppm of diazinon for H. molitrix, and 1, 2, 4, 8, 10, and 20 for R. rutilus caspi-cus, while there was no toxic concentration for the control group. LC1, LC10, LC30, LC50, LC70, LC90, and LC99 were calculated for 24, 48, 72, and 96 hours. Results: LC50 96h diazinon values were 3.93 and 1.71 ppm for H. molitrix and R. rutilus caspi-cus, respectively. Clinical observation revealed that the poisoned fishes suffered from nerve paralysis syndrome. The fishes exhibited irregular, erratic, and darting swimming movements, severe aching, and collapse to the bottom of the aquarium. Conclusion: These findings suggest that diazinon has medium toxicity at low concentrations for thede two species and causes morbidities.
George: Gaussian Process regression
Foreman-Mackey, Daniel
2015-11-01
George is a fast and flexible library, implemented in C++ with Python bindings, for Gaussian Process regression useful for accounting for correlated noise in astronomical datasets, including those for transiting exoplanet discovery and characterization and stellar population modeling.
Distributed multinomial regression
Taddy, Matt
2015-01-01
This article introduces a model-based approach to distributed computing for multinomial logistic (softmax) regression. We treat counts for each response category as independent Poisson regressions via plug-in estimates for fixed effects shared across categories. The work is driven by the high-dimensional-response multinomial models that are used in analysis of a large number of random counts. Our motivating applications are in text analysis, where documents are tokenized and the token counts ...
Sparse Multivariate Factor Regression
Kharratzadeh, Milad; Coates, Mark
2015-01-01
We consider the problem of multivariate regression in a setting where the relevant predictors could be shared among different responses. We propose an algorithm which decomposes the coefficient matrix into the product of a long matrix and a wide matrix, with an elastic net penalty on the former and an $\\ell_1$ penalty on the latter. The first matrix linearly transforms the predictors to a set of latent factors, and the second one regresses the responses on these factors. Our algorithm simulta...
Xiong, Shifeng
2011-01-01
In this paper we discuss the variable selection method from \\ell0-norm constrained regression, which is equivalent to the problem of finding the best subset of a fixed size. Our study focuses on two aspects, consistency and computation. We prove that the sparse estimator from such a method can retain all of the important variables asymptotically for even exponentially growing dimensionality under regularity conditions. This indicates that the best subset regression method can efficiently shri...
Xue, Li-Xia; Zhang, Ting; Zhao, Yu-Wu; Geng, Zhi; CHEN Jing-jiong; Chen, Hao
2016-01-01
Cerebrolysin and DL-3-n-butylphthalide (NBP) have each shown neuroprotective efficacy in preclinical models of acute ischemic stroke (AIS) and passed clinical trials as therapeutic drugs for AIS. The present study was a clinical trial to assess and compare the efficacy and safety of NBP and Cerebrolysin in the reduction of neurological and behavioral disability following AIS. A randomized, double-blind trial was conducted with enrolment of 60 patients within 12 h of AIS. In addition to routin...
Yetnikoff, Leora; Arvanitogiannis, Andreas
2013-01-01
Background Behavioral effects of stimulant drugs are influenced by non-pharmacological factors, including genetic variability and age. We examined acute and sensitized locomotor effects of methylphenidate in adolescent and early adult male Sprague Dawley (SD), spontaneously hypertensive (SHR) and Wistar Kyoto (WKY) rats using a drug regimen that differentiates clearly between initial and enduring differences in drug responsiveness. We probed for strain and age differences in the sensitizing e...
Honda, Miwako; Ito, Mio; Ishikawa, Shogo; Takebayashi, Yoichi; Tierney, LawrenceJr.
2016-01-01
Management of Behavioral and Psychological Symptoms of Dementia (BPSD) is a key challenge in geriatric dementia care. A multimodal comprehensive care methodology, Humanitude, with eye contact, verbal communication, and touch as its elements, was provided to three geriatric dementia patients for whom conventional nursing care failed in an acute care hospital. Each episode was evaluated by video analysis. All patients had advanced dementia with BPSD. Failure of care was identified by patient’s ...
Zümrüt Başbakkal; Sibel Sönmez; Nesrin Şen Celasin; Figen Esenay
2010-01-01
The study is executed with mothers of children aged 3-6 (n=170) whose children were hospitalized for the first time between the dates of 15.07.2003 and 15.06.2006, who were reachable by phone and accepted to participate in the study aiming determination of behavioral reactions of a child of 3-6 ages group to be hospitalized due to an acute illness.In this study, for data gathering "Personal Information Form" including 15 questions and "Inquiry Form of Behavior Changes of 3-6 Ages Group Child...
Aggression and the Risk for Suicidal Behaviors among Children
Greening, Leilani; Stoppelbein, Laura; Luebbe, Aaron; Fite, Paula J.
2010-01-01
Two subtypes of aggression--reactive and proactive--were examined to see how they relate to suicidal behaviors among young children admitted for acute psychiatric inpatient care. The children and their parents completed self-report questionnaires/interviews. Regression analyses revealed that depressed girls who scored higher on reactive aggression…
McConnell, Eleanor S; Karel, Michele J
2016-01-01
As the prevalence of Alzheimer disease and related dementias increases, dementia-related behavioral symptoms present growing threats to care quality and safety of older adults across care settings. Behavioral and psychological symptoms of dementia (BPSD) such as agitation, aggression, and resistance to care occur in nearly all individuals over the course of their illness. In inpatient care settings, if not appropriately treated, BPSD can result in care complications, increased length of stay, dissatisfaction with care, and caregiver stress and injury. Although evidence-based, nonpharmacological approaches to treating BPSD exist, their implementation into acute care has been thwarted by limited nursing staff expertise in behavioral health, and a lack of consistent approaches to integrate behavioral health expertise into medically focused inpatient care settings. This article describes the core components of one evidence-based approach to integrating behavioral health expertise into dementia care. This approach, called STAR-VA, was implemented in Veterans' Health Administration community living centers (nursing homes). It has demonstrated effectiveness in reducing the severity and frequency of BPSD, while improving staff knowledge and skills in caring for people with dementia. The potential for adapting this approach in acute care settings is discussed, along with key lessons learned regarding opportunities for nursing leadership to ensure consistent implementation and sustainability. PMID:27259128
Sickmann, Helle Mark; Skoven, Christian; Arentzen, Tina S.;
Prenatal maternal stress increases the predisposition for affective disorders. Furthermore, women appear twice as likely as men to develop stress- and depression-related disorders. Comparable behavioral changes characteristic of clinical depression are found in rat offspring following prenatal...... stress (PS). These include increased helplessness, altered anxiety indicators and sleep modifications. Our purpose was to further investigate behavioral depression indices following PS as well as CNS structural changes including sex specificity of these variables. Pregnant Sprague-Dawley rats were...... locomotor activity, depressive- and anxiety-like behavior as well as sleep architecture. Some animals were analyzed for CNS microstructural changes based on diffusion MRI. Subsets of PS and control rats were exposed to an acute stressor prior to the behavioral tests. Rearing/climbing activity in a familiar...
Regression versus No Regression in the Autistic Disorder: Developmental Trajectories
Bernabei, P.; Cerquiglini, A.; Cortesi, F.; D' Ardia, C.
2007-01-01
Developmental regression is a complex phenomenon which occurs in 20-49% of the autistic population. Aim of the study was to assess possible differences in the development of regressed and non-regressed autistic preschoolers. We longitudinally studied 40 autistic children (18 regressed, 22 non-regressed) aged 2-6 years. The following developmental…
The role of innate immunity in spontaneous regression of cancer
J A Thomas
2011-01-01
Full Text Available Nature has provided us with infections - acute and chronic - and these infections have both harmful and beneficial effects on the human system. Worldwide, a number of chronic infections are associated with a risk of cancer, but it is also known that cancer regresses when associated with acute infections such as bacterial, viral, fungal, protozoal, etc. Acute infections are known to cure chronic diseases since the time of Hippocrates. The benefits of these fever producing acute infections has been applied in cancer vaccinology such as the Coley′s toxins. Immune cells like the natural killer cells, macrophages and dendritic cells have taken greater precedence in cancer immunity than ever before. This review provides an insight into the benefits of fever and its role in prevention of cancer, the significance of common infections in cancer regression, the dual nature of our immune system and the role of the often overlooked primary innate immunity in tumor immunology and spontaneous regression of cancer.
Bache, Stefan Holst
A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is......, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question....
Miriam Kron
2014-09-01
Full Text Available Reduced levels of brain-derived neurotrophic factor (BDNF are thought to contribute to the pathophysiology of Rett syndrome (RTT, a severe neurodevelopmental disorder caused by loss-of-function mutations in the gene encoding methyl-CpG-binding protein 2 (MeCP2. In Mecp2 mutant mice, BDNF deficits have been associated with breathing abnormalities, a core feature of RTT, as well as with synaptic hyperexcitability within the brainstem respiratory network. Application of BDNF can reverse hyperexcitability in acute brainstem slices from Mecp2-null mice, suggesting that therapies targeting BDNF or its receptor, TrkB, could be effective at acute reversal of respiratory abnormalities in RTT. Therefore, we examined the ability of LM22A-4, a small-molecule BDNF loop-domain mimetic and TrkB partial agonist, to modulate synaptic excitability within respiratory cell groups in the brainstem nucleus tractus solitarius (nTS and to acutely reverse abnormalities in breathing at rest and during behavioral arousal in Mecp2 mutants. Patch-clamp recordings in Mecp2-null brainstem slices demonstrated that LM22A-4 decreases excitability at primary afferent synapses in the nTS by reducing the amplitude of evoked excitatory postsynaptic currents and the frequency of spontaneous and miniature excitatory postsynaptic currents. In vivo, acute treatment of Mecp2-null and -heterozygous mutants with LM22A-4 completely eliminated spontaneous apneas in resting animals, without sedation. Moreover, we demonstrate that respiratory dysregulation during behavioral arousal, a feature of human RTT, is also reversed in Mecp2 mutants by acute treatment with LM22A-4. Together, these data support the hypothesis that reduced BDNF signaling and respiratory dysfunction in RTT are linked, and establish the proof-of-concept that treatment with a small-molecule structural mimetic of a BDNF loop domain and a TrkB partial agonist can acutely reverse abnormal breathing at rest and in response to
Caporino, Nicole E; Herres, Joanna; Kendall, Philip C; Wolk, Courtney Benjamin
2016-08-01
This study evaluated the impact of dysregulation across cognitive, affective, and behavioral domains on acute and 7- to 19-year follow-up outcomes of cognitive-behavioral therapy (CBT) for anxiety, and explored dysregulation as a predictor of psychopathology and impairment in young adulthood among individuals who received anxiety treatment as youth. Participants (N = 64; 50 % female, 83 % non-Hispanic White) from two randomized clinical trials completed a follow-up assessment 7-19 years later. Latent profile analysis identified dysregulation based on Anxious/Depressed, Attention Problems, and Aggressive Behavior scores on the Child Behavior Checklist. Although pretreatment dysregulation was not related to acute or follow-up outcomes for anxiety diagnoses that were the focus of treatment, dysregulation predicted an array of non-targeted psychopathology at follow-up. Among youth with a principal anxiety disorder, the effects of CBT (Coping Cat) appear to be robust against broad impairments in self-regulation. However, youth with a pretreatment dysregulation profile likely need follow-up to monitor for the emergence of other disorders. PMID:26384978
Eliana Peresi
2008-11-01
Full Text Available OBJETIVO: Analisar o padrão de citocinas pró- e antiinflamatórias e da resposta de fase aguda (RFA como marcadores de resposta ao tratamento da tuberculose pulmonar. MÉTODOS: Determinação dos níveis de interferon-gama (IFN-γ, tumor necrosis factor-alpha (TNF-α, fator de necrose tumoral-alfa, interleucina-10 (IL-10 e transforming growth factor-beta (TGF-β, fator transformador de crescimento-beta, pelo método ELISA, em sobrenadante de cultura de células mononucleares do sangue periférico e monócitos, assim como dos níveis de proteínas totais, albumina, globulinas, alfa-1-glicoproteína ácida (AGA, proteína C reativa (PCR e velocidade de hemossedimentação (VHS em 28 doentes com tuberculose pulmonar, em três tempos: antes (T0, aos três meses (T3 e aos seis meses (T6 de tratamento, em relação aos controles saudáveis, em um único tempo. RESULTADOS: Os pacientes apresentaram valores maiores de citocinas e RFA que os controles em T0, com diminuição em T3 e diminuição (TNF-α, IL-10, TGF-β, AGA e VHS ou normalização (IFN-γ e PCR em T6. CONCLUSÕES: PCR, AGA e VHS são possíveis marcadores para auxiliar no diagnóstico de tuberculose pulmonar e na indicação de tratamento de indivíduos com baciloscopia negativa; PCR (T0 > T3 > T6 = referência pode também ser marcador de resposta ao tratamento. Antes do tratamento, o perfil Th0 (IFN-γ, IL-10, TNF-α e TGF-β, indutor de e protetor contra inflamação, prevaleceu nos pacientes; em T6, prevaleceu o perfil Th2 (IL-10, TNF-α e TGF-β, protetor contra efeito nocivo pró-inflamatório do TNF-α ainda presente. O comportamento do IFN-γ (T0 > T3 > T6 = controle sugere sua utilização como marcador de resposta ao tratamento.OBJECTIVE: To evaluate the pattern of pro-inflammatory cytokines, anti-inflammatory cytokines and the acute phase response (APR as markers of the response to treatment of pulmonary tuberculosis. METHODS: Twenty-eight patients with pulmonary tuberculosis
Bordacconi, Mats Joe; Larsen, Martin Vinæs
2014-01-01
Humans are fundamentally primed for making causal attributions based on correlations. This implies that researchers must be careful to present their results in a manner that inhibits unwarranted causal attribution. In this paper, we present the results of an experiment that suggests regression...... models should note carefully both their models’ identifying assumptions and which causal attributions can safely be concluded from their analysis....
Bayesian logistic regression analysis
Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.
2012-01-01
In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an
Multiple linear regression analysis
Edwards, T. R.
1980-01-01
Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.
Bounded Gaussian process regression
Jensen, Bjørn Sand; Nielsen, Jens Brehm; Larsen, Jan
2013-01-01
We extend the Gaussian process (GP) framework for bounded regression by introducing two bounded likelihood functions that model the noise on the dependent variable explicitly. This is fundamentally different from the implicit noise assumption in the previously suggested warped GP framework. We...
The resident-intruder paradigm was used to assess the effects of gamma radiation (0, 3, 5, 7 Gray [Gy] cobalt-60) on aggressive offensive behavior in resident male mice over a 3-month period. The defensive behavior of nonirradiated intruder mice was also monitored. A dose of 3 Gy had no effect on either the residents' offensive behavior or the defensive behavior of the intruders paired with them. Doses of 5 and 7 Gy produced decreases in offensive behavior of irradiated residents during the second week postirradiation. The nonirradiated intruders paired with these animals displayed decreases in defensive behavior during this time period, indicating a sensitivity to changes in the residents' behavior. After the third week postirradiation, offensive and defensive behavior did not differ significantly between irradiated mice and sham-irradiated controls. This study suggests that sublethal doses of radiation can temporarily suppress aggressive behavior but have no apparent permanent effect on that behavior
Li, Xiang; Li, Xu; Li, Yi-Xiang; Zhang, Yuan; Chen, Di; Sun, Ming-Zhu; Zhao, Xin; Chen, Dong-Yan; Feng, Xi-Zeng
2015-01-01
We describe an interdisciplinary comparison of the effects of acute and chronic alcohol exposure in terms of their disturbance of light, dark and color preferences and the occurrence of Parkinson-like behavior in zebrafish through computer visual tracking, data mining, and behavioral and physiological analyses. We found that zebrafish in anxiolytic and anxious states, which are induced by acute and chronic repeated alcohol exposure, respectively, display distinct emotional reactions in light/dark preference tests as well as distinct learning and memory abilities in color-enhanced conditional place preference (CPP) tests. Additionally, compared with the chronic alcohol (1.0%) treatment, acute alcohol exposure had a significant, dose-dependent effect on anxiety, learning and memory (color preference) as well as locomotive activities. Acute exposure doses (0.5%, 1.0%, and 1.5%) generated an "inverted V" dose-dependent pattern in all of the behavioral parameters, with 1.0% having the greatest effect, while the chronic treatment had a moderate effect. Furthermore, by measuring locomotive activity, learning and memory performance, the number of dopaminergic neurons, tyrosine hydroxylase expression, and the change in the photoreceptors in the retina, we found that acute and chronic alcohol exposure induced varying degrees of Parkinson-like symptoms in zebrafish. Taken together, these results illuminated the behavioral and physiological mechanisms underlying the changes associated with learning and memory and the cause of potential Parkinson-like behaviors in zebrafish due to acute and chronic alcohol exposure. PMID:26558894
Ridge Regression: A Regression Procedure for Analyzing Correlated Independent Variables.
Rakow, Ernest A.
Ridge regression is presented as an analytic technique to be used when predictor variables in a multiple linear regression situation are highly correlated, a situation which may result in unstable regression coefficients and difficulties in interpretation. Ridge regression avoids the problem of selection of variables that may occur in stepwise…
Subset selection in regression
Miller, Alan
2002-01-01
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:A separate chapter on Bayesian methodsComplete revision of the chapter on estimationA major example from the field of near infrared spectroscopyMore emphasis on cross-validationGreater focus on bootstrappingStochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presentedMore examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting...
Low rank Multivariate regression
Giraud, Christophe
2010-01-01
We consider in this paper the multivariate regression problem, when the target regression matrix $A$ is close to a low rank matrix. Our primary interest in on the practical case where the variance of the noise is unknown. Our main contribution is to propose in this setting a criterion to select among a family of low rank estimators and prove a non-asymptotic oracle inequality for the resulting estimator. We also investigate the easier case where the variance of the noise is known and outline that the penalties appearing in our criterions are minimal (in some sense). These penalties involve the expected value of the Ky-Fan quasi-norm of some random matrices. These quantities can be evaluated easily in practice and upper-bounds can be derived from recent results in random matrix theory.
Nonparametric LAD Cointegrating Regression
Toshio Honda
2011-01-01
We deal with nonparametric estimation in a nonlinear cointegration model whose regressor and dependent variable can be contemporaneously correlated. The asymptotic properties of the Nadaraya-Watson estimator are already examined in the literature. In this paper, we consider nonparametric least absolute deviation (LAD) regression and derive the asymptotic distributions of the local constant and local linear estimators by appealing to the local time approach.
Upper expectation parametric regression
Lin, Lu; Dong, Ping; Song, Yunquan; Zhu, Lixing
2014-01-01
Every observation may follow a distribution that is randomly selected in a class of distributions. It is called the distribution uncertainty. This is a fact acknowledged in some research fields such as financial risk measure. Thus, the classical expectation is not identifiable in general.In this paper, a distribution uncertainty is defined, and then an upper expectation regression is proposed, which can describe the relationship between extreme events and relevant covariates under the framewo...
Sublinear expectation linear regression
Lin, Lu; Shi, Yufeng; Wang, Xin; Yang, Shuzhen
2013-01-01
Nonlinear expectation, including sublinear expectation as its special case, is a new and original framework of probability theory and has potential applications in some scientific fields, especially in finance risk measure and management. Under the nonlinear expectation framework, however, the related statistical models and statistical inferences have not yet been well established. The goal of this paper is to construct the sublinear expectation regression and investigate its statistical infe...
Online Sparse Linear Regression
Foster, Dean; Kale, Satyen; Karloff, Howard
2016-01-01
We consider the online sparse linear regression problem, which is the problem of sequentially making predictions observing only a limited number of features in each round, to minimize regret with respect to the best sparse linear regressor, where prediction accuracy is measured by square loss. We give an inefficient algorithm that obtains regret bounded by $\\tilde{O}(\\sqrt{T})$ after $T$ prediction rounds. We complement this result by showing that no algorithm running in polynomial time per i...
ON INTERVAL ESTIMATING REGRESSION
Marcin Michalak
2014-06-01
Full Text Available This paper presents a new look on the well-known nonparametric regression estimator – the Nadaraya-Watson kernel estimator. Though it was invented 50 years ago it still being applied in many fields. After these yearsfoundations of uncertainty theory – interval analysis – are joined with this estimator. The paper presents the background of Nadaraya-Watson kernel estimator together with the basis of interval analysis and shows the interval Nadaraya-Watson kernel estimator.
Sparse Bilinear Logistic Regression
Shi, Jianing V.; Xu, Yangyang; Baraniuk, Richard G.
2014-01-01
In this paper, we introduce the concept of sparse bilinear logistic regression for decision problems involving explanatory variables that are two-dimensional matrices. Such problems are common in computer vision, brain-computer interfaces, style/content factorization, and parallel factor analysis. The underlying optimization problem is bi-convex; we study its solution and develop an efficient algorithm based on block coordinate descent. We provide a theoretical guarantee for global convergenc...
TWO REGRESSION CREDIBILITY MODELS
Constanţa-Nicoleta BODEA
2010-03-01
Full Text Available In this communication we will discuss two regression credibility models from Non – Life Insurance Mathematics that can be solved by means of matrix theory. In the first regression credibility model, starting from a well-known representation formula of the inverse for a special class of matrices a risk premium will be calculated for a contract with risk parameter θ. In the next regression credibility model, we will obtain a credibility solution in the form of a linear combination of the individual estimate (based on the data of a particular state and the collective estimate (based on aggregate USA data. To illustrate the solution with the properties mentioned above, we shall need the well-known representation theorem for a special class of matrices, the properties of the trace for a square matrix, the scalar product of two vectors, the norm with respect to a positive definite matrix given in advance and the complicated mathematical properties of conditional expectations and of conditional covariances.
Otsuka, Tomomi; Nishii, Ayu; Amemiya, Seiichiro; Kubota, Natsuko; Nishijima, Takeshi; Kita, Ichiro
2016-02-01
Accumulating evidence suggests that physical exercise can reduce and prevent the incidence of stress-related psychiatric disorders, including depression and anxiety. Activation of serotonin (5-HT) neurons in the dorsal raphe nucleus (DRN) is implicated in antidepressant/anxiolytic properties. In addition, the incidence and symptoms of these disorders may involve dysregulation of the hypothalamic-pituitary-adrenal axis that is initiated by corticotropin-releasing factor (CRF) neurons in the hypothalamic paraventricular nucleus (PVN). Thus, it is possible that physical exercise produces its antidepressant/anxiolytic effects by affecting these neuronal activities. However, the effects of acute physical exercise at different intensities on these neuronal activation and behavioral changes are still unclear. Here, we examined the activities of 5-HT neurons in the DRN and CRF neurons in the PVN during 30 min of treadmill running at different speeds (high speed, 25 m/min; low speed, 15m/min; control, only sitting on the treadmill) in male Wistar rats, using c-Fos/5-HT or CRF immunohistochemistry. We also performed the elevated plus maze test and the forced swim test to assess anxiety- and depressive-like behaviors, respectively. Acute treadmill running at low speed, but not high speed, significantly increased c-Fos expression in 5-HT neurons in the DRN compared to the control, whereas high-speed running significantly enhanced c-Fos expression in CRF neurons in the PVN compared with the control and low-speed running. Furthermore, low-speed running resulted in decreased anxiety- and depressive-like behaviors compared with high-speed running. These results suggest that acute physical exercise with mild and low stress can efficiently induce optimal neuronal activation that is involved in the antidepressant/anxiolytic effects. PMID:26542811
Vasconcelos, Ana M; Daam, Michiel A; Dos Santos, Liliana R A; Sanches, Ana L M; Araújo, Cristiano V M; Espíndola, Evaldo L G
2016-04-01
As compared to other aquatic organism groups, relatively few studies have been conducted so far evaluating the toxicity of pesticides to amphibians. This may at least partly be due to the fact that regulations for registering pesticides usually do not require testing amphibians. The sensitivity of amphibians is generally considered to be covered by that based on toxicity tests with other aquatic organisms (e.g. fish) although the impact of a pesticide on amphibians may be very different. In the present study, acute and chronic laboratory tests were conducted to evaluate the acute and chronic toxicity of abamectin (as Vertimec(®) 18EC) to bullfrog (Lithobates catesbeianus) tadpoles. Acute tests were conducted at two tadpole stages (Gosner stage 21G and 25G) and avoidance tests were also conducted with stage Gosner stage 21G tadpoles. Calculated acute toxicity values were greater than those reported for standard fish test species, hence supporting the use of fish toxicity data as surrogates for amphibians in acute risk assessments. Given the limited number and extent of available amphibian toxicity studies, however, research needs to increase our understanding of pesticide toxicity to amphibians are discussed. PMID:26758616
Multiatlas segmentation as nonparametric regression.
Awate, Suyash P; Whitaker, Ross T
2014-09-01
This paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation. We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator's convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator. We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems. PMID:24802528
Correlation and simple linear regression.
Eberly, Lynn E
2007-01-01
This chapter highlights important steps in using correlation and simple linear regression to address scientific questions about the association of two continuous variables with each other. These steps include estimation and inference, assessing model fit, the connection between regression and ANOVA, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex statistical techniques, such as multiple linear regression, linear mixed effects models, logistic regression, and proportional hazards regression. PMID:18450049
Maurizio Casarrubea
2015-06-01
Nicotine is one of the most addictive drugs of abuse. Tobacco smoking is a major cause of many health problems, and is the first preventable cause of death worldwide. Several findings show that nicotine exerts significant aversive as well as the well-known rewarding motivational effects. Less certain is the anatomical substrate that mediates or enables nicotine aversion. Here, we show that acute nicotine induces anxiogenic effects in rats at the doses investigated (0.1, 0.5, and 1.0 mg/kg, i.p., as measured by the hole-board apparatus and manifested in behaviors such as decreased rearing and head-dipping and increased grooming. No changes in locomotor behavior were observed at any of the nicotine doses given. T-pattern analysis of the behavioral outcomes revealed a drastic reduction and disruption of complex behavioral patterns induced by all three nicotine doses, with the maximum effect for 1 mg/kg. Lesion of the lateral habenula (LHb induced hyperlocomotion and, strikingly, reversed the nicotine-induced anxiety obtained at 1 mg/kg to an anxiolytic-like effect, as shown by T-pattern analysis. We suggest that the LHb is critically involved in emotional behavior states and in nicotine-induced anxiety, most likely through modulation of monoaminergic nuclei.
Zhang, Feipeng; Li, Qunhua
2016-01-01
We introduce a rank-based bent linear regression with an unknown change point. Using a linear reparameterization technique, we propose a rank-based estimate that can make simultaneous inference on all model parameters, including the location of the change point, in a computationally efficient manner. We also develop a score-like test for the existence of a change point, based on a weighted CUSUM process. This test only requires fitting the model under the null hypothesis in absence of a chang...
Adaptive Metric Kernel Regression
Goutte, Cyril; Larsen, Jan
1998-01-01
Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression by...... minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard...
Adaptive metric kernel regression
Goutte, Cyril; Larsen, Jan
2000-01-01
Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate...... regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the...
Steganalysis using logistic regression
Lubenko, Ivans; Ker, Andrew D.
2011-02-01
We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods - it estimates class probabilities as well as providing a simple classification - and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classification, and it shows comparable classification accuracy to SVM methods. This work is a case study, comparing accuracy and speed of SVM and LR classifiers in detection of LSB Matching and other related spatial-domain image steganography, through the state-of-art 686-dimensional SPAM feature set, in three image sets.
Nesrin Şen Celasin; Sibel Sönmez; Zümrüt Başbakkal; Figen Esenay
2010-01-01
The study is executed with mothers of children aged 3-6 (n=170) whose children were hospitalized for the first time between the dates of 15.07.2003 and 15.06.2006, who were reachable by phone and accepted to participate in the study aiming determination of behavioral reactions of a child of 3-6 ages group to be hospitalized due to an acute illness.In this study, for data gathering "Personal Information Form" including 15 questions and "Inquiry Form of Behavior Changes of 3-6 Ages Group Child...
Diagostische Profile bei akuten Bauchschmerzen mit multinominaler logistischer Regression
Verde, PE; Decker, F; Yang, Q.; Franke, C; Ohmann, C
2007-01-01
Purpose: Application of multinomial logistic regression for diagnostic support of acute abdominal pain, a diagnostic problem with many differential diagnoses. Methods: The analysis is based on a prospective data base with 2280 patients with acute abdominal pain, characterized by 87 variables from history and clinical examination and 12 differential diagnoses. Associations between single variables from history and clinical examination and the final diagnoses were investigated with multi...
Amin, Shaimaa Nasr; El-Aidi, Ahmed Amro; Ali, Mohamed Mostafa; Attia, Yasser Mahmoud; Rashed, Laila Ahmed
2015-06-01
Stress is any condition that impairs the balance of the organism physiologically or psychologically. The response to stress involves several neurohormonal consequences. Glutamate is the primary excitatory neurotransmitter in the central nervous system, and its release is increased by stress that predisposes to excitotoxicity in the brain. Memantine is an uncompetitive N-methyl D-aspartate glutamatergic receptors antagonist and has shown beneficial effect on cognitive function especially in Alzheimer's disease. The aim of the work was to investigate memantine effect on memory and behavior in animal models of acute and repeated restraint stress with the evaluation of serum markers of stress and the expression of hippocampal markers of synaptic plasticity. Forty-two male rats were divided into seven groups (six rats/group): control, acute restraint stress, acute restraint stress with Memantine, repeated restraint stress, repeated restraint stress with Memantine and Memantine groups (two subgroups as positive control). Spatial working memory and behavior were assessed by performance in Y-maze. We evaluated serum cortisol, tumor necrotic factor, interleukin-6 and hippocampal expression of brain-derived neurotrophic factor, synaptophysin and calcium-/calmodulin-dependent protein kinase II. Our results revealed that Memantine improved spatial working memory in repeated stress, decreased serum level of stress markers and modified the hippocampal synaptic plasticity markers in both patterns of stress exposure; in ARS, Memantine upregulated the expression of synaptophysin and brain-derived neurotrophic factor and downregulated the expression of calcium-/calmodulin-dependent protein kinase II, and in repeated restraint stress, it upregulated the expression of synaptophysin and downregulated calcium-/calmodulin-dependent protein kinase II expression. PMID:25680935
Karim Hardani*
2012-05-01
Full Text Available A 10-month-old baby presented with developmental delay. He had flaccid paralysis on physical examination.An MRI of the spine revealed malformation of the ninth and tenth thoracic vertebral bodies with complete agenesis of the rest of the spine down that level. The thoracic spinal cord ends at the level of the fifth thoracic vertebra with agenesis of the posterior arches of the eighth, ninth and tenth thoracic vertebral bodies. The roots of the cauda equina appear tightened down and backward and ended into a subdermal fibrous fatty tissue at the level of the ninth and tenth thoracic vertebral bodies (closed meningocele. These findings are consistent with caudal regression syndrome.
Adaptive functional linear regression
Comte, Fabienne
2011-01-01
We consider the estimation of the slope function in functional linear regression, where scalar responses are modeled in dependence of random functions. Cardot and Johannes [2010] have shown that a thresholded projection estimator can attain up to a constant minimax-rates of convergence in a general framework which allows to cover the prediction problem with respect to the mean squared prediction error as well as the estimation of the slope function and its derivatives. This estimation procedure, however, requires an optimal choice of a tuning parameter with regard to certain characteristics of the slope function and the covariance operator associated with the functional regressor. As this information is usually inaccessible in practice, we investigate a fully data-driven choice of the tuning parameter which combines model selection and Lepski's method. It is inspired by the recent work of Goldenshluger and Lepski [2011]. The tuning parameter is selected as minimizer of a stochastic penalized contrast function...
Green, Nella; Hoenigl, Martin; Morris, Sheldon; Little, Susan J
2015-10-01
The transgender community represents an understudied population in the literature. The objective of this study was to compare risk behavior, and HIV and sexually transmitted infection (STI) rates between transgender women and transgender men undergoing community-based HIV testing.With this retrospective analysis of a cohort study, we characterize HIV infection rates as well as reported risk behaviors and reported STI in 151 individual transgender women and 30 individual transgender men undergoing community based, voluntary screening for acute and early HIV infection (AEH) in San Diego, California between April 2008 and July 2014.HIV positivity rate was low for both, transgender women and transgender men undergoing AEH screening (2% and 3%, respectively), and the self-reported STI rate for the prior 12 months was 13% for both. Although transgender women appeared to engage in higher rates of risk behavior overall, with 69% engaged in condomless receptive anal intercourse (CRAI) and 11% engaged in sex work, it is important to note that 91% of transgender women reported recent sexual intercourse, 73% had more than 1 sexual partner, 63% reported intercourse with males, 37% intercourse with males and females, and 30% had CRAI.Our results indicate that in some settings rates of HIV infection, as well as rates of reported STIs and sexual risk behavior in transgender men may resemble those found in transgender women. Our findings support the need for comprehensive HIV prevention in both, transgender women and men. PMID:26469928
Babinski, Dara E; Waxmonsky, James G; Pelham, William E
2014-10-01
This multiple baseline study evaluated the efficacy of behavioral parent training (BPT) for 12 parents (M age = 39.17 years; 91% mothers) and their children (ages 6-12; 83% boys) both with Attention-Deficit/Hyperactivity Disorder (ADHD), and also explored the acute effect of stimulant medication for parents before and after BPT. Parents rated their own and their children's symptoms and impairment and were stabilized on optimally dosed medication. Then, parents discontinued medication and were randomly assigned to a 3, 4, or 5 week baseline (BL), during which they provided twice-weekly ratings of their impairment, parenting, and their child's behavior. Following BL, parents and their children completed two laboratory tasks, once on their optimally dosed medication and once on a placebo to assess observable effects of medication on parent-child behavior, and they completed additional assessments of family functioning. Parents then completed eight BPT sessions, during which they were unmedicated. Twice-weekly ratings of parent and child behavior were collected during BPT and additional ratings were collected upon completing BPT. Two more parent-child tasks with and without parent medication were conducted upon BPT completion to assess the observable effects of BPT and BPT plus medication. Ten (83.33%) parents completed the trial. Improvements in parent and child behavior were observed, and parents reported improved child behavior with BPT. Few benefits of BPT emerged through parent reports of parent functioning, with the exception of inconsistent discipline, and no medication or interaction effects emerged. These results, although preliminary, suggest that some parents with ADHD benefit from BPT. While pharmacological treatment is the most common intervention for adults with ADHD, further examination of psychosocial treatments for adults is needed. PMID:24687848
Frankovich, Jennifer; Thienemann, Margo; Pearlstein, Jennifer; Crable, Amber; Brown, Kayla; Chang, Kiki
2015-01-01
Background: Abrupt, dramatic onset obsessive-compulsive disorder (OCD) and/or eating restriction with at least two coinciding symptoms (anxiety, mood dysregulation, irritability/aggression/oppositionality, behavioral regression, cognitive deterioration, sensory or motor abnormalities, or somatic symptoms) defines pediatric acute-onset neuropsychiatric syndrome (PANS). Descriptions of clinical data in such youth are limited.
Regression analysis in quantum language
ISHIKAWA, Shiro
2014-01-01
Although regression analysis has a great history, we consider that it has always continued being confused. For example, the fundamental terms in regression analysis (e.g., "regression", "least-squares method", "explanatory variable", "response variable", etc.) seem to be historically conventional, that is, these words do not express the essence of regression analysis. Recently, we proposed quantum language (or, classical and quantum measurement theory), which is characterized as the linguisti...
Regression with network cohesion
Li, Tianxi; Levina, Elizaveta; Zhu, Ji
2016-01-01
Prediction problems typically assume the training data are independent samples, but in many modern applications samples come from individuals connected by a network. For example, in adolescent health studies of risk-taking behaviors, information on the subjects' social networks is often available and plays an important role through network cohesion, the empirically observed phenomenon of friends behaving similarly. Taking cohesion into account in prediction models should allow us to improve t...
Multinomial logistic regression ensembles.
Lee, Kyewon; Ahn, Hongshik; Moon, Hojin; Kodell, Ralph L; Chen, James J
2013-05-01
This article proposes a method for multiclass classification problems using ensembles of multinomial logistic regression models. A multinomial logit model is used as a base classifier in ensembles from random partitions of predictors. The multinomial logit model can be applied to each mutually exclusive subset of the feature space without variable selection. By combining multiple models the proposed method can handle a huge database without a constraint needed for analyzing high-dimensional data, and the random partition can improve the prediction accuracy by reducing the correlation among base classifiers. The proposed method is implemented using R, and the performance including overall prediction accuracy, sensitivity, and specificity for each category is evaluated on two real data sets and simulation data sets. To investigate the quality of prediction in terms of sensitivity and specificity, the area under the receiver operating characteristic (ROC) curve (AUC) is also examined. The performance of the proposed model is compared to a single multinomial logit model and it shows a substantial improvement in overall prediction accuracy. The proposed method is also compared with other classification methods such as the random forest, support vector machines, and random multinomial logit model. PMID:23611203
Luo, Chongliang; Liu, Jin; Dey, Dipak K; Chen, Kun
2016-07-01
In many fields, multi-view datasets, measuring multiple distinct but interrelated sets of characteristics on the same set of subjects, together with data on certain outcomes or phenotypes, are routinely collected. The objective in such a problem is often two-fold: both to explore the association structures of multiple sets of measurements and to develop a parsimonious model for predicting the future outcomes. We study a unified canonical variate regression framework to tackle the two problems simultaneously. The proposed criterion integrates multiple canonical correlation analysis with predictive modeling, balancing between the association strength of the canonical variates and their joint predictive power on the outcomes. Moreover, the proposed criterion seeks multiple sets of canonical variates simultaneously to enable the examination of their joint effects on the outcomes, and is able to handle multivariate and non-Gaussian outcomes. An efficient algorithm based on variable splitting and Lagrangian multipliers is proposed. Simulation studies show the superior performance of the proposed approach. We demonstrate the effectiveness of the proposed approach in an [Formula: see text] intercross mice study and an alcohol dependence study. PMID:26861909
Combining Alphas via Bounded Regression
Zura Kakushadze
2015-11-01
Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.
Regression Analysis A Constructive Critique
Berk, Richard A
2003-01-01
Regression Analysis: A Constructive Critique identifies a wide variety of problems with regression analysis as it is commonly used and then provides a number of ways in which practice could be improved. Regression is most useful for data reduction, leading to relatively simple but rich and precise descriptions of patterns in a data set. The emphasis on description provides readers with an insightful rethinking from the ground up of what regression analysis can do, so that readers can better match regression analysis with useful empirical questions and improved policy-related research. "An
Zümrüt Başbakkal
2010-02-01
Full Text Available The study is executed with mothers of children aged 3-6 (n=170 whose children were hospitalized for the first time between the dates of 15.07.2003 and 15.06.2006, who were reachable by phone and accepted to participate in the study aiming determination of behavioral reactions of a child of 3-6 ages group to be hospitalized due to an acute illness.In this study, for data gathering "Personal Information Form" including 15 questions and "Inquiry Form of Behavior Changes of 3-6 Ages Group Children After Being Hospitalized" with 30 questions were used. Date gathering forms were carried out as pre-test by using face-to-face interview method with mothers of 3-6 aged children who were hospitalized for the first time and were in first 12 hours of hospitalization. "Inquiry Form of Behavior Changes of 3-6 Ages Group Children After Being Hospitalized" was re-carried out with mothers by phone 1 month after children being discharged from hospital.In analyzing of datas statistical programme of SPSS 13.0 for Windows was used. In statistical evaluation; number-percent dispersion, Wilcoxon Sing Rank test and Paired Sample-t test were used.According to the results obtained from the study, 57.6% of children are male with age average of 4,46±1,18 and 52.3% of them were hospitalized due to Gastroentestinal System Illnesses. A significant difference was determined between average points of behavior changes of 3-6 ages group children hospitalized due to an acute illness before hospitalized (10,735±4,882 and after being discharged from hospital (15,0476±4,306. In the study, it is observed that there are some behavioral changes in children after being hospitalized such as being cranky before going to bed and during eating, disquiet, bed-wetting, seperation anxiety, excessive attachment to a parent, to need help even for the things he/she could accomplish, to have fear from new environments, people or objects, bad temper attacks, fear of doctor/nurse and hospital
Frolov, Alexander; Reyes-Vasquez, Cruz; Dafny, Nachum
2014-01-01
The nucleus accumbens (NAc) has been shown to play a key role in the brain's response to methylphenidate (MPD). The present study focuses on neuronal recording from this structure. The study postulates that repetitive exposure to the same dose of MPD will elicit in some rats behavioral sensitization and in others tolerance. Furthermore, the study postulates that NAc neuronal activity recorded from animals expressing behavioral tolerance after repetitive MPD exposure will be significantly diff...
席翼; 周泉; 刘朝霞
2015-01-01
Objective :To grasp the prevalence situation of primary and secondary school students’ obesity of Shenzhen and to study the dangerous factors resulted in obesity in order to provide the basis for further preventing and controlling the students’ obesity .Methods :The“BMI classification reference for screening overweight and obesity in Chinese school‐age chil‐dren and adolescents proposed by Working Group on Obesity in China (WGOC )”was used to screen the 3 833 primary school and secondary school students at the age of 8‐16 years in Shenzhen .Among which 195 obese students were picked out to form an “ obesity group” , while another 195 students were chosen to form the “control group” .The behavior factors of the two groups were investigated by the method of closed questionnaire (48 items ) and two rounds analysis were made by using single factor χ2 test and non conditional logistic regression model .Results and Conclusion :7 important factors which were recognized the main reasons re‐sulted in the primary and secondary school students’ obesity were selected :how to describe one’s body weight ,how to control body weight ,the living condition ,time of community sports activities ,mother’ s education level ,how long snacks before bedtime ,the amount of food to eat .That is the main reason causing the behavior of primary and middle school students obesi‐ty .Among them ,the living conditions ,education level of mother ,and body weight describation have not been reported in the previous studies .%研究目的：掌握深圳市中、小学学生肥胖的流行情况，探讨导致肥胖的行为危险因素，为进一步预防与控制学生肥胖提供依据。研究方法：采用中国肥胖问题工作组推出的“中国学龄儿童青少年超重、肥胖筛查BMI值分类标准”，对深圳3833名8～17岁普通学生筛查，确定肥胖学生195名，并选定对照组195名，对肥胖组和对照组的390人
An Application on Multinomial Logistic Regression Model
Abdalla M El-Habil
2012-01-01
This study aims to identify an application of Multinomial Logistic Regression model which is one of the important methods for categorical data analysis. This model deals with one nominal/ordinal response variable that has more than two categories, whether nominal or ordinal variable. This model has been applied in data analysis in many areas, for example health, social, behavioral, and educational.To identify the model by practical way, we used real data on physical violence against children...
Time-adaptive quantile regression
Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik
2008-01-01
An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with...... wind power production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....
Fisette, Alexandre; Fernandes, Maria F.; Hryhorczuk, Cécile; Poitout, Vincent; Alquier, Thierry; Fulton, Stephanie
2016-01-01
Background: GPR120 (FFAR4) is a G-protein coupled receptor implicated in the development of obesity and the antiinflammatory and insulin-sensitizing effects of omega-3 (ω-3) polyunsaturated fatty acids. Increasing central ω-3 polyunsaturated fatty acid levels has been shown to have both anorectic and anxiolytic actions. Despite the strong clinical interest in GPR120, its role in the brain is largely unknown, and thus we sought to determine the impact of central GPR120 pharmacological activation on energy balance, food reward, and anxiety-like behavior. Methods: Male C57Bl/6 mice with intracerebroventricular cannulae received a single injection (0.1 or 1 µM) or continuous 2-week infusion (1 µM/d; mini-pump) of a GPR120 agonist or vehicle. Free-feeding intake, operant lever-pressing for palatable food, energy expenditure (indirect calorimetry), and body weight were measured. GPR120 mRNA expression was measured in pertinent brain areas. Anxiety-like behavior was assessed in the elevated-plus maze and open field test. Results: GPR120 agonist injections substantially reduced chow intake during 4 hours postinjection, suppressed the rewarding effects of high-fat/-sugar food, and blunted approach-avoidance behavior in the open field. Conversely, prolonged central GPR120 agonist infusions reduced anxiety-like behavior in the elevated-plus maze and open field, yet failed to affect free-feeding intake, energy expenditure, and body weight on a high-fat diet. Conclusion: Acute reductions in food intake and food reward suggest that GPR120 could mediate the effects of central ω-3 polyunsaturated fatty acids to inhibit appetite. The anxiolytic effect elicited by GPR120 agonist infusions favors the testing of compounds that can enter the brain to activate GPR120 for the mitigation of anxiety. PMID:26888796
Kunjbihari Sulakhiya
2016-01-01
Full Text Available Background: Stress plays a significant role in the pathogenesis of neuropsychiatric disorders such as anxiety and depression. Beta vulgaris is commonly known as "beet root" possessing antioxidant, anticancer, hepatoprotective, nephroprotective, wound healing, and anti-inflammatory properties. Objective: To study the protective effect of Beta vulgaris Linn. ethanolic extract (BVEE of leaves against acute restraint stress (ARS-induced anxiety- and depressive-like behavior and oxidative stress in mice. Materials and Methods: Mice (n = 6 were pretreated with BVEE (100 and 200 mg/kg, p. o. for 7 days and subjected to ARS for 6 h to induce behavioral and biochemical changes. Anxiety- and depressive-like behavior were measured by using different behavioral paradigms such as open field test (OFT, elevated plus maze (EPM, forced swim test (FST, and tail suspension test (TST 40 min postARS. Brain homogenate was used to analyze oxidative stress parameters, that is, malondialdehyde (MDA and reduced glutathione (GSH level. Results: BVEE pretreatment significantly (P < 0.05 reversed the ARS-induced reduction in EPM parameters, that is, percentage entries and time spent in open arms and in OFT parameters, that is, line crossings, and rearings in mice. ARS-induced increase in the immobility time in FST and TST was attenuated significantly (P < 0.05 by BVEE pretreatment at both the dosage. An increase in MDA and depletion of GSH level postARS was prevented significantly (P < 0.05 with BVEE pretreatment at both the dosage (100 and 200 mg/kg. Conclusion: BVEE exhibits anxiolytic and antidepressant activity in stressed mice along with good antioxidant property suggesting its therapeutic potential in the treatment of stress-related psychiatric disorders.
Regressive Evolution in Astyanax Cavefish
Jeffery, William R.
2009-01-01
A diverse group of animals, including members of most major phyla, have adapted to life in the perpetual darkness of caves. These animals are united by the convergence of two regressive phenotypes, loss of eyes and pigmentation. The mechanisms of regressive evolution are poorly understood. The teleost Astyanax mexicanus is of special significance in studies of regressive evolution in cave animals. This species includes an ancestral surface dwelling form and many con-specific cave-dwelling for...
Polynomial Regression on Riemannian Manifolds
Hinkle, Jacob; Fletcher, P Thomas; Joshi, Sarang
2012-01-01
In this paper we develop the theory of parametric polynomial regression in Riemannian manifolds and Lie groups. We show application of Riemannian polynomial regression to shape analysis in Kendall shape space. Results are presented, showing the power of polynomial regression on the classic rat skull growth data of Bookstein as well as the analysis of the shape changes associated with aging of the corpus callosum from the OASIS Alzheimer's study.
Regression Testing Cost Reduction Suite
Mohamed Alaa El-Din; Ismail Abd El-Hamid Taha; Hesham El-Deeb
2014-01-01
The estimated cost of software maintenance exceeds 70 percent of total software costs [1], and large portion of this maintenance expenses is devoted to regression testing. Regression testing is an expensive and frequently executed maintenance activity used to revalidate the modified software. Any reduction in the cost of regression testing would help to reduce the software maintenance cost. Test suites once developed are reused and updated frequently as the software evolves. As a result, some...
Olivier, Jocelien D A; de Jong, Trynke R; Jos Dederen, P; van Oorschot, Ruud; Heeren, Dick; Pattij, Tommy; Waldinger, Marcel D; Coolen, Lique M; Cools, Alexander R; Olivier, Berend; Veening, Jan G
2007-01-01
Apomorphine is a non-selective dopaminergic receptor agonist. Because of its pro-erectile effects, apomorphine is clinically used for treatment of erectile dysfunction. We investigated the effects of subcutaneous apomorphine administration (0.4 mg/kg rat) on sexual behavior and mating-induced Fos-ex
Business applications of multiple regression
Richardson, Ronny
2015-01-01
This second edition of Business Applications of Multiple Regression describes the use of the statistical procedure called multiple regression in business situations, including forecasting and understanding the relationships between variables. The book assumes a basic understanding of statistics but reviews correlation analysis and simple regression to prepare the reader to understand and use multiple regression. The techniques described in the book are illustrated using both Microsoft Excel and a professional statistical program. Along the way, several real-world data sets are analyzed in deta
Quantile regression theory and applications
Davino, Cristina; Vistocco, Domenico
2013-01-01
A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and
Honda, Miwako; Ito, Mio; Ishikawa, Shogo; Takebayashi, Yoichi; Tierney, Lawrence
2016-01-01
Management of Behavioral and Psychological Symptoms of Dementia (BPSD) is a key challenge in geriatric dementia care. A multimodal comprehensive care methodology, Humanitude, with eye contact, verbal communication, and touch as its elements, was provided to three geriatric dementia patients for whom conventional nursing care failed in an acute care hospital. Each episode was evaluated by video analysis. All patients had advanced dementia with BPSD. Failure of care was identified by patient's shouting, screaming, or abrupt movements of limbs. In this case series, conventional care failed for all three patients. Each element of care communication was much shorter than in Humanitude care, which was accepted by the patients. The average of the elements performed during the care was eye contact 0.6%, verbal communication 15.7%, and touch 0.1% in conventional care and 12.5%, 54.8%, and 44.5% in Humanitude care, respectively. The duration of aggressive behavior of each patient during care was 25.0%, 25.4%, and 66.3% in conventional care and 0%, 0%, and 0.3% in Humanitude, respectively. In our case series, conventional care was provided by less eye contact, verbal communication, and touch. The multimodal comprehensive care approach, Humanitude, decreased BPSD and showed success by patients' acceptance of care. PMID:27069478
Reveron, Maria Elena; Maier, Esther Y.; Duvauchelle, Christine L.
2009-01-01
3,4-methylenedioxymethamphetamine (MDMA) is a popular methamphetamine derivative associated with young adults and all-night dance parties. However, the enduring effects of MDMA at voluntary intake levels have not been extensively investigated. In this study, MDMA-influenced behaviors and core temperatures were assessed over the course of 20 daily MDMA self-administration sessions in rats. In vivo microdialysis techniques were used in a subsequent MDMA challenge test session to determine extra...
Assessing risk factors for periodontitis using regression
Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa
2013-10-01
Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.
Fish, E W; Holloway, H T; Rumple, A; Baker, L K; Wieczorek, L A; Moy, S S; Paniagua, B; Parnell, S E
2016-09-15
Prenatal alcohol exposure (PAE) can induce physical malformations and behavioral abnormalities that depend in part on thedevelopmental timing of alcohol exposure. The current studies employed a mouse FASD model to characterize the long-term behavioral and brain structural consequences of a binge-like alcohol exposure during neurulation; a first-trimester stage when women are typically unaware that they are pregnant. Time-mated C57BL/6J female mice were administered two alcohol doses (2.8g/kg, four hours apart) or vehicle starting at gestational day 8.0. Male and female adolescent offspring (postnatal day 28-45) were then examined for motor activity (open field and elevated plus maze), coordination (rotarod), spatial learning and memory (Morris water maze), sensory motor gating (acoustic startle and prepulse inhibition), sociability (three-chambered social test), and nociceptive responses (hot plate). Regional brain volumes and shapes were determined using magnetic resonance imaging. In males, PAE increased activity on the elevated plus maze and reduced social novelty preference, while in females PAE increased exploratory behavior in the open field and transiently impaired rotarod performance. In both males and females, PAE modestly impaired Morris water maze performance and decreased the latency to respond on the hot plate. There were no brain volume differences; however, significant shape differences were found in the cerebellum, hypothalamus, striatum, and corpus callosum. These results demonstrate that alcohol exposure during neurulation can have functional consequences into adolescence, even in the absence of significant brain regional volumetric changes. However, PAE-induced regional shape changes provide evidence for persistent brain alterations and suggest alternative clinical diagnostic markers. PMID:27185739
Evaluation of the acute behavioral effects and abuse potential of a C8-C9 isoparaffin solvent.
Balster, R L; Bowen, S E; Evans, E B; Tokarz, M E
1997-07-01
We hypothesized that the abuse potential of certain types of inhalants could be evaluated in animals by determining the overlap in their profile of behavioral effects with that of CNS depressant drugs and other depressant-like abused inhalants. For our first attempt in evaluating a solvent with an unknown abuse potential we tested ISOPAR-E. ISOPAR-E is a mixture of predominantly C8-C9 isoparaffinic hydrocarbons that is being used more and more frequently as a solvent in industrial and consumer products, including, but not limited to, typewriter correction fluids. Presently, nothing is known about the potential for abuse of products containing this solvent. In the present studies, we compared the volatility of ISOPAR-E and the abused solvent 1,1,1-trichloroethane (TCE) in our exposure systems. Additionally, five behavioral procedures were conducted in mice to compare the effects of the two compounds. The results demonstrate that: (1) ISOPAR-E was less volatile than TCE; (2) ISOPAR-E produced a somewhat different profile of effects than did TCE as assessed with a functional observational battery; (3) unlike TCE, ISOPAR-E did not affect performance on tests of motor coordination; (4) TCE and ISOPAR-E produced concentration-related decreases in schedule-controlled operant performance with recovery from TCE being somewhat more rapid; (5) ISOPAR-E produced cross dependence in TCE-dependent mice; and (6) both TCE and ISOPAR-E produced substantial levels of ethanol-lever responding in a drug discrimination procedure, although the ethanol-like effects of ISOPAR-E only occurred at response rate decreasing concentrations. Overall, there was a poorer separation of behavioral and lethal concentrations for ISOPAR-E than for TCE. Although a somewhat different profile of behavioral effects was obtained with ISOPAR-E and TCE, we cannot say with certainty if enough similarities exist with abused inhalants to predict that ISOPAR-E would be subject to depressant-like abuse
... of bronchitis: acute and chronic. Most cases of acute bronchitis get better within several days. But your cough ... that cause colds and the flu often cause acute bronchitis. These viruses spread through the air when people ...
Regression analysis with categorized regression calibrated exposure: some interesting findings
Hjartåker Anette
2006-07-01
Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a
Biplots in Reduced-Rank Regression
Braak, ter C.J.F.; Looman, C.W.N.
1994-01-01
Regression problems with a number of related response variables are typically analyzed by separate multiple regressions. This paper shows how these regressions can be visualized jointly in a biplot based on reduced-rank regression. Reduced-rank regression combines multiple regression and principal c
Abstract Expression Grammar Symbolic Regression
Korns, Michael F.
This chapter examines the use of Abstract Expression Grammars to perform the entire Symbolic Regression process without the use of Genetic Programming per se. The techniques explored produce a symbolic regression engine which has absolutely no bloat, which allows total user control of the search space and output formulas, which is faster, and more accurate than the engines produced in our previous papers using Genetic Programming. The genome is an all vector structure with four chromosomes plus additional epigenetic and constraint vectors, allowing total user control of the search space and the final output formulas. A combination of specialized compiler techniques, genetic algorithms, particle swarm, aged layered populations, plus discrete and continuous differential evolution are used to produce an improved symbolic regression sytem. Nine base test cases, from the literature, are used to test the improvement in speed and accuracy. The improved results indicate that these techniques move us a big step closer toward future industrial strength symbolic regression systems.
ORDINAL REGRESSION FOR INFORMATION RETRIEVAL
无
2008-01-01
This letter presents a new discriminative model for Information Retrieval (IR), referred to as Ordinal Regression Model (ORM). ORM is different from most existing models in that it views IR as ordinal regression problem (i.e. ranking problem) instead of binary classification. It is noted that the task of IR is to rank documents according to the user information needed, so IR can be viewed as ordinal regression problem. Two parameter learning algorithms for ORM are presented. One is a perceptron-based algorithm. The other is the ranking Support Vector Machine (SVM). The effectiveness of the proposed approach has been evaluated on the task of ad hoc retrieval using three English Text REtrieval Conference (TREC) sets and two Chinese TREC sets. Results show that ORM significantly outperforms the state-of-the-art language model approaches and OKAPI system in all test sets; and it is more appropriate to view IR as ordinal regression other than binary classification.
Jan eSvoboda
2015-04-01
Full Text Available Patients with schizophrenia often manifest deficits in behavioral flexibility. Non-competitive NMDA receptor antagonists such as MK-801 induce schizophrenia-like symptoms in rodents, including cognitive functions. Despite work exploring flexibility has been done employing behavioral paradigms with simple stimuli, much less is known about what kinds of flexibility are affected in an MK-801 model of schizophrenia-like behavior in the spatial domain. We used a rotating arena-based apparatus (Carousel requiring rats to avoid an unmarked sector defined in either the reference frame of the rotating arena (arena frame task, AF or the stationary room (room frame task, RF. We investigated behavioral flexibility in four conditions involving different cognitive loads. Each condition encompassed an initial (five sessions and a test phase (five sessions in which some aspects of the task were changed to test flexibility in which rats were given saline, 0.05 mg/kg or 0.1 mg/kg MK-801 thirty minutes prior to a session. In the first condition, rats acquired avoidance in RF with clockwise rotation of the arena while in the test phase the arena rotated counterclockwise. In the second condition, rats initially acquired avoidance in RF with the sector on the north and then it was reversed to south (spatial reversal. In the third and fourth conditions, rats initially performed an AF (RF, respectively task, followed by an RF (AF, respectively task, testing the ability of cognitive set-shifting. We found no effect of MK-801 either on simple motor adjustment after reversal of arena rotation or on spatial reversal within the RF. In contrast, administration of MK-801 at a dose of 0.1 mg/kg interfered with set-shifting in both conditions. Furthermore, we observed MK-801 0.1 mg/kg elevated locomotion in all cases. These data suggest that blockade of NMDA receptors by acute system administration of MK-801 preferentially affects set-shifting in the cognitive domain rather
Testing Heteroscedasticity in Robust Regression
Kalina, Jan
2011-01-01
Roč. 1, č. 4 (2011), s. 25-28. ISSN 2045-3345 Grant ostatní: GA ČR(CZ) GA402/09/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : robust regression * heteroscedasticity * regression quantiles * diagnostics Subject RIV: BB - Applied Statistics, Operational Research http://www.researchjournals.co.uk/documents/Vol4/06%20Kalina.pdf
Partially linear censored quantile regression
Neocleous, T.; Portnoy, S.
2009-01-01
Censored regression quantile (CRQ) methods provide a powerful and flexible approach to the analysis of censored survival data when standard linear models are felt to be appropriate. In many cases however, greater flexibility is desired to go beyond the usual multiple regression paradigm. One area of common interest is that of partially linear models: one (or more) of the explanatory covariates are assumed to act on the response through a non-linear function. Here the CRQ approach of Portnoy (...
On Regression Standardization for Moments
CLIFFORD C. CLOGG; SCOTT R. ELIASON
1986-01-01
Polynomial regression models can be used to standardize means of endogenous variables for moments of exogenous variables. In other words, standardized means obtained from polynomial models adjust for group differences in location and shape parameters that characterize distributions of exogenous variables. The suggested approach is a natural extension of the conventional method of regression standardization for means (or first moments) of exogenous variables, and it is a more direct analogue t...
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.
From Rasch scores to regression
Christensen, Karl Bang
2006-01-01
Rasch models provide a framework for measurement and modelling latent variables. Having measured a latent variable in a population a comparison of groups will often be of interest. For this purpose the use of observed raw scores will often be inadequate because these lack interval scale propertie....... This paper compares two approaches to group comparison: linear regression models using estimated person locations as outcome variables and latent regression models based on the distribution of the score....
Regression methods for medical research
Tai, Bee Choo
2013-01-01
Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the
Multiple Regressive Model Adaptive Control
Garipov, Emil; Stoilkov, Teodor; Kalaykov, Ivan
2008-01-01
The essence of the ideas applied to this text consists in the development of the strategy for control of the arbitrary in complexity continuous plant by means of a set of discrete timeinvariant linear controllers. Their number and tuned parameters correspond to the number and parameters of the linear time-invariant regressive models in the model bank, which approximate the complex plant dynamics in different operating points. Described strategy is known as Multiple Regressive Model Adaptive C...
ISIR: Independent Sliced Inverse Regression
Li, Kevin
2013-01-01
International audience In this paper we consider a semiparametric regression model involving a $p$-dimensional explanatory variable ${\\mathbf{x}}$ and including a dimension reduction of ${\\mathbf{x}}$ via an index $B'{\\mathbf{x}}$. In this model, the main goal is to estimate $B$ and to predict the real response variable $Y$ conditionally to ${\\mathbf{x}}$. A standard approach is based on sliced inverse regression (SIR). We propose a new version of this method: the independent sliced invers...
Validation of a heteroscedastic hazards regression model.
Wu, Hong-Dar Isaac; Hsieh, Fushing; Chen, Chen-Hsin
2002-03-01
A Cox-type regression model accommodating heteroscedasticity, with a power factor of the baseline cumulative hazard, is investigated for analyzing data with crossing hazards behavior. Since the approach of partial likelihood cannot eliminate the baseline hazard, an overidentified estimating equation (OEE) approach is introduced in the estimation procedure. It by-product, a model checking statistic, is presented to test for the overall adequacy of the heteroscedastic model. Further, under the heteroscedastic model setting, we propose two statistics to test the proportional hazards assumption. Implementation of this model is illustrated in a data analysis of a cancer clinical trial. PMID:11878222
Nesrin Şen Celasin
2010-02-01
Full Text Available The study is executed with mothers of children aged 3-6 (n=170 whose children were hospitalized for the first time between the dates of 15.07.2003 and 15.06.2006, who were reachable by phone and accepted to participate in the study aiming determination of behavioral reactions of a child of 3-6 ages group to be hospitalized due to an acute illness.In this study, for data gathering "Personal Information Form" including 15 questions and "Inquiry Form of Behavior Changes of 3-6 Ages Group Children After Being Hospitalized" with 30 questions were used. Date gathering forms were carried out as pre-test by using face-to-face interview method with mothers of 3-6 aged children who were hospitalized for the first time and were in first 12 hours of hospitalization. "Inquiry Form of Behavior Changes of 3-6 Ages Group Children After Being Hospitalized" was re-carried out with mothers by phone 1 month after children being discharged from hospital.In analyzing of datas statistical programme of SPSS 13.0 for Windows was used. In statistical evaluation; number-percent dispersion, Wilcoxon Sing Rank test and Paired Sample-t test were used.According to the results obtained from the study, 57.6% of children are male with age average of 4,46±1,18 and 52.3% of them were hospitalized due to Gastroentestinal System Illnesses. A significant difference was determined between average points of behavior changes of 3-6 ages group children hospitalized due to an acute illness before hospitalized (10,735±4,882 and after being discharged from hospital (15,0476±4,306. In the study, it is observed that there are some behavioral changes in children after being hospitalized such as being cranky before going to bed and during eating, disquiet, bed-wetting, seperation anxiety, excessive attachment to a parent, to need help even for the things he/she could accomplish, to have fear from new environments, people or objects, bad temper attacks, fear of doctor/nurse and hospital, fear
Winkler, J.D.
1987-01-01
Mice with unilateral, 6-hydroxydopamine-induced lesions of the corpus striatum were exposed to continuous infusion of apomorphine via a subcutaneously implanted osmotic pump. The turning response of these mice when challenged with an acute injection of apomorphine was significantly reduced at one day after chronic implantation and was totally absent at two and four days after implantation. This effect of continuous exposure to apomorphine was found to be concentration- and time-dependent as well as reversible when the implant was removed. Mice tolerant to apomorphine were cross-tolerant to the rotational effects of the D{sub 1} dopaminergic agonist SKF 38393 and the D{sub 2} dopaminergic agonist Ly 171555, but not to amphetamine. Continuous exposure to apomorphine resulted in a decrease in the binding of ({sup 3}H)spiroperidol (D{sub 2} sites) by 44%, whereas the binding of ({sup 3}H)SCH 23390 (D{sub 1} sites) was not affected. Fluphenazine-N-mustard (FNM) has been shown to bind irreversibly to dopaminergic sites. Experiments using varying doses of FNM demonstrated that FNM inhibited Ly 171555-induced rotational behavior at doses ten fold lower than those required to block rotations induced by SKF 38393. In vitro, FNM inhibited the specific binding of ({sup 3}H) spiroperidol at concentrations ten fold lower than those required to inhibit the binding of ({sup 3}H)Sch23390. In vivo, FNM inhibited the binding of ({sup 3}H) spiroperidol measured ex vivo, but did not inhibit the binding of ({sup 3}H) Sch 23390, even when given in doses as high as 100 mg/kg. These studies indicate that FNM was approximately ten times more potent at inhibiting D{sub 2} than D{sub 1} mediated behavior and at displacing D{sub 2} versus D{sub 1} ligands, suggesting that FNM may be useful for studying and differentiating D{sub 2} and D{sub 1} mediated events.
Hughes, Robert N; Hancock, Nicola J
2016-01-01
To assess the possibility that acute caffeine's behavioral action might depend on rats' strain, effects of 50mg/kg of the drug were observed on activity, anxiety-related behavior and habituation learning in male and female rats from three different strains, namely PVG/c, Long-Evans and Wistar. All subjects were tested in an open field, an elevated plus maze and a light-dark box. For the three strains combined, increased occupancy of the center of the open field and entries of the open plus-maze arms with caffeine suggested caffeine-induced anxiolysis, whereas increased grooming in the open field, decreased rearing in the plus maze and increased risk assessment in the light-dark box were consistent with anxiogenesis. Caffeine also reduced open-field rearing only for PVG/c rats, and entries into and occupation of the light side of the light-dark box only for Long-Evans rats, and increased total defecation in the three types of apparatus for all three strains combined. Overall, caffeine appeared to be mainly anxiogenic. The drug also increased open-field ambulation for PVG/c rats and walking for all rats, but decreased open-field ambulation and entries into the plus maze closed arms for Wistar rats alone. In general, Wistar rats appeared to be the least and Long-Evans the most anxious of the three strains investigated. Caffeine also decreased within-session habituation of open-field ambulation for PVG/c rats alone, thereby suggesting strain-dependent interference with non-associative learning and short-term memory. Several overall sex differences were also observed that supported female rats being more active and less anxious than males. PMID:26577750
Kovalev, G I; Kondrakhin, E A; Salimov, R M; Neznamov, G G
2014-01-01
The effect of acute, 7-fold and 14-fold noopept (1 mg/kg/day) administration on the dynamics of anxiolitic and nootropic behavioral effects in cross-maze, as well as their correlations with NMDA- and BDZ-receptor density was studied in inbred mice strains, differing in exploratory and emotional status--C57BL/6 and BALB/c. The dipeptide failed to affect the anxiety and exploration activity in C57BL/6 mice at each of 3 steps of experimental session. In this strain the B(max) values of [3H]-MK-801 and [3H]-Flunitrazepam binding changed only after single administration. In respect to BALB/c mice noopept induced both the anxiolitic and nootropic effects reaching their maximum on 7th day. In BALB/c strain the dynamics of hippocampal NMDA-receptor binding corresponds to the dynamics of exploratory efficacy whereas the dynamics of BDZ-receptors in prefrontal cortex was reciprocally to dynamics of anxiety level. PMID:25739185
Josse, Jérôme; Guillaume, Christine; Bour, Camille; Lemaire, Flora; Mongaret, Céline; Draux, Florence; Velard, Frédéric; Gangloff, Sophie C
2016-01-01
Staphylococcus aureus is one of the most frequently involved pathogens in bacterial infections such as skin abscess, pneumonia, endocarditis, osteomyelitis, and implant-associated infection. As for bone homeostasis, it is partly altered during infections by S. aureus by the induction of various responses from osteoblasts, which are the bone-forming cells responsible for extracellular matrix synthesis and its mineralization. Nevertheless, bone-forming cells are a heterogeneous population with different stages of maturation and the impact of the latter on their responses toward bacteria remains unclear. We describe the impact of S. aureus on two populations of human primary bone-forming cells (HPBCs) which have distinct maturation characteristics in both acute and persistent models of interaction. Cell maturation did not influence the internalization and survival of S. aureus inside bone-forming cells or the cell death related to the infection. By studying the expression of chemokines, cytokines, and osteoclastogenic regulators by HPBCs, we observed different profiles of chemokine expression according to the degree of cell maturation. However, there was no statistical difference in the amounts of proteins released by both populations in the presence of S. aureus compared to the non-infected counterparts. Our findings show that cell maturation does not impact the behavior of HPBCs infected with S. aureus and suggest that the role of bone-forming cells may not be pivotal for the inflammatory response in osteomyelitis. PMID:27446812
Catatonia in Down syndrome; a treatable cause of regression
Ghaziuddin, Neera; Nassiri, Armin; Miles, Judith H.
2015-01-01
Objective: The main aim of this case series report is to alert physicians to the occurrence of catatonia in Down syndrome (DS). A second aim is to stimulate the study of regression in DS and of catatonia. A subset of individuals with DS is noted to experience unexplained regression in behavior, mood, activities of daily living, motor activities, and intellectual functioning during adolescence or young adulthood. Depression, early onset Alzheimer’s, or just “the Down syndrome” are often blamed...
Entrepreneurial intention modeling using hierarchical multiple regression
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.
Biplots in Reduced-Rank Regression
Braak, ter, C.J.F.; Looman, C.W.N.
1994-01-01
Regression problems with a number of related response variables are typically analyzed by separate multiple regressions. This paper shows how these regressions can be visualized jointly in a biplot based on reduced-rank regression. Reduced-rank regression combines multiple regression and principal components analysis and can therefore be carried out with standard statistical packages. The proposed biplot highlights the major aspects of the regressions by displaying the least-squares approxima...
Interpretation of Standardized Regression Coefficients in Multiple Regression.
Thayer, Jerome D.
The extent to which standardized regression coefficients (beta values) can be used to determine the importance of a variable in an equation was explored. The beta value and the part correlation coefficient--also called the semi-partial correlation coefficient and reported in squared form as the incremental "r squared"--were compared for variables…
Survival Data and Regression Models
Grégoire, G.
2014-12-01
We start this chapter by introducing some basic elements for the analysis of censored survival data. Then we focus on right censored data and develop two types of regression models. The first one concerns the so-called accelerated failure time models (AFT), which are parametric models where a function of a parameter depends linearly on the covariables. The second one is a semiparametric model, where the covariables enter in a multiplicative form in the expression of the hazard rate function. The main statistical tool for analysing these regression models is the maximum likelihood methodology and, in spite we recall some essential results about the ML theory, we refer to the chapter "Logistic Regression" for a more detailed presentation.
Regression filter for signal resolution
The problem considered is that of resolving a measured pulse height spectrum of a material mixture, e.g. gamma ray spectrum, Raman spectrum, into a weighed sum of the spectra of the individual constituents. The model on which the analytical formulation is based is described. The problem reduces to that of a multiple linear regression. A stepwise linear regression procedure was constructed. The efficiency of this method was then tested by transforming the procedure in a computer programme which was used to unfold test spectra obtained by mixing some spectra, from a library of arbitrary chosen spectra, and adding a noise component. (U.K.)
Quasi-least squares regression
Shults, Justine
2014-01-01
Drawing on the authors' substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regression-a computational approach for the estimation of correlation parameters within the framework of generalized estimating equations (GEEs). The authors present a detailed evaluation of QLS methodology, demonstrating the advantages of QLS in comparison with alternative methods. They describe how QLS can be used to extend the application of the traditional GEE approach to the analysis of unequally spaced longitu
Assessment of deforestation using regression
This work is devoted to the evaluation of deforestation using regression methods through software Idrisi Taiga. Deforestation is evaluated by the method of logistic regression. The dependent variable has discrete values '0' and '1', indicating that the deforestation occurred or not. Independent variables have continuous values, expressing the distance from the edge of the deforested areas of forests from urban areas, the river and the road network. The results were also used in predicting the probability of deforestation in subsequent periods. The result is a map showing the output probability of deforestation for the periods 1990/2000 and 200/2006 in accordance with predetermined coefficients (values of independent variables). (authors)
A flexible fuzzy regression algorithm for forecasting oil consumption estimation
Oil consumption plays a vital role in socio-economic development of most countries. This study presents a flexible fuzzy regression algorithm for forecasting oil consumption based on standard economic indicators. The standard indicators are annual population, cost of crude oil import, gross domestic production (GDP) and annual oil production in the last period. The proposed algorithm uses analysis of variance (ANOVA) to select either fuzzy regression or conventional regression for future demand estimation. The significance of the proposed algorithm is three fold. First, it is flexible and identifies the best model based on the results of ANOVA and minimum absolute percentage error (MAPE), whereas previous studies consider the best fitted fuzzy regression model based on MAPE or other relative error results. Second, the proposed model may identify conventional regression as the best model for future oil consumption forecasting because of its dynamic structure, whereas previous studies assume that fuzzy regression always provide the best solutions and estimation. Third, it utilizes the most standard independent variables for the regression models. To show the applicability and superiority of the proposed flexible fuzzy regression algorithm the data for oil consumption in Canada, United States, Japan and Australia from 1990 to 2005 are used. The results show that the flexible algorithm provides accurate solution for oil consumption estimation problem. The algorithm may be used by policy makers to accurately foresee the behavior of oil consumption in various regions.
A flexible fuzzy regression algorithm for forecasting oil consumption estimation
Oil consumption plays a vital role in socio-economic development of most countries. This study presents a flexible fuzzy regression algorithm for forecasting oil consumption based on standard economic indicators. The standard indicators are annual population, cost of crude oil import, gross domestic production (GDP) and annual oil production in the last period. The proposed algorithm uses analysis of variance (ANOVA) to select either fuzzy regression or conventional regression for future demand estimation. The significance of the proposed algorithm is three fold. First, it is flexible and identifies the best model based on the results of ANOVA and minimum absolute percentage error (MAPE), whereas previous studies consider the best fitted fuzzy regression model based on MAPE or other relative error results. Second, the proposed model may identify conventional regression as the best model for future oil consumption forecasting because of its dynamic structure, whereas previous studies assume that fuzzy regression always provide the best solutions and estimation. Third, it utilizes the most standard independent variables for the regression models. To show the applicability and superiority of the proposed flexible fuzzy regression algorithm the data for oil consumption in Canada, United States, Japan and Australia from 1990 to 2005 are used. The results show that the flexible algorithm provides accurate solution for oil consumption estimation problem. The algorithm may be used by policy makers to accurately foresee the behavior of oil consumption in various regions. (author)
Al-Ghraibah, Amani
Solar flares release stored magnetic energy in the form of radiation and can have significant detrimental effects on earth including damage to technological infrastructure. Recent work has considered methods to predict future flare activity on the basis of quantitative measures of the solar magnetic field. Accurate advanced warning of solar flare occurrence is an area of increasing concern and much research is ongoing in this area. Our previous work 111] utilized standard pattern recognition and classification techniques to determine (classify) whether a region is expected to flare within a predictive time window, using a Relevance Vector Machine (RVM) classification method. We extracted 38 features which describing the complexity of the photospheric magnetic field, the result classification metrics will provide the baseline against which we compare our new work. We find a true positive rate (TPR) of 0.8, true negative rate (TNR) of 0.7, and true skill score (TSS) of 0.49. This dissertation proposes three basic topics; the first topic is an extension to our previous work [111, where we consider a feature selection method to determine an appropriate feature subset with cross validation classification based on a histogram analysis of selected features. Classification using the top five features resulting from this analysis yield better classification accuracies across a large unbalanced dataset. In particular, the feature subsets provide better discrimination of the many regions that flare where we find a TPR of 0.85, a TNR of 0.65 sightly lower than our previous work, and a TSS of 0.5 which has an improvement comparing with our previous work. In the second topic, we study the prediction of solar flare size and time-to-flare using support vector regression (SVR). When we consider flaring regions only, we find an average error in estimating flare size of approximately half a GOES class. When we additionally consider non-flaring regions, we find an increased average
Growth Regression and Economic Theory
Elbers, Chris; Gunning, Jan Willem
2002-01-01
In this note we show that the standard, loglinear growth regression specificationis consistent with one and only one model in the class of stochastic Ramsey models. Thismodel is highly restrictive: it requires a Cobb-Douglas technology and a 100% depreciationrate and it implies that risk does not af
On Weighted Support Vector Regression
Han, Xixuan; Clemmensen, Line Katrine Harder
2014-01-01
We propose a new type of weighted support vector regression (SVR), motivated by modeling local dependencies in time and space in prediction of house prices. The classic weights of the weighted SVR are added to the slack variables in the objective function (OF‐weights). This procedure directly...
Regression of lumbar disk herniation
G. Yu Evzikov
2015-01-01
Full Text Available Compression of the spinal nerve root, giving rise to pain and sensory and motor disorders in the area of its innervation is the most vivid manifestation of herniated intervertebral disk. Different treatment modalities, including neurosurgery, for evolving these conditions are discussed. There has been recent evidence that spontaneous regression of disk herniation can regress. The paper describes a female patient with large lateralized disc extrusion that has caused compression of the nerve root S1, leading to obvious myotonic and radicular syndrome. Magnetic resonance imaging has shown that the clinical manifestations of discogenic radiculopathy, as well myotonic syndrome and morphological changes completely regressed 8 months later. The likely mechanism is inflammation-induced resorption of a large herniated disk fragment, which agrees with the data available in the literature. A decision to perform neurosurgery for which the patient had indications was made during her first consultation. After regression of discogenic radiculopathy, there was only moderate pain caused by musculoskeletal diseases (facet syndrome, piriformis syndrome that were successfully eliminated by minimally invasive techniques.
Cactus: An Introduction to Regression
Hyde, Hartley
2008-01-01
When the author first used "VisiCalc," the author thought it a very useful tool when he had the formulas. But how could he design a spreadsheet if there was no known formula for the quantities he was trying to predict? A few months later, the author relates he learned to use multiple linear regression software and suddenly it all clicked into…
Heteroscedasticity checks for regression models
无
2001-01-01
For checking on heteroscedasticity in regression models, a unified approach is proposed to constructing test statistics in parametric and nonparametric regression models. For nonparametric regression, the test is not affected sensitively by the choice of smoothing parameters which are involved in estimation of the nonparametric regression function. The limiting null distribution of the test statistic remains the same in a wide range of the smoothing parameters. When the covariate is one-dimensional, the tests are, under some conditions, asymptotically distribution-free. In the high-dimensional cases, the validity of bootstrap approximations is investigated. It is shown that a variant of the wild bootstrap is consistent while the classical bootstrap is not in the general case, but is applicable if some extra assumption on conditional variance of the squared error is imposed. A simulation study is performed to provide evidence of how the tests work and compare with tests that have appeared in the literature. The approach may readily be extended to handle partial linear, and linear autoregressive models.
Fuzzy linear regression forecasting models
吴冲; 惠晓峰; 朱洪文
2002-01-01
The fuzzy linear regression forecasting model is deduced from the symmetric triangular fuzzy number.With the help of the degree of fitting and the measure of fuzziness, the determination of symmetric triangularfuzzy numbers is changed into a problem of solving linear programming.
Correlation Weights in Multiple Regression
Waller, Niels G.; Jones, Jeff A.
2010-01-01
A general theory on the use of correlation weights in linear prediction has yet to be proposed. In this paper we take initial steps in developing such a theory by describing the conditions under which correlation weights perform well in population regression models. Using OLS weights as a comparison, we define cases in which the two weighting…
Fungible Weights in Multiple Regression
Waller, Niels G.
2008-01-01
Every set of alternate weights (i.e., nonleast squares weights) in a multiple regression analysis with three or more predictors is associated with an infinite class of weights. All members of a given class can be deemed "fungible" because they yield identical "SSE" (sum of squared errors) and R[superscript 2] values. Equations for generating…
Acute Pancreatitis and Pregnancy
... Acute Pancreatitis > Acute Pancreatitis and Pregnancy test Acute Pancreatitis and Pregnancy Timothy Gardner, MD Acute pancreatitis is ... of acute pancreatitis in pregnancy. Reasons for Acute Pancreatitis and Pregnancy While acute pancreatitis is responsible for ...
Varying-coefficient functional linear regression
Wu, Yichao; Fan, Jianqing; Müller, Hans-Georg
2010-01-01
Functional linear regression analysis aims to model regression relations which include a functional predictor. The analog of the regression parameter vector or matrix in conventional multivariate or multiple-response linear regression models is a regression parameter function in one or two arguments. If, in addition, one has scalar predictors, as is often the case in applications to longitudinal studies, the question arises how to incorporate these into a functional regression model. We study...
... sharing features on this page, please enable JavaScript. Acute bronchitis is swelling and inflammation in the main passages ... present only for a short time. Causes When acute bronchitis occurs, it almost always comes after having a ...
Acute bronchitis is swelling and inflammation in the main passages that carry air to the lungs. The swelling narrows ... makes it harder to breathe. Another symptom of bronchitis is a cough. Acute means the symptoms have ...
Bronchitis is an inflammation of the bronchial tubes, the airways that carry air to your lungs. It ... chest tightness. There are two main types of bronchitis: acute and chronic. Most cases of acute bronchitis ...
Bo-Guang Fan; Åke Andrén-Sandberg
2010-01-01
Background : Acute pancreatitis continues to be a serious illness, and the patients with acute pancreatitis are at risk to develop different complications from ongoing pancreatic inflammation. Aims : The present review is to highlight the classification, treatment and prognosis of acute pancreatitis. Material & Methods : We reviewed the English-language literature (Medline) addressing pancreatitis. Results : Acute pancreatitis is frequently caused by gallstone disease or excess alcohol ingest...
Bo-Guang Fan; Åke Andrén-Sandberg
2010-01-01
Background: Acute pancreatitis continues to be a serious illness, and the patients with acute pancreatitis are at risk to develop different complications from ongoing pancreatic inflammation. Aims: The present review is to highlight the classification, treatment and prognosis of acute pancreatitis. Material & Methods: We reviewed the English-language literature (Medline) addressing pancreatitis. Results: Acute pancreatitis is frequently caused by gallstone disease or excess alcohol ingestion....
Heteroscedasticity checks for regression models
ZHU; Lixing
2001-01-01
［1］Carroll, R. J., Ruppert, D., Transformation and Weighting in Regression, New York: Chapman and Hall, 1988.［2］Cook, R. D., Weisberg, S., Diagnostics for heteroscedasticity in regression, Biometrika, 1988, 70: 1—10.［3］Davidian, M., Carroll, R. J., Variance function estimation, J. Amer. Statist. Assoc., 1987, 82: 1079—1091.［4］Bickel, P., Using residuals robustly I: Tests for heteroscedasticity, Ann. Statist., 1978, 6: 266—291.［5］Carroll, R. J., Ruppert, D., On robust tests for heteroscedasticity, Ann. Statist., 1981, 9: 205—209.［6］Eubank, R. L., Thomas, W., Detecting heteroscedasticity in nonparametric regression, J. Roy. Statist. Soc., Ser. B, 1993, 55: 145—155.［7］Diblasi, A., Bowman, A., Testing for constant variance in a linear model, Statist. and Probab. Letters, 1997, 33: 95—103.［8］Dette, H., Munk, A., Testing heteoscedasticity in nonparametric regression, J. R. Statist. Soc. B, 1998, 60: 693—708.［9］Müller, H. G., Zhao, P. L., On a semi-parametric variance function model and a test for heteroscedasticity, Ann. Statist., 1995, 23: 946—967.［10］Stute, W., Manteiga, G., Quindimil, M. P., Bootstrap approximations in model checks for regression, J. Amer. Statist. Asso., 1998, 93: 141—149.［11］Stute, W., Thies, G., Zhu, L. X., Model checks for regression: An innovation approach, Ann. Statist., 1998, 26: 1916—1939.［12］Shorack, G. R., Wellner, J. A., Empirical Processes with Applications to Statistics, New York: Wiley, 1986.［13］Efron, B., Bootstrap methods: Another look at the jackknife, Ann. Statist., 1979, 7: 1—26.［14］Wu, C. F. J., Jackknife, bootstrap and other re-sampling methods in regression analysis, Ann. Statist., 1986, 14: 1261—1295.［15］H rdle, W., Mammen, E., Comparing non-parametric versus parametric regression fits, Ann. Statist., 1993, 21: 1926—1947.［16］Liu, R. Y., Bootstrap procedures under some non-i.i.d. models, Ann. Statist., 1988, 16: 1696—1708.［17
Observational Studies: Matching or Regression?
Brazauskas, Ruta; Logan, Brent R
2016-03-01
In observational studies with an aim of assessing treatment effect or comparing groups of patients, several approaches could be used. Often, baseline characteristics of patients may be imbalanced between groups, and adjustments are needed to account for this. It can be accomplished either via appropriate regression modeling or, alternatively, by conducting a matched pairs study. The latter is often chosen because it makes groups appear to be comparable. In this article we considered these 2 options in terms of their ability to detect a treatment effect in time-to-event studies. Our investigation shows that a Cox regression model applied to the entire cohort is often a more powerful tool in detecting treatment effect as compared with a matched study. Real data from a hematopoietic cell transplantation study is used as an example. PMID:26712591
Mission assurance increased with regression testing
Lang, R.; Spezio, M.
- C based and big endian. The presence of byte swap issues that might not have been identified in the required software changes was very real and can be difficult to find. The ability to have test designs that would exercise all major functions and operations was invaluable to assure that critical operations and tools would operate as they had since first operational use. With the longevity of the mission also came the realization that the original ISAT team would not be the people working on the ISAT regression testing. The ability to have access to all original test designs and test results identified in the regression test suite greatly improved the ability to identify not only the expected system behavior, but also the actual behavior with the old architecture. So in summary, this paper will discuss the importance, practicality, and results achieved by having a well-defined regression test available to assure the New Horizons Mission Operations Control system continues to meet its functional requirements to support the mission objectives.
Functional linear regression with derivatives
Mas, André; Pumo, Besnik
2006-01-01
International audience We introduce a new model of linear regression for random functional inputs taking into account the first order derivative of the data. We propose an estimation method which comes down to solving a special linear inverse problem. Our procedure tackles the problem through a double and synchronized penalization. An asymptotic expansion of the mean square prevision error is given. The model and the method are applied to a benchmark dataset of spectrometric curves and com...
The Standard Error of Regressions
Deirdre N. McCloskey; Stephen T. Ziliak
1996-01-01
Statistical significance as used in economics has weak theoretical justification. In particular it merges statistical and substantive significance. The 182 papers using regression analysis in the American Economic Review in the 1980s were tested against 19 criteria for the accepted use of statistical significance. Most, some three-quarters of the papers, did poorly. Likewise, textbooks in econometrics do not distinguish statistical and economic significance. Statistical significance should no...
On Heteroscedasticity in Robust Regression
Kalina, Jan
Slaný : Melandrium, 2011 - (Löster, T.), s. 228-237 ISBN 978-80-86175-77-5. [International Days of Statistics and Economics /5./. Prague (CZ), 22.09.2011-23.09.2011] Grant ostatní: GA ČR(CZ) GA402/09/0732 Institutional research plan: CEZ:AV0Z10300504 Keywords : robust statistics * linear regression * diagnostics Subject RIV: BB - Applied Statistics, Operational Research http://msed.vse.cz/files/2011/Kalina.pdf
An Application on Multinomial Logistic Regression Model
Abdalla M El-Habil
2012-03-01
Full Text Available Normal 0 false false false EN-US X-NONE X-NONE This study aims to identify an application of Multinomial Logistic Regression model which is one of the important methods for categorical data analysis. This model deals with one nominal/ordinal response variable that has more than two categories, whether nominal or ordinal variable. This model has been applied in data analysis in many areas, for example health, social, behavioral, and educational.To identify the model by practical way, we used real data on physical violence against children, from a survey of Youth 2003 which was conducted by Palestinian Central Bureau of Statistics (PCBS. Segment of the population of children in the age group (10-14 years for residents in Gaza governorate, size of 66,935 had been selected, and the response variable consisted of four categories. Eighteen of explanatory variables were used for building the primary multinomial logistic regression model. Model had been tested through a set of statistical tests to ensure its appropriateness for the data. Also the model had been tested by selecting randomly of two observations of the data used to predict the position of each observation in any classified group it can be, by knowing the values of the explanatory variables used. We concluded by using the multinomial logistic regression model that we can able to define accurately the relationship between the group of explanatory variables and the response variable, identify the effect of each of the variables, and we can predict the classification of any individual case.
Bo-Guang Fan
2010-05-01
Full Text Available Background: Acute pancreatitis continues to be a serious illness, and the patients with acute pancreatitis are at risk to develop different complications from ongoing pancreatic inflammation. Aims: The present review is to highlight the classification, treatment and prognosis of acute pancreatitis. Material & Methods: We reviewed the English-language literature (Medline addressing pancreatitis. Results: Acute pancreatitis is frequently caused by gallstone disease or excess alcohol ingestion. There are a number of important issues regarding clinical highlights in the classification, treatment and prognosis of acute pancreatitis, and treatment options for complications of acute pancreatitis including pancreatic pseudocysts. Conclusions: Multidisciplinary approach should be used for the management of the patient with acute pancreatitis.
Bo-Guang Fan
2010-01-01
Full Text Available Background : Acute pancreatitis continues to be a serious illness, and the patients with acute pancreatitis are at risk to develop different complications from ongoing pancreatic inflammation. Aims : The present review is to highlight the classification, treatment and prognosis of acute pancreatitis. Material & Methods : We reviewed the English-language literature (Medline addressing pancreatitis. Results : Acute pancreatitis is frequently caused by gallstone disease or excess alcohol ingestion. There are a number of important issues regarding clinical highlights in the classification, treatment and prognosis of acute pancreatitis, and treatment options for complications of acute pancreatitis including pancreatic pseudocysts. Conclusions : Multidisciplinary approach should be used for the management of the patient with acute pancreatitis.
Gurkoff, Gene G; Giza, Christopher C; Hovda, David A
2006-03-10
Lateral fluid percussion injury (LFP), a model of mild-moderate concussion, leads to the temporary loss of the capacity for experience-dependent plasticity in developing rats. To determine if this injury-induced loss in capacity for plasticity is due to cell death, we conducted stereological measurements within the cerebral cortex and CA3 of the hippocampus 2 weeks following mild, moderate or severe LFP in the post-natal day 19 (P19) rat. Results indicated that there was no significant change in the absolute number of neurons, regardless of injury severity, in either the ipsilateral cortex (sham = 10.6 +/- 1.7, mild = 11.5 +/- 2.1, moderate = 10.0 +/- 1.0, severe = 10.9 +/- 1.3 million neurons) or CA3 region of the hippocampus (sham = 251 +/- 38, mild = 289 +/- 2, moderate = 245 +/- 48, severe = 255 +/- 62 thousand neurons). Even though there was no evidence of a significant degree of injury-induced cell death, animals exhibited cognitive deficits as revealed in a Morris water maze task (MWM). The MWM results indicated that regardless of injury severity, P19-injured rats exhibited a significant increase in escape latency compared to age-matched shams (injury by day; P < 0.001) and a significant increase in the number of trials needed to reach criterion (P < 0.05). Analysis of a probe trial one week post-MWM training, however, indicated that there was no deficit in storage or recall of the learned behavior as analyzed by platform hits (sham = 2.9 +/- 0.37, mild = 2.0 +/- 0.40, moderate = 1 +/- 0, severe = 2.8 +/- 0.62) or percent time spent in, or immediately surrounding, the platform area (sham = 13.5 +/- 1.71, mild = 10.8 +/- 2.32, moderate = 12.7 +/- 0, severe = 13.5 +/- 1.69). Taken together, these results indicate that while LFP in P19-injured animals does not lead to significant cell death, it does generate acute, mild deficits in MWM performance. PMID:16490184
Credit Scoring Problem Based on Regression Analysis
Khassawneh, Bashar Suhil Jad Allah
2014-01-01
ABSTRACT: This thesis provides an explanatory introduction to the regression models of data mining and contains basic definitions of key terms in the linear, multiple and logistic regression models. Meanwhile, the aim of this study is to illustrate fitting models for the credit scoring problem using simple linear, multiple linear and logistic regression models and also to analyze the found model functions by statistical tools. Keywords: Data mining, linear regression, logistic regression....
Heteroscedastic transformation cure regression models.
Chen, Chyong-Mei; Chen, Chen-Hsin
2016-06-30
Cure models have been applied to analyze clinical trials with cures and age-at-onset studies with nonsusceptibility. Lu and Ying (On semiparametric transformation cure model. Biometrika 2004; 91:331?-343. DOI: 10.1093/biomet/91.2.331) developed a general class of semiparametric transformation cure models, which assumes that the failure times of uncured subjects, after an unknown monotone transformation, follow a regression model with homoscedastic residuals. However, it cannot deal with frequently encountered heteroscedasticity, which may result from dispersed ranges of failure time span among uncured subjects' strata. To tackle the phenomenon, this article presents semiparametric heteroscedastic transformation cure models. The cure status and the failure time of an uncured subject are fitted by a logistic regression model and a heteroscedastic transformation model, respectively. Unlike the approach of Lu and Ying, we derive score equations from the full likelihood for estimating the regression parameters in the proposed model. The similar martingale difference function to their proposal is used to estimate the infinite-dimensional transformation function. Our proposed estimating approach is intuitively applicable and can be conveniently extended to other complicated models when the maximization of the likelihood may be too tedious to be implemented. We conduct simulation studies to validate large-sample properties of the proposed estimators and to compare with the approach of Lu and Ying via the relative efficiency. The estimating method and the two relevant goodness-of-fit graphical procedures are illustrated by using breast cancer data and melanoma data. Copyright © 2016 John Wiley & Sons, Ltd. PMID:26887342
Autocorrelated residuals of robust regression
Kalina, Jan
Slaný : Melandrium, 2013 - (Löster, T.; Pavelka, T.), s. 551-560 ISBN 978-80-86175-87-4. [International Days of Statistics and Economics /7./. Prague (CZ), 19.09.2013-21.09.2013] Grant ostatní: GA ČR(CZ) GA13-01930S Institutional support: RVO:67985807 Keywords : linear regression * robust statistics * diagnostics * autocorrelation Subject RIV: BB - Applied Statistics, Operational Research http://msed.vse.cz/files/2013/1-Kalina-Jan-paper.pdf
Acute parotitis during induction therapy including L-asparaginase in acute lymphoblastic leukemia.
Sica, S; Pagano, L; Salutari, P; Di Mario, A; Rutella, S; Leone, G
1994-02-01
In a patient affected by acute lymphoblastic leukemia (ALL) and subjected to therapy with Erwinia L-asparaginase, acute parotitis was observed. Microbiological studies excluded any infectious etiology. Regression of parotitis was spontaneous. This complication has not been previously reported and could be due to the same mechanism of pancreatic injury. The occurrence of acute parotitis needs to be promptly recognized in order to avoid the continuation of L-asparaginase. PMID:8148421
General bound of overfitting for MLP regression models
Rynkiewicz, Joseph
2012-01-01
Multilayer perceptrons (MLP) with one hidden layer have been used for a long time to deal with non-linear regression. However, in some task, MLP's are too powerful models and a small mean square error (MSE) may be more due to overfitting than to actual modelling. If the noise of the regression model is Gaussian, the overfitting of the model is totally determined by the behavior of the likelihood ratio test statistic (LRTS), however in numerous cases the assumption of normality of the noise is...
Adaptive Local Linear Quantile Regression
Yu-nan Su; Mao-zai Tian
2011-01-01
In this paper we propose a new method of local linear adaptive smoothing for nonparametric conditional quantile regression. Some theoretical properties of the procedure are investigated. Then we demonstrate the performance of the method on a simulated example and compare it with other methods. The simulation results demonstrate a reasonable performance of our method proposed especially in situations when the underlying image is piecewise linear or can be approximated by such images. Generally speaking, our method outperforms most other existing methods in the sense of the mean square estimation (MSE) and mean absolute estimation (MAE) criteria. The procedure is very stable with respect to increasing noise level and the algorithm can be easily applied to higher dimensional situations.
Prediction, Regression and Critical Realism
Næss, Petter
2004-01-01
This paper considers the possibility of prediction in land use planning, and the use of statistical research methods in analyses of relationships between urban form and travel behaviour. Influential writers within the tradition of critical realism reject the possibility of predicting social...... of prediction necessary and possible in spatial planning of urban development. Finally, the political implications of positions within theory of science rejecting the possibility of predictions about social phenomena are addressed....... phenomena. This position is fundamentally problematic to public planning. Without at least some ability to predict the likely consequences of different proposals, the justification for public sector intervention into market mechanisms will be frail. Statistical methods like regression analyses are commonly...
RANDOM WEIGHTING METHOD FOR CENSORED REGRESSION MODEL
ZHAO Lincheng; FANG Yixin
2004-01-01
Rao and Zhao (1992) used random weighting method to derive the approximate distribution of the M-estimator in linear regression model. In this paper we extend the result to the censored regression model (or censored "Tobit" model).
Bigger, J. T. Jr; Steinman, R. C.; Rolnitzky, L. M.; Fleiss, J. L.; Albrecht, P.; Cohen, R. J.
1996-01-01
BACKGROUND. The purposes of the present study were (1) to establish normal values for the regression of log(power) on log(frequency) for, RR-interval fluctuations in healthy middle-aged persons, (2) to determine the effects of myocardial infarction on the regression of log(power) on log(frequency), (3) to determine the effect of cardiac denervation on the regression of log(power) on log(frequency), and (4) to assess the ability of power law regression parameters to predict death after myocardial infarction. METHODS AND RESULTS. We studied three groups: (1) 715 patients with recent myocardial infarction; (2) 274 healthy persons age and sex matched to the infarct sample; and (3) 19 patients with heart transplants. Twenty-four-hour RR-interval power spectra were computed using fast Fourier transforms and log(power) was regressed on log(frequency) between 10(-4) and 10(-2) Hz. There was a power law relation between log(power) and log(frequency). That is, the function described a descending straight line that had a slope of approximately -1 in healthy subjects. For the myocardial infarction group, the regression line for log(power) on log(frequency) was shifted downward and had a steeper negative slope (-1.15). The transplant (denervated) group showed a larger downward shift in the regression line and a much steeper negative slope (-2.08). The correlation between traditional power spectral bands and slope was weak, and that with log(power) at 10(-4) Hz was only moderate. Slope and log(power) at 10(-4) Hz were used to predict mortality and were compared with the predictive value of traditional power spectral bands. Slope and log(power) at 10(-4) Hz were excellent predictors of all-cause mortality or arrhythmic death. To optimize the prediction of death, we calculated a log(power) intercept that was uncorrelated with the slope of the power law regression line. We found that the combination of slope and zero-correlation log(power) was an outstanding predictor, with a
Gaussian process regression analysis for functional data
Shi, Jian Qing
2011-01-01
Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dime
Semiparametric regression during 2003–2007
Ruppert, David
2009-01-01
Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.
Standards for Standardized Logistic Regression Coefficients
Menard, Scott
2011-01-01
Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…
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,
Functional Regression for Quasar Spectra
Ciollaro, Mattia; Freeman, Peter; Genovese, Christopher; Lei, Jing; O'Connell, Ross; Wasserman, Larry
2014-01-01
The Lyman-alpha forest is a portion of the observed light spectrum of distant galactic nuclei which allows us to probe remote regions of the Universe that are otherwise inaccessible. The observed Lyman-alpha forest of a quasar light spectrum can be modeled as a noisy realization of a smooth curve that is affected by a `damping effect' which occurs whenever the light emitted by the quasar travels through regions of the Universe with higher matter concentration. To decode the information conveyed by the Lyman-alpha forest about the matter distribution, we must be able to separate the smooth `continuum' from the noise and the contribution of the damping effect in the quasar light spectra. To predict the continuum in the Lyman-alpha forest, we use a nonparametric functional regression model in which both the response and the predictor variable (the smooth part of the damping-free portion of the spectrum) are function-valued random variables. We demonstrate that the proposed method accurately predicts the unobserv...
Harmonic regression and scale stability.
Lee, Yi-Hsuan; Haberman, Shelby J
2013-10-01
Monitoring a very frequently administered educational test with a relatively short history of stable operation imposes a number of challenges. Test scores usually vary by season, and the frequency of administration of such educational tests is also seasonal. Although it is important to react to unreasonable changes in the distributions of test scores in a timely fashion, it is not a simple matter to ascertain what sort of distribution is really unusual. Many commonly used approaches for seasonal adjustment are designed for time series with evenly spaced observations that span many years and, therefore, are inappropriate for data from such educational tests. Harmonic regression, a seasonal-adjustment method, can be useful in monitoring scale stability when the number of years available is limited and when the observations are unevenly spaced. Additional forms of adjustments can be included to account for variability in test scores due to different sources of population variations. To illustrate, real data are considered from an international language assessment. PMID:24092490
Spontaneous regression of a congenital melanocytic nevus
Amiya Kumar Nath
2011-01-01
Full Text Available Congenital melanocytic nevus (CMN may rarely regress which may also be associated with a halo or vitiligo. We describe a 10-year-old girl who presented with CMN on the left leg since birth, which recently started to regress spontaneously with associated depigmentation in the lesion and at a distant site. Dermoscopy performed at different sites of the regressing lesion demonstrated loss of epidermal pigments first followed by loss of dermal pigments. Histopathology and Masson-Fontana stain demonstrated lymphocytic infiltration and loss of pigment production in the regressing area. Immunohistochemistry staining (S100 and HMB-45, however, showed that nevus cells were present in the regressing areas.
Regression with Sparse Approximations of Data
Noorzad, Pardis; Sturm, Bob L.
2012-01-01
We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected by a...... sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based on the...
Thomas, Michael S. C.; Knowland, Victoria C. P.; Karmiloff-Smith, Annette
2011-01-01
Loss of previously established behaviors in early childhood constitutes a markedly atypical developmental trajectory. It is found almost uniquely in autism and its cause is currently unknown (Baird et al., 2008). We present an artificial neural network model of developmental regression, exploring the hypothesis that regression is caused by…
Use of Home Videotapes to Confirm Parental Reports of Regression in Autism
Goldberg, Wendy A.; Thorsen, Kara L.; Osann, Kathryn; Spence, M. Anne
2008-01-01
The current study examined consistency between parental reports on early language development and behaviors in non-language domains and observer-coded videotapes of young children with and without autism spectrum disorder (ASD) and autistic regression. Data are reported on 56 children (84% male) with ASD (early onset or autistic regression) and 14…
Functional linear regression via canonical analysis
He, Guozhong; Wang, Jane-Ling; Yang, Wenjing; 10.3150/09-BEJ228
2011-01-01
We study regression models for the situation where both dependent and independent variables are square-integrable stochastic processes. Questions concerning the definition and existence of the corresponding functional linear regression models and some basic properties are explored for this situation. We derive a representation of the regression parameter function in terms of the canonical components of the processes involved. This representation establishes a connection between functional regression and functional canonical analysis and suggests alternative approaches for the implementation of functional linear regression analysis. A specific procedure for the estimation of the regression parameter function using canonical expansions is proposed and compared with an established functional principal component regression approach. As an example of an application, we present an analysis of mortality data for cohorts of medflies, obtained in experimental studies of aging and longevity.
Analysis of Sting Balance Calibration Data Using Optimized Regression Models
Ulbrich, N.; Bader, Jon B.
2010-01-01
Calibration data of a wind tunnel sting balance was processed using a candidate math model search algorithm that recommends an optimized regression model for the data analysis. During the calibration the normal force and the moment at the balance moment center were selected as independent calibration variables. The sting balance itself had two moment gages. Therefore, after analyzing the connection between calibration loads and gage outputs, it was decided to choose the difference and the sum of the gage outputs as the two responses that best describe the behavior of the balance. The math model search algorithm was applied to these two responses. An optimized regression model was obtained for each response. Classical strain gage balance load transformations and the equations of the deflection of a cantilever beam under load are used to show that the search algorithm s two optimized regression models are supported by a theoretical analysis of the relationship between the applied calibration loads and the measured gage outputs. The analysis of the sting balance calibration data set is a rare example of a situation when terms of a regression model of a balance can directly be derived from first principles of physics. In addition, it is interesting to note that the search algorithm recommended the correct regression model term combinations using only a set of statistical quality metrics that were applied to the experimental data during the algorithm s term selection process.
Modeling Lateral and Longitudinal Control of Human Drivers with Multiple Linear Regression Models
Lenk, Jan; M, Claus
2011-01-01
In this paper, we describe results to model lateral and longitudinal control behavior of drivers with simple linear multiple regression models. This approach fits into the Bayesian Programming (BP) approach (Bessi
PM10 forecasting using clusterwise regression
Poggi, Jean-Michel; Portier, Bruno
2011-12-01
In this paper, we are interested in the statistical forecasting of the daily mean PM10 concentration. Hourly concentrations of PM10 have been measured in the city of Rouen, in Haute-Normandie, France. Located at northwest of Paris, near the south side of Manche sea and heavily industrialised. We consider three monitoring stations reflecting the diversity of situations: an urban background station, a traffic station and an industrial station near the cereal harbour of Rouen. We have focused our attention on data for the months that register higher values, from December to March, on years 2004-2009. The models are obtained from the winter days of the four seasons 2004/2005 to 2007/2008 (training data) and then the forecasting performance is evaluated on the winter days of the season 2008/2009 (test data). We show that it is possible to accurately forecast the daily mean concentration by fitting a function of meteorological predictors and the average concentration measured on the previous day. The values of observed meteorological variables are used for fitting the models and are also considered for the test data. We have compared the forecasts produced by three different methods: persistence, generalized additive nonlinear models and clusterwise linear regression models. This last method gives very impressive results and the end of the paper tries to analyze the reasons of such a good behavior.
... page: //medlineplus.gov/ency/article/000287.htm Acute pancreatitis To use the sharing features on this page, ... fatty foods after the attack has improved. Outlook (Prognosis) Most cases go away in a week. However, ...
... Sugar Control Helps Fight Diabetic Eye Disease Are 'Workaholics' Prone to OCD, Anxiety? ALL NEWS > Resources First ... cancer, or heart surgery, the fluid is blood. Causes Acute pericarditis usually results from infection or other ...
Radiodiagnosis is applied to determine the causes of acute dyspnea. Acute dyspnea is shown to aggravate the course of pulmonary diseases (bronchial asthma, obstructive bronchitis, pulmonary edema, throboembolism of pulmonary arteries etc) and cardiovascular diseases (desiseas of myocardium). The main tasks of radiodiagnosis are to determine volume and state of the lungs, localization and type of pulmonary injuries, to verify heart disease and to reveal concomitant complications
Wark, Peter
2008-01-01
Acute bronchitis, with transient inflammation of the trachea and major bronchi, affects over 40/1000 adults a year in the UK. The causes are usually considered to be infective, but only around half of people have identifiable pathogens.The role of smoking or environmental tobacco smoke inhalation in predisposing to acute bronchitis is unclear.A third of people may have longer-term symptoms or recurrence.
Applied regression analysis a research tool
Pantula, Sastry; Dickey, David
1998-01-01
Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to...
Tydén Patrik; Engström Gunnar; Schlyter Mona; André-Petersson Lena; Hedblad Bo
2011-01-01
Abstract Background Psychosocial stress has been identified as a risk factor in association with cardiovascular disease but less attention has been paid to heterogeneity in vulnerability to stress. The serial Color Word Test (CWT) measures adaptation to a stressful situation and it can be used to identify individuals that are vulnerable to stress. Prospective studies have shown that individuals with a maladaptive behavior in this test are exposed to an increased risk of future cardiovascular ...
Green, Nella; Hoenigl, Martin; Morris, Sheldon; Little, Susan J.
2015-01-01
Abstract The transgender community represents an understudied population in the literature. The objective of this study was to compare risk behavior, and HIV and sexually transmitted infection (STI) rates between transgender women and transgender men undergoing community-based HIV testing. With this retrospective analysis of a cohort study, we characterize HIV infection rates as well as reported risk behaviors and reported STI in 151 individual transgender women and 30 individual transgender ...
PLUTO: Penalized Unbiased Logistic Regression Trees
Zhang, Wenwen; Loh, Wei-Yin
2014-01-01
We propose a new algorithm called PLUTO for building logistic regression trees to binary response data. PLUTO can capture the nonlinear and interaction patterns in messy data by recursively partitioning the sample space. It fits a simple or a multiple linear logistic regression model in each partition. PLUTO employs the cyclical coordinate descent method for estimation of multiple linear logistic regression models with elastic net penalties, which allows it to deal with high-dimensional data ...
Investigating bias in squared regression structure coefficients
Nimon, Kim F.; Zientek, Linda R.; Thompson, Bruce
2015-01-01
The importance of structure coefficients and analogs of regression weights for analysis within the general linear model (GLM) has been well-documented. The purpose of this study was to investigate bias in squared structure coefficients in the context of multiple regression and to determine if a formula that had been shown to correct for bias in squared Pearson correlation coefficients and coefficients of determination could be used to correct for bias in squared regression structure coefficie...
Simultaneous confidence bands in linear regression analysis
Ah-Kine, Pascal Soon Shien
2010-01-01
A simultaneous confidence band provides useful information on the plausible range of an unknown regression model. For a simple linear regression model, the most frequently quoted bands in the statistical literature include the two-segment band, the three-segment band and the hyperbolic band, and for a multiple linear regression model, the most com- mon bands in the statistical literature include the hyperbolic band and the constant width band. The optimality criteria for confid...
Data Mining within a Regression Framework
Berk, Richard A.
Regression analysis can imply a far wider range of statistical procedures than often appreciated. In this chapter, a number of common Data Mining procedures are discussed within a regression framework. These include non-parametric smoothers, classification and regression trees, bagging, and random forests. In each case, the goal is to characterize one or more of the distributional features of a response conditional on a set of predictors.
LRGS: Linear Regression by Gibbs Sampling
Mantz, Adam B.
2016-02-01
LRGS (Linear Regression by Gibbs Sampling) implements a Gibbs sampler to solve the problem of multivariate linear regression with uncertainties in all measured quantities and intrinsic scatter. LRGS extends an algorithm by Kelly (2007) that used Gibbs sampling for performing linear regression in fairly general cases in two ways: generalizing the procedure for multiple response variables, and modeling the prior distribution of covariates using a Dirichlet process.
Gaussian Process Quantile Regression using Expectation Propagation
Boukouvalas, Alexis; Barillec, Remi; Cornford, Dan
2012-01-01
Direct quantile regression involves estimating a given quantile of a response variable as a function of input variables. We present a new framework for direct quantile regression where a Gaussian process model is learned, minimising the expected tilted loss function. The integration required in learning is not analytically tractable so to speed up the learning we employ the Expectation Propagation algorithm. We describe how this work relates to other quantile regression methods and apply the ...
Interpretation of Regressions with Multiple Proxies
Darren Lubotsky; Martin Wittenberg
2001-01-01
We consider the situation in which there are multiple proxies for one unobserved explanatory variable in a linear regression and provide a procedure by which the coefficient of interest can be extracted "post hoc" from a multiple regression in which all the proxies are used simultaneously. This post hoc estimator is strictly superior in large samples to coefficients derived using any index or linear combination of the proxies that is created prior to the regression. To use an index created fr...
High-dimensional regression with unknown variance
Giraud, Christophe; Verzelen, Nicolas
2011-01-01
We review recent results for high-dimensional sparse linear regression in the practical case of unknown variance. Different sparsity settings are covered, including coordinate-sparsity, group-sparsity and variation-sparsity. The emphasize is put on non-asymptotic analyses and feasible procedures. In addition, a small numerical study compares the practical performance of three schemes for tuning the Lasso esti- mator and some references are collected for some more general models, including multivariate regression and nonparametric regression.
Hierarchical sparsity priors for regression models
Griffin, Jim E.; Brown, Philip J
2013-01-01
We focus on the increasingly important area of sparse regression problems where there are many variables and the effects of a large subset of these are negligible. This paper describes the construction of hierarchical prior distributions when the effects are considered related. These priors allow dependence between the regression coefficients and encourage related shrinkage towards zero of different regression coefficients. The properties of these priors are discussed and applications to line...
Some Priors for Sparse Regression Modelling
Griffin, Jim E.; Brown, Philip J
2013-01-01
A wide range of methods, Bayesian and others, tackle regression when there are many variables. In the Bayesian context, the prior is constructed to reflect ideas of variable selection and to encourage appropriate shrinkage. The prior needs to be reasonably robust to different signal to noise structures. Two simple evergreen prior constructions stem from ridge regression on the one hand and g-priors on the other. We seek to embed recent ideas about sparsity of the regression coefficients and r...
Deletion Diagnostics for Alternating Logistic Regressions
Preisser, John S; By, Kunthel; Perin, Jamie; Qaqish, Bahjat F.
2012-01-01
Deletion diagnostics are introduced for the regression analysis of clustered binary outcomes estimated with alternating logistic regressions, an implementation of generalized estimating equations (GEE) that estimates regression coefficients in a marginal mean model and in a model for the intracluster association given by the log odds ratio. The diagnostics are developed within an estimating equations framework that recasts the estimating functions for association parameters based upon conditi...
Logistic Regression for Evolving Data Streams Classification
YIN Zhi-wu; HUANG Shang-teng; XUE Gui-rong
2007-01-01
Logistic regression is a fast classifier and can achieve higher accuracy on small training data. Moreover,it can work on both discrete and continuous attributes with nonlinear patterns. Based on these properties of logistic regression, this paper proposed an algorithm, called evolutionary logistical regression classifier (ELRClass), to solve the classification of evolving data streams. This algorithm applies logistic regression repeatedly to a sliding window of samples in order to update the existing classifier, to keep this classifier if its performance is deteriorated by the reason of bursting noise, or to construct a new classifier if a major concept drift is detected. The intensive experimental results demonstrate the effectiveness of this algorithm.
Linear regression and sensitivity analysis in nuclear reactor design
Highlights: • Presented a benchmark for the applicability of linear regression to complex systems. • Applied linear regression to a nuclear reactor power system. • Performed neutronics, thermal–hydraulics, and energy conversion using Brayton’s cycle for the design of a GCFBR. • Performed detailed sensitivity analysis to a set of parameters in a nuclear reactor power system. • Modeled and developed reactor design using MCNP, regression using R, and thermal–hydraulics in Java. - Abstract: The paper presents a general strategy applicable for sensitivity analysis (SA), and uncertainity quantification analysis (UA) of parameters related to a nuclear reactor design. This work also validates the use of linear regression (LR) for predictive analysis in a nuclear reactor design. The analysis helps to determine the parameters on which a LR model can be fit for predictive analysis. For those parameters, a regression surface is created based on trial data and predictions are made using this surface. A general strategy of SA to determine and identify the influential parameters those affect the operation of the reactor is mentioned. Identification of design parameters and validation of linearity assumption for the application of LR of reactor design based on a set of tests is performed. The testing methods used to determine the behavior of the parameters can be used as a general strategy for UA, and SA of nuclear reactor models, and thermal hydraulics calculations. A design of a gas cooled fast breeder reactor (GCFBR), with thermal–hydraulics, and energy transfer has been used for the demonstration of this method. MCNP6 is used to simulate the GCFBR design, and perform the necessary criticality calculations. Java is used to build and run input samples, and to extract data from the output files of MCNP6, and R is used to perform regression analysis and other multivariate variance, and analysis of the collinearity of data
Acute myelogenous leukemia (AML) - children
Acute myelogenous leukemia - children; AML; Acute myeloid leukemia - children; Acute granulocytic leukemia - children; Acute myeloblastic leukemia - children; Acute non-lymphocytic leukemia (ANLL) - children
2013-01-01
Background Inappropriate antibiotic prescribing for nonbacterial infections leads to increases in the costs of care, antibiotic resistance among bacteria, and adverse drug events. Acute respiratory infections (ARIs) are the most common reason for inappropriate antibiotic use. Most prior efforts to decrease inappropriate antibiotic prescribing for ARIs (e.g., educational or informational interventions) have relied on the implicit assumption that clinicians inappropriately prescribe antibiotics because they are unaware of guideline recommendations for ARIs. If lack of guideline awareness is not the reason for inappropriate prescribing, educational interventions may have limited impact on prescribing rates. Instead, interventions that apply social psychological and behavioral economic principles may be more effective in deterring inappropriate antibiotic prescribing for ARIs by well-informed clinicians. Methods/design The Application of Behavioral Economics to Improve the Treatment of Acute Respiratory Infections (BEARI) Trial is a multisite, cluster-randomized controlled trial with practice as the unit of randomization. The primary aim is to test the ability of three interventions based on behavioral economic principles to reduce the rate of inappropriate antibiotic prescribing for ARIs. We randomized practices in a 2 × 2 × 2 factorial design to receive up to three interventions for non-antibiotic-appropriate diagnoses: 1) Accountable Justifications: When prescribing an antibiotic for an ARI, clinicians are prompted to record an explicit justification that appears in the patient electronic health record; 2) Suggested Alternatives: Through computerized clinical decision support, clinicians prescribing an antibiotic for an ARI receive a list of non-antibiotic treatment choices (including prescription options) prior to completing the antibiotic prescription; and 3) Peer Comparison: Each provider’s rate of inappropriate antibiotic prescribing relative to top
Power Prediction in Smart Grids with Evolutionary Local Kernel Regression
Kramer, Oliver; Satzger, Benjamin; Lässig, Jörg
Electric grids are moving from a centralized single supply chain towards a decentralized bidirectional grid of suppliers and consumers in an uncertain and dynamic scenario. Soon, the growing smart meter infrastructure will allow the collection of terabytes of detailed data about the grid condition, e.g., the state of renewable electric energy producers or the power consumption of millions of private customers, in very short time steps. For reliable prediction strong and fast regression methods are necessary that are able to cope with these challenges. In this paper we introduce a novel regression technique, i.e., evolutionary local kernel regression, a kernel regression variant based on local Nadaraya-Watson estimators with independent bandwidths distributed in data space. The model is regularized with the CMA-ES, a stochastic non-convex optimization method. We experimentally analyze the load forecast behavior on real power consumption data. The proposed method is easily parallelizable, and therefore well appropriate for large-scale scenarios in smart grids.
Competing Risks Quantile Regression at Work
Dlugosz, Stephan; Lo, Simon M. S.; Wilke, Ralf
2016-01-01
Despite its emergence as a frequently used method for the empirical analysis of multivariate data, quantile regression is yet to become a mainstream tool for the analysis of duration data. We present a pioneering empirical study on the grounds of a competing risks quantile regression model. We use...
Regression Analysis and the Sociological Imagination
De Maio, Fernando
2014-01-01
Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.
Multispectral colormapping using penalized least square regression
Dissing, Bjørn Skovlund; Carstensen, Jens Michael; Larsen, Rasmus
2010-01-01
-XYZ color matching functions. The target of the regression is a well known color chart, and the models are validated using leave one out cross validation in order to maintain best possible generalization ability. The authors compare the method with a direct linear regression and see that the...
Repeated Results Analysis for Middleware Regression Benchmarking
Bulej, Lubomír; Kalibera, T.; Tůma, P.
2005-01-01
Roč. 60, - (2005), s. 345-358. ISSN 0166-5316 R&D Projects: GA ČR GA102/03/0672 Institutional research plan: CEZ:AV0Z10300504 Keywords : middleware benchmarking * regression benchmarking * regression testing Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.756, year: 2005
Regression of Environmental Noise in LIGO Data
Tiwari, Vaibhav; Frolov, Valery; Klimenko, Sergey; Mitselmakher, Guenakh; Necula, Valentin; Prodi, Giovanni; Re, Virginia; Salemi, Francesco; Vedovato, Gabriele; Yakushin, Igor
2015-01-01
We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the gravitational-wave channel from the PEM measurements. One of the most promising regression method is based on the construction of Wiener-Kolmogorov filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the Wiener-Kolmogorov method has been extended, incorporating banks of Wiener filters in the time-frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we presents the first results on regression of the bi-coherent noise in the LIGO data.
Takagi, Daisuke; Ikeda, Ken'ichi; Kawachi, Ichiro
2012-11-01
Crime is an important determinant of public health outcomes, including quality of life, mental well-being, and health behavior. A body of research has documented the association between community social capital and crime victimization. The association between social capital and crime victimization has been examined at multiple levels of spatial aggregation, ranging from entire countries, to states, metropolitan areas, counties, and neighborhoods. In multilevel analysis, the spatial boundaries at level 2 are most often drawn from administrative boundaries (e.g., Census tracts in the U.S.). One problem with adopting administrative definitions of neighborhoods is that it ignores spatial spillover. We conducted a study of social capital and crime victimization in one ward of Tokyo city, using a spatial Durbin model with an inverse-distance weighting matrix that assigned each respondent a unique level of "exposure" to social capital based on all other residents' perceptions. The study is based on a postal questionnaire sent to 20-69 years old residents of Arakawa Ward, Tokyo. The response rate was 43.7%. We examined the contextual influence of generalized trust, perceptions of reciprocity, two types of social network variables, as well as two principal components of social capital (constructed from the above four variables). Our outcome measure was self-reported crime victimization in the last five years. In the spatial Durbin model, we found that neighborhood generalized trust, reciprocity, supportive networks and two principal components of social capital were each inversely associated with crime victimization. By contrast, a multilevel regression performed with the same data (using administrative neighborhood boundaries) found generally null associations between neighborhood social capital and crime. Spatial regression methods may be more appropriate for investigating the contextual influence of social capital in homogeneous cultural settings such as Japan. PMID
In precision agriculture regression has been used widely to quality the relationship between soil attributes and other environmental variables. However, spatial correlation existing in soil samples usually makes the regression model suboptimal. In this study, a regression-kriging method was attemp...
Darnah
2016-04-01
Poisson regression has been used if the response variable is count data that based on the Poisson distribution. The Poisson distribution assumed equal dispersion. In fact, a situation where count data are over dispersion or under dispersion so that Poisson regression inappropriate because it may underestimate the standard errors and overstate the significance of the regression parameters, and consequently, giving misleading inference about the regression parameters. This paper suggests the generalized Poisson regression model to handling over dispersion and under dispersion on the Poisson regression model. The Poisson regression model and generalized Poisson regression model will be applied the number of filariasis cases in East Java. Based regression Poisson model the factors influence of filariasis are the percentage of families who don't behave clean and healthy living and the percentage of families who don't have a healthy house. The Poisson regression model occurs over dispersion so that we using generalized Poisson regression. The best generalized Poisson regression model showing the factor influence of filariasis is percentage of families who don't have healthy house. Interpretation of result the model is each additional 1 percentage of families who don't have healthy house will add 1 people filariasis patient.
A Regression Analysis Model Based on Wavelet Networks
XIONG Zheng-feng
2002-01-01
In this paper, an approach is proposed to combine wavelet networks and techniques of regression analysis. The resulting wavelet regression estimator is well suited for regression estimation of moderately large dimension, in particular for regressions with localized irregularities.
Pomerantz, Ori; Paukner, Annika; Terkel, Joseph
2012-01-01
The most prevalent sub-group of abnormal repetitive behaviors among captive animals is that of stereotypies. Previous studies have demonstrated some resemblance between stereotypy in captive animals and in humans, including the involvement of neurological malfunctions that lead to the expression of stereotypies. This malfunction can be evaluated through the use of neuropsychological tasks that assess perseveration as implying a failure of the basal ganglia (BG) to operate properly. Other stud...
Chauke, Miyetani; Malisch, Jessica L.; Robinson, Cymphonee; de Jong, Trynke R.; Saltzman, Wendy
2011-01-01
In several mammalian species, lactating females show blunted neural, hormonal, and behavioral responses to stressors. It is not known whether new fathers also show stress hyporesponsiveness in species in which males provide infant care. To test this possibility, we determined the effects of male and female reproductive status on stress responsiveness in the biparental, monogamous California mouse (Peromyscus californicus).Breeding (N=8 females, 8 males), nonbreeding (N=10 females, 10 males) a...
Regression of altitude-produced cardiac hypertrophy.
Sizemore, D. A.; Mcintyre, T. W.; Van Liere, E. J.; Wilson , M. F.
1973-01-01
The rate of regression of cardiac hypertrophy with time has been determined in adult male albino rats. The hypertrophy was induced by intermittent exposure to simulated high altitude. The percentage hypertrophy was much greater (46%) in the right ventricle than in the left (16%). The regression could be adequately fitted to a single exponential function with a half-time of 6.73 plus or minus 0.71 days (90% CI). There was no significant difference in the rates of regression for the two ventricles.
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
Robust Logistic Regression to Static Geometric Representation of Ratios
Alireza Bahiraie
2009-01-01
Full Text Available Problem statement: Some methodological problems concerning financial ratios such as non-proportionality, non-asymetricity, non-salacity were solved in this study and we presented a complementary technique for empirical analysis of financial ratios and bankruptcy risk. This new method would be a general methodological guideline associated with financial data and bankruptcy risk. Approach: We proposed the use of a new measure of risk, the Share Risk (SR measure. We provided evidence of the extent to which changes in values of this index are associated with changes in each axis values and how this may alter our economic interpretation of changes in the patterns and directions. Our simple methodology provided a geometric illustration of the new proposed risk measure and transformation behavior. This study also employed Robust logit method, which extends the logit model by considering outlier. Results: Results showed new SR method obtained better numerical results in compare to common ratios approach. With respect to accuracy results, Logistic and Robust Logistic Regression Analysis illustrated that this new transformation (SR produced more accurate prediction statistically and can be used as an alternative for common ratios. Additionally, robust logit model outperforms logit model in both approaches and was substantially superior to the logit method in predictions to assess sample forecast performances and regressions. Conclusion/Recommendations: This study presented a new perspective on the study of firm financial statement and bankruptcy. In this study, a new dimension to risk measurement and data representation with the advent of the Share Risk method (SR was proposed. With respect to forecast results, robust loigt method was substantially superior to the logit method. It was strongly suggested the use of SR methodology for ratio analysis, which provided a conceptual and complimentary methodological solution to many problems associated with the
Kruk-Slomka, Marta; Boguszewska-Czubara, Anna; Slomka, Tomasz; Budzynska, Barbara; Biala, Grazyna
2016-01-01
The endocannabinoid system, through cannabinoid (CB) receptors, is involved in memory-related responses, as well as in processes that may affect cognition, like oxidative stress processes. The purpose of the experiments was to investigate the impact of CB1 and CB2 receptor ligands on the long-term memory stages in male Swiss mice, using the passive avoidance (PA) test, as well as the influence of these compounds on the level of oxidative stress biomarkers in the mice brain. A single injection of a selective CB1 receptor antagonist, AM 251, improved long-term memory acquisition and consolidation in the PA test in mice, while a mixed CB1/CB2 receptor agonist WIN 55,212-2 impaired both stages of cognition. Additionally, JWH 133, a selective CB2 receptor agonist, and AM 630, a competitive CB2 receptor antagonist, significantly improved memory. Additionally, an acute administration of the highest used doses of JWH 133, WIN 55,212-2, and AM 630, but not AM 251, increased total antioxidant capacity (TAC) in the brain. In turn, the processes of lipids peroxidation, expressed as the concentration of malondialdehyde (MDA), were more advanced in case of AM 251. Thus, some changes in the PA performance may be connected with the level of oxidative stress in the brain. PMID:26839719
Marta Kruk-Slomka
2016-01-01
Full Text Available The endocannabinoid system, through cannabinoid (CB receptors, is involved in memory-related responses, as well as in processes that may affect cognition, like oxidative stress processes. The purpose of the experiments was to investigate the impact of CB1 and CB2 receptor ligands on the long-term memory stages in male Swiss mice, using the passive avoidance (PA test, as well as the influence of these compounds on the level of oxidative stress biomarkers in the mice brain. A single injection of a selective CB1 receptor antagonist, AM 251, improved long-term memory acquisition and consolidation in the PA test in mice, while a mixed CB1/CB2 receptor agonist WIN 55,212-2 impaired both stages of cognition. Additionally, JWH 133, a selective CB2 receptor agonist, and AM 630, a competitive CB2 receptor antagonist, significantly improved memory. Additionally, an acute administration of the highest used doses of JWH 133, WIN 55,212-2, and AM 630, but not AM 251, increased total antioxidant capacity (TAC in the brain. In turn, the processes of lipids peroxidation, expressed as the concentration of malondialdehyde (MDA, were more advanced in case of AM 251. Thus, some changes in the PA performance may be connected with the level of oxidative stress in the brain.
Heteroscedastic regression analysis method for mixed data
FU Hui-min; YUE Xiao-rui
2011-01-01
The heteroscedastic regression model was established and the heteroscedastic regression analysis method was presented for mixed data composed of complete data, type- I censored data and type- Ⅱ censored data from the location-scale distribution. The best unbiased estimations of regression coefficients, as well as the confidence limits of the location parameter and scale parameter were given. Furthermore, the point estimations and confidence limits of percentiles were obtained. Thus, the traditional multiple regression analysis method which is only suitable to the complete data from normal distribution can be extended to the cases of heteroscedastic mixed data and the location-scale distribution. So the presented method has a broad range of promising applications.
A new bivariate negative binomial regression model
Faroughi, Pouya; Ismail, Noriszura
2014-12-01
This paper introduces a new form of bivariate negative binomial (BNB-1) regression which can be fitted to bivariate and correlated count data with covariates. The BNB regression discussed in this study can be fitted to bivariate and overdispersed count data with positive, zero or negative correlations. The joint p.m.f. of the BNB1 distribution is derived from the product of two negative binomial marginals with a multiplicative factor parameter. Several testing methods were used to check overdispersion and goodness-of-fit of the model. Application of BNB-1 regression is illustrated on Malaysian motor insurance dataset. The results indicated that BNB-1 regression has better fit than bivariate Poisson and BNB-2 models with regards to Akaike information criterion.
Patterns of Regression in Rett Syndrome
J Gordon Millichap
2002-10-01
Full Text Available Patterns and features of regression in a case series of 53 girls and women with Rett syndrome were studied at the Institute of Child Health and Great Ormond Street Children’s Hospital, London, UK.
Genetics Home Reference: caudal regression syndrome
... umbilical artery: Further support for a caudal regression-sirenomelia spectrum. Am J Med Genet A. 2007 Dec ... AK, Dickinson JE, Bower C. Caudal dysgenesis and sirenomelia-single centre experience suggests common pathogenic basis. Am ...
Unbalanced Regressions and the Predictive Equation
Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo
Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the...... theoretical predictive equation by suggesting a data generating process, where returns are generated as linear functions of a lagged latent I(0) risk process. The observed predictor is a function of this latent I(0) process, but it is corrupted by a fractionally integrated noise. Such a process may arise due...... to aggregation or unexpected level shifts. In this setup, the practitioner estimates a misspecified, unbalanced, and endogenous predictive regression. We show that the OLS estimate of this regression is inconsistent, but standard inference is possible. To obtain a consistent slope estimate, we then...
Regression Analysis with a Stochastic Design Variable
Sazak,, Hakan S.; Moti L Tiku; Qamarul Islam, M.
2006-01-01
In regression models, the design variable has primarily been treated as a nonstochastic variable. In numerous situations, however, the design variable is stochastic. The estimation and hypothesis testing problems in such situations are considered. Real life examples are given.
Prediction of Dynamical Systems by Symbolic Regression
Quade, Markus; Shafi, Kamran; Niven, Robert K; Noack, Bernd R
2016-01-01
We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting a...
An Improved Volumetric Estimation Using Polynomial Regression
Noraini Abdullah
2011-12-01
Full Text Available The polynomial regression (PR technique is used to estimate the parameters of the dependent variable having a polynomial relationship with the independent variable. Normality and nonlinearity exhibit polynomial characterization of power terms greater than 2. Polynomial Regression models (PRM with the auxiliary variables are considered up to their third order interactions. Preliminary, multicollinearity between the independent variables is minimized and statistical tests involving the Global, Correlation Coefficient, Wald, and Goodness-of-Fit tests, are carried out to select significant variables with their possible interactions. Comparisons between the polynomial regression models (PRM are made using the eight selection criteria (8SC. The best regression model is identified based on the minimum value of the eight selection criteria (8SC. The use of an appropriate transformation will increase in the degree of a statistically valid polynomial, hence, providing a better estimation for the model.
Bayesian nonparametric regression with varying residual density
Pati, Debdeep; Dunson, David B.
2013-01-01
We consider the problem of robust Bayesian inference on the mean regression function allowing the residual density to change flexibly with predictors. The proposed class of models is based on a Gaussian process prior for the mean regression function and mixtures of Gaussians for the collection of residual densities indexed by predictors. Initially considering the homoscedastic case, we propose priors for the residual density based on probit stick-breaking (PSB) scale mixtures and symmetrized ...
Computing multiple-output regression quantile regions
Paindaveine, D.; Šiman, Miroslav
2012-01-01
Roč. 56, č. 4 (2012), s. 840-853. ISSN 0167-9473 R&D Projects: GA MŠk(CZ) 1M06047 Institutional research plan: CEZ:AV0Z10750506 Keywords : halfspace depth * multiple-output regression * parametric linear programming * quantile regression Subject RIV: BA - General Mathematics Impact factor: 1.304, year: 2012 http://library.utia.cas.cz/separaty/2012/SI/siman-0376413.pdf
Robust Outlier Detection in Linear Regression
Nethal K. Jajo; Xizhi Wu
2004-01-01
New methodology of robust outlier detection based on Robustly Studentized Robust Residuals (RSRR) examination is well established in linear regression analysis. Two new robust location estimators of linear regression parameters are developed in simple and multiple cases. Based on these robust estimators we obtain RSRR. We used RSRR to derive a new measure of distance to be used in outlier detection. A graphical display using new measure of distance is constructed for detecting multiple outlie...
Lognormal and Gamma Mixed Negative Binomial Regression
Zhou, Mingyuan; Li, Lingbo; Dunson, David; Carin, Lawrence
2012-01-01
In regression analysis of counts, a lack of simple and efficient algorithms for posterior computation has made Bayesian approaches appear unattractive and thus underdeveloped. We propose a lognormal and gamma mixed negative binomial (NB) regression model for counts, and present efficient closed-form Bayesian inference; unlike conventional Poisson models, the proposed approach has two free parameters to include two different kinds of random effects, and allows the incorporation of prior inform...
Marginal longitudinal semiparametric regression via penalized splines
Al Kadiri, M.
2010-08-01
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
Kernel Regression Mapping for Vocal EEG Sonification
Hermann, Thomas; Baier, Gerold; Stephani, Ulrich; Ritter, Helge; Susini, Patrick; Warusfel, Olivier
2008-01-01
This paper introduces kernel regression mapping sonification (KRMS) for optimized mappings between data features and the parameter space of Parameter Mapping Sonification. Kernel regression allows to map data spaces to high-dimensional parameter spaces such that specific locations in data space with pre-determined extent are represented by selected acoustic parameter vectors. Thereby, specifically chosen correlated settings of parameters may be selected to create perceptual fingerprints, such...
A qualitative survey of regression testing practices
Engström, Emelie; Runeson, Per
2010-01-01
Aim: Regression testing practices in industry have to be better understood, both for the industry itself and for the research community. Method: We conducted a qualitative industry survey by i) running a focus group meeting with 15 industry participants and ii) validating the outcome in an on line questionnaire with 32 respondents. Results: Regression testing needs and practices vary greatly between and within organizations and at different stages of a project. The importance and challenges o...
Caudal regression syndrome : a case report
Lee, Eun Joo; Kim, Hi Hye; Kim, Hyung Sik; Park, So Young; Han, Hye Young; Lee, Kwang Hun [Chungang Gil Hospital, Incheon (Korea, Republic of)
1998-07-01
Caudal regression syndrome is a rare congenital anomaly, which results from a developmental failure of the caudal mesoderm during the fetal period. We present a case of caudal regression syndrome composed of a spectrum of anomalies including sirenomelia, dysplasia of the lower lumbar vertebrae, sacrum, coccyx and pelvic bones,genitourinary and anorectal anomalies, and dysplasia of the lung, as seen during infantography and MR imaging.
Caudal regression syndrome : a case report
Caudal regression syndrome is a rare congenital anomaly, which results from a developmental failure of the caudal mesoderm during the fetal period. We present a case of caudal regression syndrome composed of a spectrum of anomalies including sirenomelia, dysplasia of the lower lumbar vertebrae, sacrum, coccyx and pelvic bones,genitourinary and anorectal anomalies, and dysplasia of the lung, as seen during infantography and MR imaging
The Geometry of Enhancement in Multiple Regression
Waller, Niels G.
2011-01-01
In linear multiple regression, "enhancement" is said to occur when R[superscript 2] = b[prime]r greater than r[prime]r, where b is a p x 1 vector of standardized regression coefficients and r is a p x 1 vector of correlations between a criterion y and a set of standardized regressors, x. When p = 1 then b [is congruent to] r and enhancement cannot…
A Geometrical Approach to Iterative Isotone Regression
Guyader, Arnaud; Jégou, Nicolas; Németh, Alexander B.; Németh, Sándor Z.
2012-01-01
In the present paper, we propose and analyze a novel method for estimating a univariate regression function of bounded variation. The underpinning idea is to combine two classical tools in nonparametric statistics, namely isotonic regression and the estimation of additive models. A geometrical interpretation enables us to link this iterative method with Von Neumann's algorithm. Moreover, making a connection with the general property of isotonicity of projection onto convex cones, we derive an...
An Improved Volumetric Estimation Using Polynomial Regression
Noraini Abdullah; Amran Ahmed; Zainodin Hj. Jubok
2011-01-01
The polynomial regression (PR) technique is used to estimate the parameters of the dependent variable having a polynomial relationship with the independent variable. Normality and nonlinearity exhibit polynomial characterization of power terms greater than 2. Polynomial Regression models (PRM) with the auxiliary variables are considered up to their third order interactions. Preliminary, multicollinearity between the independent variables is minimized and statistical tests involving the Global...
Fuzzy multiple linear regression: A computational approach
Juang, C. H.; Huang, X. H.; Fleming, J. W.
1992-01-01
This paper presents a new computational approach for performing fuzzy regression. In contrast to Bardossy's approach, the new approach, while dealing with fuzzy variables, closely follows the conventional regression technique. In this approach, treatment of fuzzy input is more 'computational' than 'symbolic.' The following sections first outline the formulation of the new approach, then deal with the implementation and computational scheme, and this is followed by examples to illustrate the new procedure.
Forecasting with Optimized Moving Local Regression
Fedorov, Valery V.; Hackl, Peter; Müller, Werner
1992-01-01
This paper empirically demonstrates the relative merits of the optimal choice of the weight function in a moving local regression as suggested by Fedorov et al., (1993) over traditional weight functions which ignore the form of the local model. The discussion is based on a task that is imbedded into the smoothing methodology, namely the forecasting of business time series data with the help of a one-sided moving local regression model. (author's abstract)
Spontaneous regression of metastatic Merkel cell carcinoma.
Hassan, S J
2010-01-01
Merkel cell carcinoma is a rare aggressive neuroendocrine carcinoma of the skin predominantly affecting elderly Caucasians. It has a high rate of local recurrence and regional lymph node metastases. It is associated with a poor prognosis. Complete spontaneous regression of Merkel cell carcinoma has been reported but is a poorly understood phenomenon. Here we present a case of complete spontaneous regression of metastatic Merkel cell carcinoma demonstrating a markedly different pattern of events from those previously published.
The Infinite Hierarchical Factor Regression Model
Rai, Piyush
2009-01-01
We propose a nonparametric Bayesian factor regression model that accounts for uncertainty in the number of factors, and the relationship between factors. To accomplish this, we propose a sparse variant of the Indian Buffet Process and couple this with a hierarchical model over factors, based on Kingman's coalescent. We apply this model to two problems (factor analysis and factor regression) in gene-expression data analysis.
Asymptotic equivalence for regression under fractional noise
Schmidt-Hieber, Johannes
2013-01-01
Consider estimation of the regression function based on a model with equidistant design and measurement errors generated from a fractional Gaussian noise process. In previous literature, this model has been heuristically linked to an experiment, where the anti-derivative of the regression function is continuously observed under additive perturbation by a fractional Brownian motion. Based on a reformulation of the problem using reproducing kernel Hilbert spaces, we derive abstract approximatio...
Tests in contingency tables as regression tests
Stanislav Anatolyev; Grigory Kosenok
2006-01-01
Applied researchers often use tests based on contingency tables in preliminary data analysis and diagnostic testing. We show that many of such tests may be alternatively implemented by testing for coecient restrictions in linear regression systems (as a rule, employing the Wald test). This uni es the theories of regression analysis and contingency tables, sheds more light on intuitive contents of contingency table tests, and provides a more convenient and familiar tool for practitioners.
Regression Models for Market-Shares
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...... 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....
Correlation, regression, and cointegration of nonstationary economic time series
Johansen, Søren
), and Phillips (1986) found the limit distributions. We propose to distinguish between empirical and population correlation coeffients and show in a bivariate autoregressive model for nonstationary variables that the empirical correlation and regression coe¢ cients do not converge to the relevant...... population values, due to the trending nature of the data. We conclude by giving a simple cointegration analysis of two interests. The analysis illustrates that much more insight can be gained about the dynamic behavior of the nonstationary variables then simply by calculating a correlation coe¢ cient....
Correlation, Regression, and Cointegration of Nonstationary Economic Time Series
Johansen, Søren
), and Phillips (1986) found the limit distributions. We propose to distinguish between empirical and population correlation coefficients and show in a bivariate autoregressive model for nonstationary variables that the empirical correlation and regression coefficients do not converge to the relevant...... population values, due to the trending nature of the data. We conclude by giving a simple cointegration analysis of two interests. The analysis illustrates that much more insight can be gained about the dynamic behavior of the nonstationary variables then simply by calculating a correlation coefficient...
Al Mofleh Ibrahim
1997-01-01
Full Text Available The past few years have witnessed a tremendous progress in our knowledge regarding the pathogenesis, diagnosis, prognostic evaluation and classification of acute pancreatitis. The role of ischemia, lysosomal enzymes, oxygen free radicals, polymorphnuclear cells-byproducts and inflammatory mediators in the pathogenesis of pancreatic necrosis and multiple organ failure has been emphasized. Furthermore, the recent knowledge about agents infecting pancreatic necrosis, routes of infection, bacteriological examination of fine needle aspirate and appropriate antibiotics have changed the concept of acute pancreatitis. New diagnostic tests such as rapid urinary trypsinogen-2 test and inflammatory mediators including polymorphnuclear elastase, C-reactive protein and interleukin-6 contribute to early diagnosis, prognostic evaluation and initiation of an appropriate therapy.
Ginty, Annie T; Williams, Sarah E; Jones, Alexander; Roseboom, Tessa J; Phillips, Anna C; Painter, Rebecca C; Carroll, Douglas; de Rooij, Susanne R
2016-06-01
Recent evidence demonstrates that individuals with low heart rate (HR) reactions to acute psychological stress are more likely to be obese or smokers. Smoking and obesity are established risk factors for increased carotid intima-media thickness (IMT). The aim of this study was to examine the potential pathways linking intima-media thickness, smoking, body mass index (BMI), and HR stress reactivity. A total of 552 participants, 47.6% male, M (SD) age = 58.3 (0.94) years, were exposed to three psychological stress tasks (Stroop, mirror drawing, and speech) preceded by a resting baseline period; HR was recorded throughout. HR reactivity was calculated as the average response across the three tasks minus average baseline HR. Smoking status, BMI, and IMT were determined by trained personnel. Controlling for important covariates (e.g., socioeconomic status), structural equation modeling revealed that BMI and smoking mediated the negative relationship between HR reactivity and IMT. The hypothesized model demonstrated a good overall fit to the data, χ(2) (8) = 0.692, p = .403; CFI = 1.00; TLI = 1.00 SRMR = .01; RMSEA < .001 (90% CI < 0.01-0.11). HR reactivity was negatively related to BMI (β = -.16) and smoking (β = -.18), and these in turn were positively associated with IMT (BMI: β = .10; smoking: β = .17). Diminished HR stress reactivity appears to be a marker for enlarged IMT and appears to be exerting its impact through already established risks. Future research should examine this relationship longitudinally and aim to intervene early. PMID:27005834
余学; 戴秀英; 李秋丽; 王玲玲; 李林贵
2014-01-01
Objective To understand the behavioral problems and related factors of left-behind children in rural areas of Ningxia . Methods A total of 2000 students were sampled by stratified randomly and cluster from the whole class of the southern rural areas in Ningxia.The general self-made questionnaire,Egma Minnen av Bardndosnauppforstran (EMBU),Eysenck Personality Questionnaire(EPQ Children),Piers-Harri Children's Self-concept Scale(PHCSS)and Achenbach's Child behavior Checklist(CBCL parent version)were used to investigate the related factors .Results ①The overall detection rate of child behavior problems was 18.0%(343/1905).The detection rate of left-behind children and non-left-behind children was 20.9%and 16.0%respectively,and the difference was significant between the two groups(χ2 =7.66,P=0.006).The behavior problems of Han left behind and non-left-behind children were detected in 17.0%and 11.2%respectively,and there was significant difference between two groups (χ2 =6.64,P=0.010).The detection rate of Muslim left-behind children behavioral problems (25.1%) was significantly higher than that of Han left-behind children(17.0%)(χ2 =7.51,P=0.006).②Multivariate logistic regression analysis showed that parents working outside (OR=1.239),Extraversion personality (OR=0.807),Neurotic personality(OR=1.310),father overprotection(OR=1.727),mother refusing and denying(OR=1.561)and total score of self-awareness(OR=0.613)eventually entered the equation,which could be directly predicted the occurrence of psycholog-ical and behavioral problems .Emotional instability ,father overprotection ,mother refusing and denying ,and both parents working outside were risk factors ,whereas the extroversion ( high score features ) and good self-awareness were protective factors .Conclusion Left-behind children have higher incidence of behavioral problems in rural areas of Ningxia ,which has many influence factors .Measures should be taken from social ,family,character building and other
Use of Pollutant Load Regression Models with Various Sampling Frequencies for Annual Load Estimation
Youn Shik Park; Bernie A. Engel
2014-01-01
Water quality data are collected by various sampling frequencies, and the data may not be collected at a high frequency nor over the range of streamflow conditions. Therefore, regression models are used to estimate pollutant data for days on which water quality data were not measured. Pollutant load regression models were evaluated with six sampling frequencies for daily nitrogen, phosphorus, and sediment data. Annual pollutant load estimates exhibited various behaviors by sampling frequency...
David C. Randall
2011-08-01
Full Text Available We recorded via telemetry the arterial blood pressure (BP and heart rate (HR response to classical conditioning following the spontaneous onset of autoimmune diabetes in BBDP/Wor rats versus age-matched, diabetes resistant control (BBDR/Wor rats. Our purpose was to evaluate the autonomic regulatory responses to an acute stress in a diabetic state of up to 12 months duration. The stress was a 15 sec. pulsed tone (CS+ followed by a 0.5 sec. tail shock. The initial, transient increase in BP (i.e., the ‘first component’, or C1, known to be derived from an orienting response and produced by a sympathetic increase in peripheral resistance, was similar in diabetic and control rats through ~9 months of diabetes; it was smaller in diabetic rats 10 months after diabetes onset. Weakening of the C1 BP increase in rats that were diabetic for > 10 months is consistent with the effects of sympathetic neuropathy. A longer-latency, smaller, but sustained ‘second component’ (C2 conditional increase in BP, that is acquired as a rat learns the association between CS+ and the shock, and which results from an increase in cardiac output, was smaller in the diabetic vs. control rats starting from the first month of diabetes. A concomitant HR slowing was also smaller in diabetic rats. The difference in the C2 BP increase, as observed already during the first month of diabetes, is probably secondary to the effects of hyperglycemia upon myocardial metabolism and contractile function, but it may also result from effects on cognition. The small HR slowing concomitant with the C2 pressor event is probably secondary to differences in baroreflex activation or function, though parasympathetic dysfunction may contribute later in the duration of diabetes. The nearly immediate deficit after disease onset in the C2 response indicates that diabetes alters BP and HR responses to external challenges prior to the development of structural changes in the vasculature or autonomic
Rahier, J F; Lion, L; Dewit, O; Lambert, M
2005-01-01
The association of inflammatory bowel disease and acute febrile neutrophilic dermatitis (Sweet's syndrome) has infrequently been reported in the literature. We describe the case of a 41-year-old Caucasian woman with ileo- anal Crohn's disease who presented simultaneously an erythema nodosum and a Sweet's syndrome. A dramatic regression of the cutaneous lesions was observed after infliximab treatment, indicating that this therapy might be useful for both Crohn's disease and Sweet's syndrome. PMID:16268426
Effect of HIV infection on time to recovery from an acute manic episode
E Nakimuli-Mpungu; B Mutamba; Nshemerirwe, S; Kiwuwa, MS; Musisi, S
2010-01-01
Introduction Understanding factors affecting the time to recovery from acute mania is critical in the management of manic syndromes. The aim of this study was to determine the effect of HIV infection on time to recovery from acute mania. Methods We performed a retrospective study in which medical charts of individuals who were treated for acute mania were reviewed. Survival analysis with Cox regression models were used to compare time to recovery from an acute manic episode between human immu...
Inverse Regression for the Wiener Class of Systems
Lyzell, Christian; Enqvist, Martin
2011-01-01
The concept of inverse regression has turned out to be quite useful for dimension reduction in regression analysis problems. Using methods like sliced inverse regression (SIR) and directional regression (DR), some high-dimensional nonlinear regression problems can be turned into more tractable low-dimensional problems. Here, the usefulness of inverse regression for identification of nonlinear dynamical systems will be discussed. In particular, it will be shown that the inverse regression meth...
Hierarchical regression for analyses of multiple outcomes.
Richardson, David B; Hamra, Ghassan B; MacLehose, Richard F; Cole, Stephen R; Chu, Haitao
2015-09-01
In cohort mortality studies, there often is interest in associations between an exposure of primary interest and mortality due to a range of different causes. A standard approach to such analyses involves fitting a separate regression model for each type of outcome. However, the statistical precision of some estimated associations may be poor because of sparse data. In this paper, we describe a hierarchical regression model for estimation of parameters describing outcome-specific relative rate functions and associated credible intervals. The proposed model uses background stratification to provide flexible control for the outcome-specific associations of potential confounders, and it employs a hierarchical "shrinkage" approach to stabilize estimates of an exposure's associations with mortality due to different causes of death. The approach is illustrated in analyses of cancer mortality in 2 cohorts: a cohort of dioxin-exposed US chemical workers and a cohort of radiation-exposed Japanese atomic bomb survivors. Compared with standard regression estimates of associations, hierarchical regression yielded estimates with improved precision that tended to have less extreme values. The hierarchical regression approach also allowed the fitting of models with effect-measure modification. The proposed hierarchical approach can yield estimates of association that are more precise than conventional estimates when one wishes to estimate associations with multiple outcomes. PMID:26232395
Regression Test Selection for C# Programs
Nashat Mansour
2009-01-01
Full Text Available We present a regression test selection technique for C# programs. C# is fairly new and is often used within the Microsoft .Net framework to give programmers a solid base to develop a variety of applications. Regression testing is done after modifying a program. Regression test selection refers to selecting a suitable subset of test cases from the original test suite in order to be rerun. It aims to provide confidence that the modifications are correct and did not affect other unmodified parts of the program. The regression test selection technique presented in this paper accounts for C#.Net specific features. Our technique is based on three phases; the first phase builds an Affected Class Diagram consisting of classes that are affected by the change in the source code. The second phase builds a C# Interclass Graph (CIG from the affected class diagram based on C# specific features. In this phase, we reduce the number of selected test cases. The third phase involves further reduction and a new metric for assigning weights to test cases for prioritizing the selected test cases. We have empirically validated the proposed technique by using case studies. The empirical results show the usefulness of the proposed regression testing technique for C#.Net programs.
Regressão da fibrose hepática Regression of hepatic fibrosis
Zilton A. Andrade
2005-12-01
Full Text Available Durante muito tempo, se acreditou que a fibrose hepática extensa e de longa duração fosse um processo irreversível. As investigações sobre o comportamento da fibrose hepática, nas formas avançadas da esquistossomose, vieram abalar este conceito e hoje em dia está se estabelecendo a noção de que qualquer fibrose é reversível, inclusive aquela associada à cirrose hepática. O problema é identificar sua causa e removê-la. Embora, a fibrose hepática tenha per se pouca significação fisiopatológica, sua gravidade está relacionada com as alterações vasculares que ela encerra. O que dá ao assunto primordial importância são os indícios até aqui obtidos de que, a regressão da fibrose costuma se acompanhar de uma remodelação das alterações vasculares no seu interior. Mas, há peculiaridades relativas ao tipo anatômico e ao papel fisiológico que certas fibroses exibem, e tais peculiaridades podem interferir com o processo regressivo da mesma, o que pode significar que por vezes a fibrose pode se tornar permanente. Esses assuntos, alguns deles controversos, são aqui apresentados e discutidos.Extensive and persistent hepatic fibrosis has for a long time been considered irreversible. However, recent studies on the behavior of hepatic fibrosis, especially those related to evolution and involution of advanced schistosomiasis in man, have challenged this concept, and nowadays it is becoming clear that any type of fibrosis is reversible, including that associated with hepatic cirrhosis. The problem consists in identifying and eliminating its cause. Although fibrosis in the liver has little functional significance by itself, its severity derives from associated vascular changes. However, new data on fibrosis regression indicate that disappearance of fibrosis is usually accompanied by remodeling of vascular changes. But, there are peculiarities related to the anatomic type of fibrosis and to its functional significance, which
Federico A. Sturzeneger
1992-03-01
Full Text Available Currency Substitution and the Regressivity of Inflationary Taxation The purpose of this paper is to show that in the presence of financial adaptation or currency substitution. the inflation tax is extremely regressive. This regressivity arises from the existence of a fixed cost of switching to inflation-proof transactions technologies. This fixed cost makes it optimal only for those agents with sufficiently high incomes to switch out of domestic currency. The effects are illustrated and quantified for a particular case.
Acute abdomen may be connected with the injury of one of the internal organs, injury of large blood vessels, with the spreading of pains from some other area. It may also be a manifestation of systemic disease or poisoning. The main purposes of radiodiagnosis are: determination of the cause of clinical syndrome; determination of the localization and spreading of pathological changes in abdominal organs; finding out the character of complications. If the data of the ordinary roentgenological investiagtion and isn't complete, the computer tomography of abdominal and pelvic cavities is needed
Variable and subset selection in PLS regression
Høskuldsson, Agnar
2001-01-01
The purpose of this paper is to present some useful methods for introductory analysis of variables and subsets in relation to PLS regression. We present here methods that are efficient in finding the appropriate variables or subset to use in the PLS regression. The general conclusion...... is that variable selection is important for successful analysis of chemometric data. An important aspect of the results presented is that lack of variable selection can spoil the PLS regression, and that cross-validation measures using a test set can show larger variation, when we use different subsets of X, than...... obtained by different methods. We also present an approach to orthogonal scatter correction. The procedures and comparisons are applied to industrial data. (C) 2001 Elsevier Science B.V. All rights reserved....
LINEAR REGRESSION WITH R AND HADOOP
Bogdan OANCEA
2015-07-01
Full Text Available In this paper we present a way to solve the linear regression model with R and Hadoop using the Rhadoop library. We show how the linear regression model can be solved even for very large models that require special technologies. For storing the data we used Hadoop and for computation we used R. The interface between R and Hadoop is the open source library RHadoop. We present the main features of the Hadoop and R software systems and the way of interconnecting them. We then show how the least squares solution for the linear regression problem could be expressed in terms of map-reduce programming paradigm and how could be implemented using the Rhadoop library.
Linear and robust Gaussian regression filters
This paper presents a brief overview about Gaussian regression filters to extract surface roughness. The mathematical background in the spatial as well as in the frequency domain is discussed. It is shown that Gaussian regression filters work without any running in and running out sections and can approximate form up to pth degree. In the industrial world it is well known that linear filters are non robust, which means that any protruding peak or valley (also called 'outlier') leads to a distorted roughness topography and effects the calculation of surface parameters directly. In particular plateau like surfaces are good candidates for such critical datasets. In the paper it is shown that such a distortion can be avoided by choosing an appropriate Ψ function. This proceeding leads to the so called robust Gaussian regression filter with all the advanced properties of the linear one
On Solving Lq-Penalized Regressions
Tracy Zhou Wu
2007-01-01
Full Text Available Lq-penalized regression arises in multidimensional statistical modelling where all or part of the regression coefficients are penalized to achieve both accuracy and parsimony of statistical models. There is often substantial computational difficulty except for the quadratic penalty case. The difficulty is partly due to the nonsmoothness of the objective function inherited from the use of the absolute value. We propose a new solution method for the general Lq-penalized regression problem based on space transformation and thus efficient optimization algorithms. The new method has immediate applications in statistics, notably in penalized spline smoothing problems. In particular, the LASSO problem is shown to be polynomial time solvable. Numerical studies show promise of our approach.
Principal component regression for crop yield estimation
Suryanarayana, T M V
2016-01-01
This book highlights the estimation of crop yield in Central Gujarat, especially with regard to the development of Multiple Regression Models and Principal Component Regression (PCR) models using climatological parameters as independent variables and crop yield as a dependent variable. It subsequently compares the multiple linear regression (MLR) and PCR results, and discusses the significance of PCR for crop yield estimation. In this context, the book also covers Principal Component Analysis (PCA), a statistical procedure used to reduce a number of correlated variables into a smaller number of uncorrelated variables called principal components (PC). This book will be helpful to the students and researchers, starting their works on climate and agriculture, mainly focussing on estimation models. The flow of chapters takes the readers in a smooth path, in understanding climate and weather and impact of climate change, and gradually proceeds towards downscaling techniques and then finally towards development of ...
Uncertainty quantification in DIC with Kriging regression
Wang, Dezhi; DiazDelaO, F. A.; Wang, Weizhuo; Lin, Xiaoshan; Patterson, Eann A.; Mottershead, John E.
2016-03-01
A Kriging regression model is developed as a post-processing technique for the treatment of measurement uncertainty in classical subset-based Digital Image Correlation (DIC). Regression is achieved by regularising the sample-point correlation matrix using a local, subset-based, assessment of the measurement error with assumed statistical normality and based on the Sum of Squared Differences (SSD) criterion. This leads to a Kriging-regression model in the form of a Gaussian process representing uncertainty on the Kriging estimate of the measured displacement field. The method is demonstrated using numerical and experimental examples. Kriging estimates of displacement fields are shown to be in excellent agreement with 'true' values for the numerical cases and in the experimental example uncertainty quantification is carried out using the Gaussian random process that forms part of the Kriging model. The root mean square error (RMSE) on the estimated displacements is produced and standard deviations on local strain estimates are determined.
A tutorial on Bayesian Normal linear regression
Klauenberg, Katy; Wübbeler, Gerd; Mickan, Bodo; Harris, Peter; Elster, Clemens
2015-12-01
Regression is a common task in metrology and often applied to calibrate instruments, evaluate inter-laboratory comparisons or determine fundamental constants, for example. Yet, a regression model cannot be uniquely formulated as a measurement function, and consequently the Guide to the Expression of Uncertainty in Measurement (GUM) and its supplements are not applicable directly. Bayesian inference, however, is well suited to regression tasks, and has the advantage of accounting for additional a priori information, which typically robustifies analyses. Furthermore, it is anticipated that future revisions of the GUM shall also embrace the Bayesian view. Guidance on Bayesian inference for regression tasks is largely lacking in metrology. For linear regression models with Gaussian measurement errors this tutorial gives explicit guidance. Divided into three steps, the tutorial first illustrates how a priori knowledge, which is available from previous experiments, can be translated into prior distributions from a specific class. These prior distributions have the advantage of yielding analytical, closed form results, thus avoiding the need to apply numerical methods such as Markov Chain Monte Carlo. Secondly, formulas for the posterior results are given, explained and illustrated, and software implementations are provided. In the third step, Bayesian tools are used to assess the assumptions behind the suggested approach. These three steps (prior elicitation, posterior calculation, and robustness to prior uncertainty and model adequacy) are critical to Bayesian inference. The general guidance given here for Normal linear regression tasks is accompanied by a simple, but real-world, metrological example. The calibration of a flow device serves as a running example and illustrates the three steps. It is shown that prior knowledge from previous calibrations of the same sonic nozzle enables robust predictions even for extrapolations.
Recurrence of hepatocellular carcinoma with rapid growth after spontaneous regression
Tomoki Nakajima; Michihisa Moriguchi; Tadashi Watanabe; Masao Noda; Nobuaki Fuji; Masahito Minami; Yoshito Itoh; Takeshi Okanoue
2004-01-01
We report an 80-year-old man who presented with spontaneous regression of hepatocellular carcinoma (HCC). He complained of sudden right flank pain and low-grade fever.The level of protein induced by vitamin K antagonist (PIVKA)-Ⅱ was 1 137 mAU/mL. A computed tomography scan in November 2000 demonstrated a low-density mass located in liver S4 with marginal enhancement and a cystic mass of 68 mmx55 mm in liver S6, with slightly high density content and without marginal enhancement. Angiography revealed that the tumor in S4 with a size of 25 mm×20 mm was a typical hypervascular HCC, and transarterial chemoembolization was performed. However, the tumor in S6 was hypovascular and atypical of HCC, and thus no therapy was given. In December 2000, the cystic mass regressed spontaneously to 57 mmx44 mm, and aspiration cytology revealed bloody fluid, and the mass was diagnosed cytologically as class Ⅰ.The tumor in S4 was treated successfully with a 5 mm margin of safety around it. The PⅣKA-Ⅱ level normalized in February2001. In July 2001, the tumor regressed further but presented with an enhanced area at the posterior margin. In November2001, the enhanced area extended, and a biopsy revealed well-differentiated HCC, although the previous tumor in S4 disappeared. Angiography demonstrated two tumor stains, one was in S6, which was previously hypovascular,and the other was in S8. Subsequently, the PⅣKA-Ⅱ level started to rise with the doubling time of 2-3 wk, and the tumor grew rapidly despite repeated transarterial embolization with gel foam. In February 2003, the patient died of bleeding into the peritoneal cavity from the tumor that occupied almost the entire right lobe. Considering the acute onset of the symptoms, we speculate that local ischemia possibly due to rapid tumor growth, resulted in intratumoral bleeding and/or hemorrhagic necrosis, and finally spontaneous regression of the initial tumor in S6.
Panel data specifications in nonparametric kernel regression
Czekaj, Tomasz Gerard; Henningsen, Arne
We discuss nonparametric regression models for panel data. A fully nonparametric panel data specification that uses the time variable and the individual identifier as additional (categorical) explanatory variables is considered to be the most suitable. We use this estimator and conventional...... parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...... found the estimates of the fully nonparametric panel data model to be more reliable....
LINEAR REGRESSION WITH R AND HADOOP
Oancea, Bogdan
2015-01-01
In this paper we present a way to solve the linear regression model with R and Hadoop using the Rhadoop library. We show how the linear regression model can be solved even for very large models that require special technologies. For storing the data we used Hadoop and for computation we used R. The interface between R and Hadoop is the open source library RHadoop. We present the main features of the Hadoop and R software systems and the way of interconnecting them. We then show how the least ...
Confidence intervals in regression utilizing prior information
Kabaila, Paul; Giri, Khageswor
2007-01-01
We consider a linear regression model with regression parameter beta=(beta_1,...,beta_p) and independent and identically N(0,sigma^2) distributed errors. Suppose that the parameter of interest is theta = a^T beta where a is a specified vector. Define the parameter tau=c^T beta-t where the vector c and the number t are specified and a and c are linearly independent. Also suppose that we have uncertain prior information that tau = 0. We present a new frequentist 1-alpha confidence interval for ...
On directional multiple-output quantile regression
Paindaveine, D.; Šiman, Miroslav
2011-01-01
Roč. 102, č. 2 (2011), s. 193-212. ISSN 0047-259X R&D Projects: GA MŠk(CZ) 1M06047 Grant ostatní: Commision EC(BE) Fonds National de la Recherche Scientifique Institutional research plan: CEZ:AV0Z10750506 Keywords : multivariate quantile * quantile regression * multiple-output regression * halfspace depth * portfolio optimization * value-at risk Subject RIV: BA - General Mathematics Impact factor: 0.879, year: 2011 http://library.utia.cas.cz/separaty/2011/SI/siman-0364128.pdf
Modeling data for pancreatitis in presence of a duodenal diverticula using logistic regression
Dineva, S.; Prodanova, K.; Mlachkova, D.
2013-12-01
The presence of a periampullary duodenal diverticulum (PDD) is often observed during upper digestive tract barium meal studies and endoscopic retrograde cholangiopancreatography (ERCP). A few papers reported that the diverticulum had something to do with the incidence of pancreatitis. The aim of this study is to investigate if the presence of duodenal diverticula predisposes to the development of a pancreatic disease. A total 3966 patients who had undergone ERCP were studied retrospectively. They were divided into 2 groups-with and without PDD. Patients with a duodenal diverticula had a higher rate of acute pancreatitis. The duodenal diverticula is a risk factor for acute idiopathic pancreatitis. A multiple logistic regression to obtain adjusted estimate of odds and to identify if a PDD is a predictor of acute or chronic pancreatitis was performed. The software package STATISTICA 10.0 was used for analyzing the real data.
无
2007-01-01
Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study,measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respectively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demonstrates that it is feasible to estimate the disease severity office brown spot using hyperspectral reflectance data at the leaf level.
Liu, Zhan-yu; Huang, Jing-feng; Shi, Jing-jing; Tao, Rong-xiang; Zhou, Wan; Zhang, Li-Li
2007-10-01
Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2,500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respectively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demonstrates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level. PMID:17910117
Combes, Alain
2013-05-01
Myocarditis is defined as inflammation of the myocardium accompanied by myocellular necrosis. Acute myocarditis must be considered in patients who present with recent onset of cardiac failure or arrhythmia. Fulminant myocarditis is a distinct entity characterized by sudden onset of severe congestive heart failure or cardiogenic shock, usually following a flu-like illness, parvovirus B19, human herpesvirus 6, coxsackievirus and adenovirus being the most frequently viruses responsible for the disease. Treatment of myocarditis remains largely supportive, since immunosuppression has not been proven to be beneficial for acute lymphocytic myocarditis. Trials of antiviral therapies, or immunostimulants such as interferons, suggest a potential therapeutic role but require further investigation. Lastly, early recognition of patients rapidly progressing to refractory cardiac failure and their immediate transfer to a medical-surgical center experienced in mechanical circulatory support is warranted. In this setting, ECMO should be the first-line mechanical assistance. For highly unstable patients, a Mobile Cardiac Assistance Unit, that rapidly travels to primary care hospitals with a portable ECMO system and hooks it up before refractory multiorgan failure takes hold, is the preferred option. PMID:23789482
Demonstration of a Fiber Optic Regression Probe
Korman, Valentin; Polzin, Kurt A.
2010-01-01
The capability to provide localized, real-time monitoring of material regression rates in various applications has the potential to provide a new stream of data for development testing of various components and systems, as well as serving as a monitoring tool in flight applications. These applications include, but are not limited to, the regression of a combusting solid fuel surface, the ablation of the throat in a chemical rocket or the heat shield of an aeroshell, and the monitoring of erosion in long-life plasma thrusters. The rate of regression in the first application is very fast, while the second and third are increasingly slower. A recent fundamental sensor development effort has led to a novel regression, erosion, and ablation sensor technology (REAST). The REAST sensor allows for measurement of real-time surface erosion rates at a discrete surface location. The sensor is optical, using two different, co-located fiber-optics to perform the regression measurement. The disparate optical transmission properties of the two fiber-optics makes it possible to measure the regression rate by monitoring the relative light attenuation through the fibers. As the fibers regress along with the parent material in which they are embedded, the relative light intensities through the two fibers changes, providing a measure of the regression rate. The optical nature of the system makes it relatively easy to use in a variety of harsh, high temperature environments, and it is also unaffected by the presence of electric and magnetic fields. In addition, the sensor could be used to perform optical spectroscopy on the light emitted by a process and collected by fibers, giving localized measurements of various properties. The capability to perform an in-situ measurement of material regression rates is useful in addressing a variety of physical issues in various applications. An in-situ measurement allows for real-time data regarding the erosion rates, providing a quick method for
Profile-driven regression for modeling and runtime optimization of mobile networks
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...
RECURRENT SEASONAL ACUTE PSYCHOSIS
Agarwal, Vivek
1999-01-01
Acute psychoses have been reported to occur more frequently in summer. This is a report of seasonal recurrence of acute psychosis in a patient. This case report emphasizes towards the biological etiology of acute psychoses.
Cerebellar ataxia; Ataxia - acute cerebellar; Cerebellitis; Post-varicella acute cerebellar ataxia; PVACA ... Acute cerebellar ataxia in children, especially younger than age 3, may occur several weeks after an illness caused by a virus. ...
Lin, Yingzhi; Deng, Xiangzheng; Li, Xing; Ma, Enjun
2014-12-01
Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.
Williams, John D.; Lindem, Alfred C.
Four computer programs using the general purpose multiple linear regression program have been developed. Setwise regression analysis is a stepwise procedure for sets of variables; there will be as many steps as there are sets. Covarmlt allows a solution to the analysis of covariance design with multiple covariates. A third program has three…
Bayesian nonparametric regression with varying residual density.
Pati, Debdeep; Dunson, David B
2014-02-01
We consider the problem of robust Bayesian inference on the mean regression function allowing the residual density to change flexibly with predictors. The proposed class of models is based on a Gaussian process prior for the mean regression function and mixtures of Gaussians for the collection of residual densities indexed by predictors. Initially considering the homoscedastic case, we propose priors for the residual density based on probit stick-breaking (PSB) scale mixtures and symmetrized PSB (sPSB) location-scale mixtures. Both priors restrict the residual density to be symmetric about zero, with the sPSB prior more flexible in allowing multimodal densities. We provide sufficient conditions to ensure strong posterior consistency in estimating the regression function under the sPSB prior, generalizing existing theory focused on parametric residual distributions. The PSB and sPSB priors are generalized to allow residual densities to change nonparametrically with predictors through incorporating Gaussian processes in the stick-breaking components. This leads to a robust Bayesian regression procedure that automatically down-weights outliers and influential observations in a locally-adaptive manner. Posterior computation relies on an efficient data augmentation exact block Gibbs sampler. The methods are illustrated using simulated and real data applications. PMID:24465053
Regression Segmentation for M³ Spinal Images.
Wang, Zhijie; Zhen, Xiantong; Tay, KengYeow; Osman, Said; Romano, Walter; Li, Shuo
2015-08-01
Clinical routine often requires to analyze spinal images of multiple anatomic structures in multiple anatomic planes from multiple imaging modalities (M(3)). Unfortunately, existing methods for segmenting spinal images are still limited to one specific structure, in one specific plane or from one specific modality (S(3)). In this paper, we propose a novel approach, Regression Segmentation, that is for the first time able to segment M(3) spinal images in one single unified framework. This approach formulates the segmentation task innovatively as a boundary regression problem: modeling a highly nonlinear mapping function from substantially diverse M(3) images directly to desired object boundaries. Leveraging the advancement of sparse kernel machines, regression segmentation is fulfilled by a multi-dimensional support vector regressor (MSVR) which operates in an implicit, high dimensional feature space where M(3) diversity and specificity can be systematically categorized, extracted, and handled. The proposed regression segmentation approach was thoroughly tested on images from 113 clinical subjects including both disc and vertebral structures, in both sagittal and axial planes, and from both MRI and CT modalities. The overall result reaches a high dice similarity index (DSI) 0.912 and a low boundary distance (BD) 0.928 mm. With our unified and expendable framework, an efficient clinical tool for M(3) spinal image segmentation can be easily achieved, and will substantially benefit the diagnosis and treatment of spinal diseases. PMID:25361503
Model Selection in Kernel Ridge Regression
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...
Poisson regression analysis of ungrouped data
Loomis, D; Richardson, D.; Elliott, L
2005-01-01
Background: Poisson regression is routinely used for analysis of epidemiological data from studies of large occupational cohorts. It is typically implemented as a grouped method of data analysis in which all exposure and covariate information is categorised and person-time and events are tabulated.
Optimal Changepoint Tests for Normal Linear Regression
Donald W.K. Andrews; Inpyo Lee; Werner Ploberger
1992-01-01
This paper determines a class of finite sample optimal tests for the existence of a changepoint at an unknown time in a normal linear multiple regression model with known variance. Optimal tests for multiple changepoints are also derived. Power comparisons of several tests are provided based on simulations.
Bootstrap inference longitudinal semiparametric regression model
Pane, Rahmawati; Otok, Bambang Widjanarko; Zain, Ismaini; Budiantara, I. Nyoman
2016-02-01
Semiparametric regression contains two components, i.e. parametric and nonparametric component. Semiparametric regression model is represented by yt i=μ (x˜'ti,zt i)+εt i where μ (x˜'ti,zt i)=x˜'tiβ ˜+g (zt i) and yti is response variable. It is assumed to have a linear relationship with the predictor variables x˜'ti=(x1 i 1,x2 i 2,…,xT i r) . Random error εti, i = 1, …, n, t = 1, …, T is normally distributed with zero mean and variance σ2 and g(zti) is a nonparametric component. The results of this study showed that the PLS approach on longitudinal semiparametric regression models obtain estimators β˜^t=[X'H(λ)X]-1X'H(λ )y ˜ and g˜^λ(z )=M (λ )y ˜ . The result also show that bootstrap was valid on longitudinal semiparametric regression model with g^λ(b )(z ) as nonparametric component estimator.
Piecewise linear regression splines with hyperbolic covariates
Consider the problem of fitting a curve to data that exhibit a multiphase linear response with smooth transitions between phases. We propose substituting hyperbolas as covariates in piecewise linear regression splines to obtain curves that are smoothly joined. The method provides an intuitive and easy way to extend the two-phase linear hyperbolic response model of Griffiths and Miller and Watts and Bacon to accommodate more than two linear segments. The resulting regression spline with hyperbolic covariates may be fit by nonlinear regression methods to estimate the degree of curvature between adjoining linear segments. The added complexity of fitting nonlinear, as opposed to linear, regression models is not great. The extra effort is particularly worthwhile when investigators are unwilling to assume that the slope of the response changes abruptly at the join points. We can also estimate the join points (the values of the abscissas where the linear segments would intersect if extrapolated) if their number and approximate locations may be presumed known. An example using data on changing age at menarche in a cohort of Japanese women illustrates the use of the method for exploratory data analysis. (author)
Statistics review 7: Correlation and regression
Bewick, Viv; Cheek, Liz; Ball, Jonathan
2003-01-01
The present review introduces methods of analyzing the relationship between two quantitative variables. The calculation and interpretation of the sample product moment correlation coefficient and the linear regression equation are discussed and illustrated. Common misuses of the techniques are considered. Tests and confidence intervals for the population parameters are described, and failures of the underlying assumptions are highlighted.
Spontaneous regression of an intraspinal disc cyst
Demaerel, P.; Eerens, I.; Wilms, G. [University Hospital, Leuven (Belgium). Dept. of Radiology; Goffin, J. [Dept. of Neurosurgery, University Hospitals, Leuven (Belgium)
2001-11-01
We present a patient with a so-called disc cyst. Its location in the ventrolateral epidural space and its communication with the herniated disc are clearly shown. The disc cyst developed rapidly and regressed spontaneously. This observation, which has not been reported until now, appears to support focal degeneration with cyst formation as the pathogenesis. (orig.)
Regression testing Ajax applications: coping with dynamism
Roest, D.; Mesbah, A.; Van Deursen, A.
2009-01-01
Note: This paper is a pre-print of: Danny Roest, Ali Mesbah and Arie van Deursen. Regression Testing AJAX Applications: Coping with Dynamism. In Proceedings of the 3rd International Conference on Software Testing, Verification and Validation (ICST’10), Paris, France. IEEE Computer Society, 2010. Th
Hybrid Particle Swarm Optimization for Regression Testing
Dr. Arvinder Kaur
2011-05-01
Full Text Available Regression Testing ensures that any enhancement made to software will not affect specified functionality of software. The execution of all test cases can be long and complex to run; this makes it a costlier process. The prioritization of test cases can help in reduction in cost of regression testing, as it is inefficient to re- run each and every test case. In this research paper, the criterion considered is of maximum fault coverage in minimum execution time. In this research paper, the Hybrid Particle Swarm Optimization (HPSO algorithm has been used, to make regression testing efficient. The HPSO is acombination of Particle Swarm Optimization (PSO technique and Genetic Algorithms (GA, to widen the search space for the solution. The Genetic Algorithm (GA operators provides optimized way to performprioritization in regression testing and on blending it with Particle Swarm Optimization (PSO technique makes it effective and provides fast solution. The Genetic Algorithm (GA operator that has been used is Mutation operator which allows the search engine to evaluate all aspects of the search space. Here, AVERAGE PERCENTAGE OF FAULTS DETECTED (APFD metric has been used to represent the solution derived from HPSO for better transparency in proposed algorithm.
Nonparametric and semiparametric dynamic additive regression models
Scheike, Thomas Harder; Martinussen, Torben
Dynamic additive regression models provide a flexible class of models for analysis of longitudinal data. The approach suggested in this work is suited for measurements obtained at random time points and aims at estimating time-varying effects. Both fully nonparametric and semiparametric models can...
A simple bivariate count data regression model
Shiferaw Gurmu; John Elder
2007-01-01
This paper develops a simple bivariate count data regression model in which dependence between count variables is introduced by means of stochastically related unobserved heterogeneity components. Unlike existing commonly used bivariate models, we obtain a computationally simple closed form of the model with an unrestricted correlation pattern.
Finite Algorithms for Robust Linear Regression
Madsen, Kaj; Nielsen, Hans Bruun
1990-01-01
The Huber M-estimator for robust linear regression is analyzed. Newton type methods for solution of the problem are defined and analyzed, and finite convergence is proved. Numerical experiments with a large number of test problems demonstrate efficiency and indicate that this kind of approach may...
Empirical Bayes Estimation in Regression Model
Li-chun Wang
2005-01-01
This paper considers the empirical Bayes (EB) estimation problem for the parameterβ of the linear regression model y = Xβ + ε with ε～ N(0, σ2I) givenβ. Based on Pitman closeness (PC) criterion and mean square error matrix (MSEM) criterion, we prove the superiority of the EB estimator over the ordinary least square estimator (OLSE).
Functional data analysis of generalized regression quantiles
Guo, Mengmeng
2013-11-05
Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.
Function approximation with polynomial regression slines
Principles of the polynomial regression splines as well as algorithms and programs for their computation are presented. The programs prepared using software package MATLAB are generally intended for approximation of the X-ray spectra and can be applied in the multivariate calibration of radiometric gauges. (author)
Prediction of dynamical systems by symbolic regression.
Quade, Markus; Abel, Markus; Shafi, Kamran; Niven, Robert K; Noack, Bernd R
2016-07-01
We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting an arriving front in an excitable system, and as a real-world application, the prediction of solar power production based on energy production observations at a given site together with the weather forecast. PMID:27575130
Regression Discontinuity Designs Based on Population Thresholds
Eggers, Andrew C.; Freier, Ronny; Grembi, Veronica;
In many countries, important features of municipal government (such as the electoral system, mayors' salaries, and the number of councillors) depend on whether the municipality is above or below arbitrary population thresholds. Several papers have used a regression discontinuity design (RDD) to...
Prediction of dynamical systems by symbolic regression
Quade, Markus; Abel, Markus; Shafi, Kamran; Niven, Robert K.; Noack, Bernd R.
2016-07-01
We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting an arriving front in an excitable system, and as a real-world application, the prediction of solar power production based on energy production observations at a given site together with the weather forecast.
Acute Myopericarditis Mimicking Acute Myocardial Infarction
Seval İzdeş; Neriman Defne Altıntaş; Gülin Karaaslan; Recep Uygun; Abdulkadir But
2011-01-01
Acute coronary syndromes among young adults are relatively low when compared with older population in the intensive care unit. Electrocardiographic abnormalities mimicking acute coronary syndromes may be caused by non-coronary syndromes and the differential diagnosis requires a detailed evaluation. We are reporting a case of myopericarditis presenting with acute ST elevation and elevated cardiac enzymes simulating acute coronary syndrome. In this case report, the literature is reviewed to dis...
Interpreting parameters in the logistic regression model with random effects
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...
谢萍; 许勤; 陈娟
2015-01-01
目的：调查急性胰腺炎出院患者健康行为能力的现状,分析其影响因素。方法采用一般资料评估表、健康行为能力自评量表( SRAHP)、健康意识量表、抑郁自评量表( SDS)、社会支持评定量表( SSRS)对入住某三甲医院胆胰中心的193例首发急性胰腺炎出院患者进行调查。结果急性胰腺炎出院患者健康行为能力平均得分为(64.09±15.25)分。4个维度中,运动维度得分最高,其次是心理调适、营养和健康责任。70例(36.27%)患者健康行为能力处于良好水平,118例(61.14%)处于中等水平,5例(2.59%)处于较差水平。急性胰腺炎出院患者健康意识平均得分为(7.19±3.12)分,抑郁平均得分为(49.86±8.04)分。单因素分析显示,职业、文化程度、个人月收入、医疗费用承担情况、生活方式状况、社会支持、抑郁状况与健康意识是急性胰腺炎出院患者健康行为能力的影响因素(P<0.05)。多元逐步回归分析显示,患者的健康意识、生活方式状况、社会支持状况、医疗费用承担情况与健康行为能力呈正相关(β值分别为0.99,1.00,0.56,1.01；P<0.05)；抑郁状况与健康行为能力呈负相关(β=-0.68,P<0.05)。结论急性急性胰腺炎出院患者的健康行为能力亟待提高,护理人员在制定干预措施时,要注重帮助患者建立和强化健康意识,改变其不良生活方式,提高社会支持水平,保持良好情绪,以更好地提高其健康行为能力。%Objective To conduct the research on the health behavior ability of discharged acute pancreatitis ( AP) patients, and analyze the relevant influencing factors. Methods Totals of 193 first-episode discharged AP in a third-grade class-A hospital were investigated with the General Information Evaluation Scale, Self-rated Abilities for Health Practices Scale ( SRAHP) , Health Awareness Scale, Self-rating Depression Scale ( SDS) and Social Support Rate Scale ( SSRS) . Results Among
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)
Online support vector regression for reinforcement learning
Yu Zhenhua; Cai Yuanli
2007-01-01
The goal in reinforcement learning is to learn the value of state-action pair in order to maximize the total reward. For continuous states and actions in the real world, the representation of value functions is critical. Furthermore, the samples in value functions are sequentially obtained. Therefore, an online support vector regression (OSVR) is set up, which is a function approximator to estimate value functions in reinforcement learning. OSVR updates the regression function by analyzing the possible variation of support vector sets after new samples are inserted to the training set. To evaluate the OSVR learning ability, it is applied to the mountain-car task. The simulation results indicate that the OSVR has a preferable convergence speed and can solve continuous problems that are infeasible using lookup table.
Wavelet Scattering Regression of Quantum Chemical Energies
Hirn, Matthew; Poilvert, Nicolas
2016-01-01
We introduce multiscale invariant dictionaries to estimate quantum chemical energies of organic molecules, from training databases. Molecular energies are invariant to isometric atomic displacements, and are Lipschitz continuous to molecular deformations. Similarly to density functional theory (DFT), the molecule is represented by an electronic density function. A multiscale invariant dictionary is calculated with wavelet scattering invariants. It cascades a first wavelet transform which separates scales, with a second wavelet transform which computes interactions across scales. Sparse scattering regressions give state of the art results over two databases of organic planar molecules. On these databases, the regression error is of the order of the error produced by DFT codes, but at a fraction of the computational cost.
[Caudal regression sequence: clinical-radiological case].
Zepeda T, Juan; García M, Mirna; Morales S, Jorge; Pantoja H, Miguel A; Espinoza G, Aníbal
2015-01-01
Caudal regression syndrome is an uncommon congenital malformation that includes a wide spectrum of clinical presentations. Characterised by caudal musculoskeletal compromise, it can be associated to neurological, gastrointestinal, renal and genitourinary defects. Although the specific aetiology has not been clarified, it has been associated with the presence of maternal diabetes and mutations in homeobox gene HBLX9. Its diagnosis is based on a good prenatal ultrasound detection, detailed physical examination, and post-natal imaging study using radiography and magnetic resonance. Caudal regression syndrome requires multidisciplinary management, and it seems that good metabolic control of gestational diabetes constitutes the best preventive measure available. We present the clinical case and images of a male term newborn, born to a pregestational diabetic mother with poor metabolic control and a prenatal ultrasound diagnosis of lumbar spine, iliac bones and lower limbs malformation. Born in good conditions, the diagnosis was confirmed using X-rays and magnetic resonance. PMID:26455704
Controlling attribute effect in linear regression
Calders, Toon
2013-12-01
In data mining we often have to learn from biased data, because, for instance, data comes from different batches or there was a gender or racial bias in the collection of social data. In some applications it may be necessary to explicitly control this bias in the models we learn from the data. This paper is the first to study learning linear regression models under constraints that control the biasing effect of a given attribute such as gender or batch number. We show how propensity modeling can be used for factoring out the part of the bias that can be justified by externally provided explanatory attributes. Then we analytically derive linear models that minimize squared error while controlling the bias by imposing constraints on the mean outcome or residuals of the models. Experiments with discrimination-aware crime prediction and batch effect normalization tasks show that the proposed techniques are successful in controlling attribute effects in linear regression models. © 2013 IEEE.
A Gibbs Sampler for Multivariate Linear Regression
Mantz, Adam B
2015-01-01
Kelly (2007, hereafter K07) described an efficient algorithm, using Gibbs sampling, for performing linear regression in the fairly general case where non-zero measurement errors exist for both the covariates and response variables, where these measurements may be correlated (for the same data point), where the response variable is affected by intrinsic scatter in addition to measurement error, and where the prior distribution of covariates is modeled by a flexible mixture of Gaussians rather than assumed to be uniform. Here I extend the K07 algorithm in two ways. First, the procedure is generalized to the case of multiple response variables. Second, I describe how to model the prior distribution of covariates using a Dirichlet process, which can be thought of as a Gaussian mixture where the number of mixture components is learned from the data. I present an example of multivariate regression using the extended algorithm, namely fitting scaling relations of the gas mass, temperature, and luminosity of dynamica...
Gabriele eBuruck
2014-05-01
Full Text Available Psychosocial stress affects resources for adequate coping with environmental demands. A crucial question in this context is the extent to which acute psychosocial stressors impact empathy and emotion regulation. In the present study, 120 participants were randomly assigned to a control group vs. a group confronted with the Trier Social Stress Test, an established paradigm for the induction of acute psychosocial stress. Empathy for pain as a specific subgroup of empathy was assessed via pain intensity ratings during a pain-picture task. Self-reported emotion regulation skills were measured as predictors using an established questionnaire. Stressed individuals scored significantly lower on the appraisal of pain pictures. A regression model was chosen to find variables that further predict the pain ratings. These findings implicate that acute psychosocial stress might impair empathic processes to observed pain in another person and the ability to accept one’s emotion additionally predicts the empathic reaction. Furthermore, the ability to tolerate negative emotions modulated the relation between stress and pain judgments, and thus influenced core cognitive-affective functions relevant for coping with environmental challenges. In conclusion, our study emphasizes the necessity of reducing negative emotions in terms of empathic distress when confronted with pain of another person under psychosocial stress, in order to be able to retain pro-social behavior.
Buruck, Gabriele; Wendsche, Johannes; Melzer, Marlen; Strobel, Alexander; Dörfel, Denise
2014-01-01
Psychosocial stress affects resources for adequate coping with environmental demands. A crucial question in this context is the extent to which acute psychosocial stressors impact empathy and emotion regulation. In the present study, 120 participants were randomly assigned to a control group vs. a group confronted with the Trier Social Stress Test (TSST), an established paradigm for the induction of acute psychosocial stress. Empathy for pain as a specific subgroup of empathy was assessed via pain intensity ratings during a pain-picture task. Self-reported emotion regulation skills were measured as predictors using an established questionnaire. Stressed individuals scored significantly lower on the appraisal of pain pictures. A regression model was chosen to find variables that further predict the pain ratings. These findings implicate that acute psychosocial stress might impair empathic processes to observed pain in another person and the ability to accept one's emotion additionally predicts the empathic reaction. Furthermore, the ability to tolerate negative emotions modulated the relation between stress and pain judgments, and thus influenced core cognitive-affective functions relevant for coping with environmental challenges. In conclusion, our study emphasizes the necessity of reducing negative emotions in terms of empathic distress when confronted with pain of another person under psychosocial stress, in order to be able to retain pro-social behavior. PMID:24910626
Quantile regression modeling for Malaysian automobile insurance premium data
Fuzi, Mohd Fadzli Mohd; Ismail, Noriszura; Jemain, Abd Aziz
2015-09-01
Quantile regression is a robust regression to outliers compared to mean regression models. Traditional mean regression models like Generalized Linear Model (GLM) are not able to capture the entire distribution of premium data. In this paper we demonstrate how a quantile regression approach can be used to model net premium data to study the effects of change in the estimates of regression parameters (rating classes) on the magnitude of response variable (pure premium). We then compare the results of quantile regression model with Gamma regression model. The results from quantile regression show that some rating classes increase as quantile increases and some decrease with decreasing quantile. Further, we found that the confidence interval of median regression (τ = O.5) is always smaller than Gamma regression in all risk factors.
Bae, Gihyun; Huh, Hoon; Park, Sungho
This paper deals with a regression model for light weight and crashworthiness enhancement design of automotive parts in frontal car crash. The ULSAB-AVC model is employed for the crash analysis and effective parts are selected based on the amount of energy absorption during the crash behavior. Finite element analyses are carried out for designated design cases in order to investigate the crashworthiness and weight according to the material and thickness of main energy absorption parts. Based on simulations results, a regression analysis is performed to construct a regression model utilized for light weight and crashworthiness enhancement design of automotive parts. An example for weight reduction of main energy absorption parts demonstrates the validity of a regression model constructed.
Acute pollution of recipients in urban areas
Rauch, W.; Harremoës, P.
1997-01-01
Oxygen and ammonia concentration are key parameters of acute water pollution in urban rivers. These two abiotic parameters are statistically assessed for a historical rain series by means of a simplified deterministic model of the integrated drainage system. Continuous simulation of the system...... performance indicates that acute water pollution is caused by intermittent discharges from both sewer system and wastewater treatment plant. Neglecting one of them in the evaluation of the environmental impact gives a wrong impression of total system behavior. Detention basins and alternative operational...... modes in the treatment plant under wet weather loading have a limited positive effect for minimizing acute water pollution. (C) 1997 IAWQ. Published by Elsevier Science Ltd....
Robust logistic regression for insurance risk classification
Garrido, José; Flores, Esteban
2001-01-01
Risk classification is an important part of the actuarial process in Insurance companies. It allows for the underwriting of the best risks, through an appropriate choice of classification variables, and helps set fair premiums in rate-making. Logistic regression is one of the sophisticated statistical methods used by the banking industry to select credit rating variables. Extending the method to insurance risk classification seems natural. But Insurance risks are usually classified in a large...
Multinomial Inverse Regression for Text Analysis
Taddy, Matt
2010-01-01
Text data, including speeches, stories, and other document forms, are often connected to sentiment variables that are of interest for research in marketing, economics, and elsewhere. It is also very high dimensional and difficult to incorporate into statistical analyses. This article introduces a straightforward framework of sentiment-preserving dimension reduction for text data. Multinomial inverse regression is introduced as a general tool for simplifying predictor sets that can be represen...
Directional quantile regression in Octave (and MATLAB)
Boček, Pavel; Šiman, Miroslav
2016-01-01
Roč. 52, č. 1 (2016), s. 28-51. ISSN 0023-5954 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : quantile regression * multivariate quantile * depth contour * Matlab Subject RIV: IN - Informatics, Computer Science Impact factor: 0.541, year: 2014 http://library.utia.cas.cz/separaty/2016/SI/bocek-0458380.pdf
Multiple Imputations for LInear Regression Models
Brownstone, David
1991-01-01
Rubin (1987) has proposed multiple imputations as a general method for estimation in the presence of missing data. Rubinâ€™s results only strictly apply to Bayesian models, but Schenker and Welsh (1988) directly prove the consistency Â multiple imputations inference~ when there are missing values of the dependent variable in linear regression models. This paper extends and modifies Schenker and Welshâ€™s theorems to give conditions where multiple imputations yield consistent inferences for bo...
Identification of regression models - application in traffic
Dohnal, Pavel
Ljubljana : Jozef Stefan Institute, 2005, s. 1-5. [International PhD Workshop on Systems and Control a Young Generation Viewpoint /6./. Izola (SI), 04.10.2005-08.10.2005] R&D Projects: GA MŠk(CZ) 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : regression model * model order * intensity of traffic flow * prediction Subject RIV: BC - Control Systems Theory
Average Regression-Adjusted Controlled Regenerative Estimates
Lewis, Peter A.W.; Ressler, Richard
1991-01-01
Proceedings of the 1991 Winter Simulation Conference Barry L. Nelson, W. David Kelton, Gordon M. Clark (eds.) One often uses computer simulations of queueing systems to generate estimates of system characteristics along with estimates of their precision. Obtaining precise estimates, espescially for high traffic intensities, can require large amounts of computer time. Average regression-adjusted controlled regenerative estimates result from combining the two techniques ...
Conjoined legs: Sirenomelia or caudal regression syndrome?
Sakti Prasad Das; Niranjan Ojha; G Shankar Ganesh; Ram Narayan Mohanty
2013-01-01
Presence of single umbilical persistent vitelline artery distinguishes sirenomelia from caudal regression syndrome. We report a case of a12-year-old boy who had bilateral umbilical arteries presented with fusion of both legs in the lower one third of leg. Both feet were rudimentary. The right foot had a valgus rocker-bottom deformity. All toes were present but rudimentary. The left foot showed absence of all toes. Physical examination showed left tibia vara. The chest evaluation in sitting re...
Sirenomelia and severe caudal regression syndrome
Seidahmed, Mohammed Z.; Abdelbasit, Omer B.; Alhussein, Khalid A.; Miqdad, Abeer M.; Khalil, Mohammed I.; Salih, Mustafa A.
2014-01-01
Objective: To describe cases of sirenomelia and severe caudal regression syndrome (CRS), to report the prevalence of sirenomelia, and compare our findings with the literature. Methods: Retrospective data was retrieved from the medical records of infants with the diagnosis of sirenomelia and CRS and their mothers from 1989 to 2010 (22 years) at the Security Forces Hospital, Riyadh, Saudi Arabia. A perinatologist, neonatologist, pediatric neurologist, and radiologist ascertained the diagnoses. ...
New developments in Sparse PLS regression
Magnanensi, Jérémy; Maumy-Bertrand, Myriam; Meyer, Nicolas; Bertrand, Frédéric
2016-01-01
Methods based on partial least squares (PLS) regression, which has recently gained much attention in the analysis of high-dimensional genomic datasets, have been developed since the early 2000s for performing variable selection. Most of these techniques rely on tuning parameters that are often determined by cross-validation (CV) based methods, which raises important stability issues. To overcome this, we have developed a new dynamic bootstrapbased method for significant predictor selection, s...
Expectation-maximization for logistic regression
Scott, James G.; Sun, Liang
2013-01-01
We present a family of expectation-maximization (EM) algorithms for binary and negative-binomial logistic regression, drawing a sharp connection with the variational-Bayes algorithm of Jaakkola and Jordan (2000). Indeed, our results allow a version of this variational-Bayes approach to be re-interpreted as a true EM algorithm. We study several interesting features of the algorithm, and of this previously unrecognized connection with variational Bayes. We also generalize the approach to sparsi...
Time series regression studies in environmental epidemiology
Bhaskaran, Krishnan; Gasparrini, Antonio; Hajat, Shakoor; Smeeth, Liam; Armstrong, Ben
2013-01-01
Time series regression studies have been widely used in environmental epidemiology, notably in investigating the short-term associations between exposures such as air pollution, weather variables or pollen, and health outcomes such as mortality, myocardial infarction or disease-specific hospital admissions. Typically, for both exposure and outcome, data are available at regular time intervals (e.g. daily pollution levels and daily mortality counts) and the aim is to explore short-term associa...
Specification Testing for Nonlinear Multivariate Cointegrating Regressions
Chaohua Dong; Jiti Gao; Dag Tjostheim; Jiying Yin
2014-01-01
This paper considers a general model specification test for nonlinear multivariate cointegrating regressions where the regressor consists of a univariate integrated time series and a vector of stationary time series. The regressors and the errors are generated from the same innovations, so that the model accommodates endogeniety. A new and simple test is proposed and the resulting asymptotic theory is established. The test statistic is constructed based on a natural distance function between ...
Pricing Single Malt Whisky : A Regression Analysis
Bjartmar Hylta, Sanna; Lundquist, Emma
2016-01-01
This thesis examines the factors that affect the price of whisky. Multiple regression analysis is used to model the relationship between the identified covariates that are believed to impact the price of whisky. The optimal marketing strategy for whisky producers in the regions Islay and Campbeltown are discussed. This analysis is based on the Marketing Mix. Furthermore, a Porter’s five forces analysis, focusing on the regions Campeltown and Islay, is examined. Finally the findings are summar...
In utero diagnosis of caudal regression syndrome
Lindsey M. Negrete, BS
2015-01-01
Full Text Available We present a case of caudal regression syndrome (CRS, a relatively uncommon defect of the lower spine accompanied by a wide range of developmental abnormalities. CRS is closely associated with pregestational diabetes and is nearly 200 times more prevalent in infants of diabetic mothers (1, 2. We report a case of prenatally suspected CRS in a fetus of a nondiabetic mother and discuss how the initial neurological abnormalities found on imaging correlate with the postnatal clinical deficits.
Supporting Regularized Logistic Regression Privately and Efficiently
Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei
2015-01-01
As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Increasing concerns over data privacy make it more and more difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joi...
Nonexistence in Reciprocal and Logarithmic Regression
Josef Bukac
2003-01-01
Fitting logarithmic b ln(clx), a+bln(c+x) or reciprocal b/(c+x), a+b/(c+x) regression models to data by the least squares method asks for the determination of the closure of the set of each type of these functions defined on a finite domain. It follows that a minimal solution may not exist. But it does exist when the closure is considered.
Logistic regression a self-learning text
Kleinbaum, David G
1994-01-01
This textbook provides students and professionals in the health sciences with a presentation of the use of logistic regression in research. The text is self-contained, and designed to be used both in class or as a tool for self-study. It arises from the author's many years of experience teaching this material and the notes on which it is based have been extensively used throughout the world.
Regression of Labrador keratopathy following cataract extraction.
Dahan, E; Judelson, J; Welsh, N H
1986-01-01
Labrador keratopathy (LK) is an acquired corneal degeneration thought to be caused by chronic exposure to solar irradiation. Reports so far suggest that it is a progressive or at least a stationary condition. There are no detailed reports on recommended therapy. A prospective clinical study was conducted to show regression of LK following extracapsular cataract extraction. Seventeen black patients (26 eyes) with LK and mature cataracts underwent extracapsular cataract extraction. The severity...
Confidence Corridors for Multivariate Generalized Quantile Regression
Chao, Shih-Kang; Proksch, Katharina; Dette, Holger; Härdle, Wolfgang
2014-01-01
We focus on the construction of confidence corridors for multivariate nonparametric generalized quantile regression functions. This construction is based on asymptotic results for the maximal deviation between a suitable nonparametric estimator and the true function of interest which follow after a series of approximation steps including a Bahadur representation, a new strong approximation theorem and exponential tail inequalities for Gaussian random fields. As a byproduct we also obtain conf...
Estimation of a semiparametric contaminated regression model
Vandekerkhove, Pierre
2011-01-01
We consider in this paper a contamined regression model where the distribution of the contaminating component is known when the Eu- clidean parameters of the regression model, the noise distribution, the contamination ratio and the distribution of the design data are un- known. Our model is said to be semiparametric in the sense that the probability density function (pdf) of the noise involved in the regression model is not supposed to belong to a parametric density family. When the pdf's of the noise and the contaminating phenomenon are supposed to be symmetric about zero, we propose an estimator of the various (Eu- clidean and functionnal) parameters of the model, and prove under mild conditions its convergence. We prove in particular that, under technical conditions all satisfied in the Gaussian case, the Euclidean part of the model is estimated at the rate $o_{a.s}(n-1/4+\\gamma), $\\gamma> 0$. We recall that, as it is pointed out in Bordes and Vandekerkhove (2010), this result cannot be ignored to go furth...
Spontaneous Regression of a Cervical Disk Herniation
Emre Delen
2014-03-01
Full Text Available A 54 years old female patient was admitted to our outpatient clinic with a two months history of muscle spasms of her neck and pain radiating to the left upper extremity. Magnetic resonance imaging had shown a large left-sided paracentral disk herniation at the C6-C7 disk space (Figure 1. Neurological examination showed no obvious neurological deficit. She received conservative treatment including bed rest, rehabilitation, and analgesic drugs. After 13 months, requested by the patient, a second magnetic resonance imaging study showed resolution of the disc herniation.(Figure 2 Although the literature contains several reports about spontaneous regression of herniated lumbar disc without surgical intervention, that of phenomenon reported for herniated cervical level is rare, and such reports are few[1]. In conclusion, herniated intervertebral disc have the potential to spontaneously regress independently from the spine level. With further studies, determining the predictive signs for prognostic evaluation for spontaneous regression which would yield to conservative treatment would be beneficial.
Time series regression studies in environmental epidemiology.
Bhaskaran, Krishnan; Gasparrini, Antonio; Hajat, Shakoor; Smeeth, Liam; Armstrong, Ben
2013-08-01
Time series regression studies have been widely used in environmental epidemiology, notably in investigating the short-term associations between exposures such as air pollution, weather variables or pollen, and health outcomes such as mortality, myocardial infarction or disease-specific hospital admissions. Typically, for both exposure and outcome, data are available at regular time intervals (e.g. daily pollution levels and daily mortality counts) and the aim is to explore short-term associations between them. In this article, we describe the general features of time series data, and we outline the analysis process, beginning with descriptive analysis, then focusing on issues in time series regression that differ from other regression methods: modelling short-term fluctuations in the presence of seasonal and long-term patterns, dealing with time varying confounding factors and modelling delayed ('lagged') associations between exposure and outcome. We finish with advice on model checking and sensitivity analysis, and some common extensions to the basic model. PMID:23760528
General regression and representation model for classification.
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.
Regression of pituitary macroadenoma after intratumoral hemorrhage
Pituitary apoplexy syndrome is rarely recognized, whereas it can be the first sign of a nonfunctioning adenoma. It is caused by degenerative changes of vascular origin, necrotic and/or hemorrhagic, which can involve a normal gland or a pituitary tumor. It can be found in 0.6% to 9% of all pituitary tumors. Regression of the tumor mass can occasionally occur in patients after pituitary apoplexy. The authors present a case of pituitary macroadenoma regression as the result of hemorrhage into the tumor in a patient with clinical signs of pituitary apoplexy. Three MR examinations (at intervals of three and nine months) revealed the evolution of a hemorrhage within the tumor as well as an evident decrease in adenoma size. Because of the complete regression of clinical symptoms and hormonal inactivity of the tumor, the previously planned surgery was not performed. It is important to remember the possibility of pituitary apoplexy, also in nonfunctioning adenomas. Conservative treatment should be considered when the clinical symptoms of pituitary apoplexy resolve in a patient with a nonfunctioning adenoma. (author)
Bayesian Inference of a Multivariate Regression Model
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.
On spline approximation of sliced inverse regression
2007-01-01
The dimension reduction is helpful and often necessary in exploring the nonparametric regression structure.In this area,Sliced inverse regression (SIR) is a promising tool to estimate the central dimension reduction (CDR) space.To estimate the kernel matrix of the SIR,we herein suggest the spline approximation using the least squares regression.The heteroscedasticity can be incorporated well by introducing an appropriate weight function.The root-n asymptotic normality can be achieved for a wide range choice of knots.This is essentially analogous to the kernel estimation.Moreover, we also propose a modified Bayes information criterion (BIC) based on the eigenvalues of the SIR matrix.This modified BIC can be applied to any form of the SIR and other related methods.The methodology and some of the practical issues are illustrated through the horse mussel data.Empirical studies evidence the performance of our proposed spline approximation by comparison of the existing estimators.
Modeling oil production based on symbolic regression
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
Kepler AutoRegressive Planet Search: Motivation & Methodology
Caceres, Gabriel; Feigelson, Eric; Jogesh Babu, G.; Bahamonde, Natalia; Bertin, Karine; Christen, Alejandra; Curé, Michel; Meza, Cristian
2015-08-01
The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Auto-Regressive Moving-Average (ARMA) models, Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), and related models are flexible, phenomenological methods used with great success to model stochastic temporal behaviors in many fields of study, particularly econometrics. Powerful statistical methods are implemented in the public statistical software environment R and its many packages. Modeling involves maximum likelihood fitting, model selection, and residual analysis. These techniques provide a useful framework to model stellar variability and are used in KARPS with the objective of reducing stellar noise to enhance opportunities to find as-yet-undiscovered planets. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; ARMA-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. We apply the procedures to simulated Kepler-like time series with known stellar and planetary signals to evaluate the effectiveness of the KARPS procedures. The ARMA-type modeling is effective at reducing stellar noise, but also reduces and transforms the transit signal into ingress/egress spikes. A periodogram based on the TCF is constructed to concentrate the signal
Fitting a linear regression model by combining least squares and least absolute value estimation
Allende, Sira; Bouza, Carlos; Romero, Isidro
1995-01-01
Robust estimation of the multiple regression is modeled by using a convex combination of Least Squares and Least Absolute Value criterions. A Bicriterion Parametric algorithm is developed for computing the corresponding estimates. The proposed procedure should be specially useful when outliers are expected. Its behavior is analyzed using some examples.
The book first presents the anatomy and physiology of the abdomen and continues with chapters discussing clinical and laboratory aspects and a suitable order of diagnostic examinations with reference to the acute processes, explaining the diagnostic tools: ultrasonography, radiography including angiography and CT, tapping techniques and endoscopy together with their basic principles, examination techniques, and diagnosis. One chapter presents a complete survey of the processes involving the entire abdomen - as e.g. peritonitis, ileus, abdominal trauma, intraperitoneal hemorrage. This chapter profoundly discusses the diagnostics and therapies including emergency measures and surgery. Problems requiring consultation among varous specialists, in internal medicine, gynecology, urology, or pediatrics, are discussed in great detail. Information for the anesthetist is given for cases of emergency. More than one third of the book is devoted to organ-specific information, dicussing the pathogenesis, diagnostics, and therapy of the oesophagus, stomach, large and small intestine, bile ducts, pankreas, liver, spleen, and the abdominal vessels and the abdominal wall. (orig.) With 153 figs., 90 tabs
A prospective study was performed on the relationship of CT findings to the clinical course of 148 patients with acute pancreatitis. The type of pancreatic inflammation seen on CT was classified into six categories based on an overall assessment of size, contour and density of the gland, and peripancreatic abnormalities. The majority (94%) of patients in whom CT showed mild pancreatic changes (grades A, B and C) had two or less positive clinical indicaters of severe pancreatitis (Ranson's signs). In contrast, 92% of patients in whom CT showed more severe changes of pancreatitis (grades D, E or F) had three or more positive signs. The nine patients who died with pancreatitis-related complications were in grades D, E or F. We wish to draw attention to a CT appearance which we have called 'fat islets' (low density intrapancreatic or peripancreatic areas, the contents of which approach fat in attenuation values); there was a strong correlation between this appearance and subsequent infection. (author). 24 refs.; 7 figs.; 4 tabs
General bound of overfitting for MLP regression models
Rynkiewicz, Joseph
2012-01-01
Multilayer perceptrons (MLP) with one hidden layer have been used for a long time to deal with non-linear regression. However, in some task, MLP's are too powerful models and a small mean square error (MSE) may be more due to overfitting than to actual modelling. If the noise of the regression model is Gaussian, the overfitting of the model is totally determined by the behavior of the likelihood ratio test statistic (LRTS), however in numerous cases the assumption of normality of the noise is arbitrary if not false. In this paper, we present an universal bound for the overfitting of such model under weak assumptions, this bound is valid without Gaussian or identifiability assumptions. The main application of this bound is to give a hint about determining the true architecture of the MLP model when the number of data goes to infinite. As an illustration, we use this theoretical result to propose and compare effective criteria to find the true architecture of an MLP.
On relationship between regression models and interpretation of multiple regression coefficients
A N Varaksin; Panov, V. G.
2012-01-01
In this paper, we consider the problem of treating linear regression equation coefficients in the case of correlated predictors. It is shown that in general there are no natural ways of interpreting these coefficients similar to the case of single predictor. Nevertheless we suggest linear transformations of predictors, reducing multiple regression to a simple one and retaining the coefficient at variable of interest. The new variable can be treated as the part of the old variable that has no ...
Bry, Xavier; Verron, Thomas; Cazes, Pierre
2008-01-01
A variable group Y is assumed to depend upon R thematic variable groups X 1, >..., X R . We assume that components in Y depend linearly upon components in the Xr's. In this work, we propose a multiple covariance criterion which extends that of PLS regression to this multiple predictor groups situation. On this criterion, we build a PLS-type exploratory method - Structural Equation Exploratory Regression (SEER) - that allows to simultaneously perform dimension reduction in groups and investiga...
TWO-STAGE QUANTILE REGRESSION WHEN THE FIRST STAGE IS BASED ON QUANTILE REGRESSION
Christophe Muller; Tae-Hwan Kim
2004-01-01
We present the asymptotic properties of double-stage quantile regression estimators with random regressors, where the first stage is based on quantile regressions with the same quantile as in the second stage, which ensures robustness of the estimation procedure. We derive invariance properties with respect to the reformulation of the dependent variable. We propose a consistent estimator of the variance-covariance matrix of the new estimator. Finally, we investigate finite sample properties o...
A test of significance in functional quadratic regression
Horvath, Lajos
2011-01-01
We consider a quadratic functional regression model in which a scalar response depends on a functional predictor; the common functional linear model is a special case. We wish to test the significance of the nonlinear term in the model. We develop a testing method which is based on projecting the observations onto a suitably chosen finite dimensional space using functional principal component analysis. The asymptotic behavior of our testing procedure is established. A simulation study shows that the testing procedure has good size and power with finite sample sizes. We then apply our test to a data set provided by Tecator, which consists of near-infrared absorbance spectra and fat content of meat.
Dynamic Regression Intervention Modeling for the Malaysian Daily Load
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.
Hierarchical linear regression models for conditional quantiles
TIAN; Maozai
2006-01-01
The quantile regression has several useful features and therefore is gradually developing into a comprehensive approach to the statistical analysis of linear and nonlinear response models,but it cannot deal effectively with the data with a hierarchical structure.In practice,the existence of such data hierarchies is neither accidental nor ignorable,it is a common phenomenon.To ignore this hierarchical data structure risks overlooking the importance of group effects,and may also render many of the traditional statistical analysis techniques used for studying data relationships invalid.On the other hand,the hierarchical models take a hierarchical data structure into account and have also many applications in statistics,ranging from overdispersion to constructing min-max estimators.However,the hierarchical models are virtually the mean regression,therefore,they cannot be used to characterize the entire conditional distribution of a dependent variable given high-dimensional covariates.Furthermore,the estimated coefficient vector (marginal effects)is sensitive to an outlier observation on the dependent variable.In this article,a new approach,which is based on the Gauss-Seidel iteration and taking a full advantage of the quantile regression and hierarchical models,is developed.On the theoretical front,we also consider the asymptotic properties of the new method,obtaining the simple conditions for an n1/2-convergence and an asymptotic normality.We also illustrate the use of the technique with the real educational data which is hierarchical and how the results can be explained.
Multiple linear regression for isotopic measurements
Garcia Alonso, J. I.
2012-04-01
There are two typical applications of isotopic measurements: the detection of natural variations in isotopic systems and the detection man-made variations using enriched isotopes as indicators. For both type of measurements accurate and precise isotope ratio measurements are required. For the so-called non-traditional stable isotopes, multicollector ICP-MS instruments are usually applied. In many cases, chemical separation procedures are required before accurate isotope measurements can be performed. The off-line separation of Rb and Sr or Nd and Sm is the classical procedure employed to eliminate isobaric interferences before multicollector ICP-MS measurement of Sr and Nd isotope ratios. Also, this procedure allows matrix separation for precise and accurate Sr and Nd isotope ratios to be obtained. In our laboratory we have evaluated the separation of Rb-Sr and Nd-Sm isobars by liquid chromatography and on-line multicollector ICP-MS detection. The combination of this chromatographic procedure with multiple linear regression of the raw chromatographic data resulted in Sr and Nd isotope ratios with precisions and accuracies typical of off-line sample preparation procedures. On the other hand, methods for the labelling of individual organisms (such as a given plant, fish or animal) are required for population studies. We have developed a dual isotope labelling procedure which can be unique for a given individual, can be inherited in living organisms and it is stable. The detection of the isotopic signature is based also on multiple linear regression. The labelling of fish and its detection in otoliths by Laser Ablation ICP-MS will be discussed using trout and salmon as examples. As a conclusion, isotope measurement procedures based on multiple linear regression can be a viable alternative in multicollector ICP-MS measurements.
Monthly streamflow forecasting using Gaussian Process Regression
Sun, Alexander Y.; Wang, Dingbao; Xu, Xianli
2014-04-01
Streamflow forecasting plays a critical role in nearly all aspects of water resources planning and management. In this work, Gaussian Process Regression (GPR), an effective kernel-based machine learning algorithm, is applied to probabilistic streamflow forecasting. GPR is built on Gaussian process, which is a stochastic process that generalizes multivariate Gaussian distribution to infinite-dimensional space such that distributions over function values can be defined. The GPR algorithm provides a tractable and flexible hierarchical Bayesian framework for inferring the posterior distribution of streamflows. The prediction skill of the algorithm is tested for one-month-ahead prediction using the MOPEX database, which includes long-term hydrometeorological time series collected from 438 basins across the U.S. from 1948 to 2003. Comparisons with linear regression and artificial neural network models indicate that GPR outperforms both regression methods in most cases. The GPR prediction of MOPEX basins is further examined using the Budyko framework, which helps to reveal the close relationships among water-energy partitions, hydrologic similarity, and predictability. Flow regime modification and the resulting loss of predictability have been a major concern in recent years because of climate change and anthropogenic activities. The persistence of streamflow predictability is thus examined by extending the original MOPEX data records to 2012. Results indicate relatively strong persistence of streamflow predictability in the extended period, although the low-predictability basins tend to show more variations. Because many low-predictability basins are located in regions experiencing fast growth of human activities, the significance of sustainable development and water resources management can be even greater for those regions.
Prediction of Rainfall Using Logistic Regression
A.H.M. Rahmatullah Imon
2012-07-01
Full Text Available Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} The use of logistic regression modeling has exploded during the past decade for prediction and forecasting. From its original acceptance in epidemiologic research, the method is now commonly employed in almost all branches of knowledge. Rainfall is one of the most important phenomena of climate system. It is well known that the variability and intensity of rainfall act on natural, agricultural, human and even total biological system. So it is essential to be able to predict rainfall by finding out the appropriate predictors. In this paper an attempt has been made to use logistic regression for predicting rainfall. It is evident that the climatic data are often subjected to gross recording errors though this problem often goes unnoticed to the analysts. In this paper we have used very recent screening methods to check and correct the climatic data that we use in our study. We have used fourteen years’ daily rainfall data to formulate our model. Then we use two years’ observed daily rainfall data treating them as future data for the cross validation of our model. Our findings clearly show that if we are able to choose appropriate predictors for rainfall, logistic regression model can predict the rainfall very efficiently.
Regression models for expected length of stay.
Grand, Mia Klinten; Putter, Hein
2016-03-30
In multi-state models, the expected length of stay (ELOS) in a state is not a straightforward object to relate to covariates, and the traditional approach has instead been to construct regression models for the transition intensities and calculate ELOS from these. The disadvantage of this approach is that the effect of covariates on the intensities is not easily translated into the effect on ELOS, and it typically relies on the Markov assumption. We propose to use pseudo-observations to construct regression models for ELOS, thereby allowing a direct interpretation of covariate effects while at the same time avoiding the Markov assumption. For this approach, all we need is a non-parametric consistent estimator for ELOS. For every subject (and for every state of interest), a pseudo-observation is constructed, and they are then used as outcome variables in the regression model. We furthermore show how to construct longitudinal (pseudo-) data when combining the concept of pseudo-observations with landmarking. In doing so, covariates are allowed to be time-varying, and we can investigate potential time-varying effects of the covariates. The models can be fitted using generalized estimating equations, and dependence between observations on the same subject is handled by applying the sandwich estimator. The method is illustrated using data from the US Health and Retirement Study where the impact of socio-economic factors on ELOS in health and disability is explored. Finally, we investigate the performance of our approach under different degrees of left-truncation, non-Markovianity, and right-censoring by means of simulation. PMID:26497637
Affine Projection Algorithm Using Regressive Estimated Error
Zhang, Shu; Zhi, Yongfeng
2011-01-01
An affine projection algorithm using regressive estimated error (APA-REE) is presented in this paper. By redefining the iterated error of the affine projection algorithm (APA), a new algorithm is obtained, and it improves the adaptive filtering convergence rate. We analyze the iterated error signal and the stability for the APA-REE algorithm. The steady-state weights of the APA-REE algorithm are proved to be unbiased and consist. The simulation results show that the proposed algorithm has a f...
Convex Regression with Interpretable Sharp Partitions
Petersen, Ashley; Simon, Noah; Witten, Daniela
2016-01-01
We consider the problem of predicting an outcome variable on the basis of a small number of covariates, using an interpretable yet non-additive model. We propose convex regression with interpretable sharp partitions (CRISP) for this task. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. We explore the properties of CRISP, and evaluate its performance in a simulation study and on a housing price data set.
Fixed kernel regression for voltammogram feature extraction
Cyclic voltammetry is an electroanalytical technique for obtaining information about substances under analysis without the need for complex flow systems. However, classifying the information in voltammograms obtained using this technique is difficult. In this paper, we propose the use of fixed kernel regression as a method for extracting features from these voltammograms, reducing the information to a few coefficients. The proposed approach has been applied to a wine classification problem with accuracy rates of over 98%. Although the method is described here for extracting voltammogram information, it can be used for other types of signals
Survival Analysis with Multivariate adaptive Regression Splines
Kriner, Monika
2007-01-01
Multivariate adaptive regression splines (MARS) are a useful tool to identify linear and nonlinear eﬀects and interactions between two covariates. In this dissertation a new proposal to model survival type data with MARS is introduced. Martingale and deviance residuals of a Cox PH model are used as response in a common MARS approach to model functional forms of covariate eﬀects as well as possible interactions in a data-driven way. Simulation studies prove that the new method yields a bett...
Novel Time Aware Regression Testing Technique
Harsh Bhasin
2013-05-01
Full Text Available Regression testing comes into play when changes are made in the software. It is not possible to re-run all the previous test cases therefore, a minimization technique is required in order to reduce thetest case suit. The present work proposes a time aware minimization technique to accomplish the task. The technique is verified by taking 3 KLOC professional management system developed by Sahib Soft. The proposed work takes into consideration the shortcomings in the existing techniques and presents a theoretically sound model to handle the anomalies of the existing techniques. The initial results obtained are encouraging.
Three Contributions to Robust Regression Diagnostics
Kalina, Jan
2015-01-01
Roč. 11, č. 2 (2015), s. 69-78. ISSN 1336-9180 Grant ostatní: GA ČR(CZ) GA13-01930S; Nadační fond na podporu vědy(CZ) Neuron Institutional support: RVO:67985807 Keywords : robust regression * robust econometrics * hypothesis testing Subject RIV: BA - General Mathematics http://www.degruyter.com/view/j/jamsi.2015.11.issue-2/jamsi-2015-0013/jamsi-2015-0013.xml?format=INT
Fuzzy Logic and Piecewise-Linear Regression
Fröhlich, J.; Holeňa, Martin
Košice: Prírodovedecká fakulta, Univerzita P.J. Šafárika, 2008 - (Vojtáš, P.), s. 35-38 ISBN 978-80-969184-8-5. [ITAT 2008. Conference on Theory and Practice of Information Theory . Hrebienok (SK), 22.09.2008-26.09.2008] R&D Projects: GA ČR GA201/08/0802; GA ČR GEICC/08/E018 Institutional research plan: CEZ:AV0Z10300504 Keywords : Lukasiewicz propositional logic * piecewise-linear regression Subject RIV: IN - Informatics, Computer Science
Paraneoplastic pemphigus regression after thymoma resection
Stergiou Eleni
2008-08-01
Full Text Available Abstract Background Among human neoplasms thymomas are associated with highest frequency with paraneoplastic autoimmune diseases. Case presentation A case of a 42-year-old woman with paraneoplastic pemphigus as the first manifestation of thymoma is reported. Transsternal complete thymoma resection achieved pemphigus regression. The clinical correlations between pemphigus and thymoma are presented. Conclusion Our case report provides further evidence for the important role of autoantibodies in the pathogenesis of paraneoplastic skin diseases in thymoma patients. It also documents the improvement of the associated pemphigus after radical treatment of the thymoma.
Inferring gene regression networks with model trees
Aguilar-Ruiz Jesus S
2010-10-01
Full Text Available Abstract Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear
Conformal sets in neural network regression
Demut, R.; Holeňa, Martin
Seňa: PONT s.r.o., 2012 - (Horváth, T.), s. 17-24 ISBN 978-80-971144-0-4. [ITAT 2012. Conference on Theory and Practice of Information Technologies. Ždiar (SK), 17.09.2012-21.09.2012] R&D Projects: GA ČR GA201/08/0802 Grant ostatní: GA CTU(CZ) SGS12/157/OHK4/2T/14 Institutional support: RVO:67985807 Keywords : nonlinear regression * artificial neural networks * confidence intervals * transductive inference * conformal sets Subject RIV: IN - Informatics, Computer Science
An operational GLS model for hydrologic regression
Tasker, Gary D.; Stedinger, J.R.
1989-01-01
Recent Monte Carlo studies have documented the value of generalized least squares (GLS) procedures to estimate empirical relationships between streamflow statistics and physiographic basin characteristics. This paper presents a number of extensions of the GLS method that deal with realities and complexities of regional hydrologic data sets that were not addressed in the simulation studies. These extensions include: (1) a more realistic model of the underlying model errors; (2) smoothed estimates of cross correlation of flows; (3) procedures for including historical flow data; (4) diagnostic statistics describing leverage and influence for GLS regression; and (5) the formulation of a mathematical program for evaluating future gaging activities. ?? 1989.
Acute chylous peritonitis due to acute pancreatitis
2012-01-01
We report a case of acute chylous ascites formation presenting as peritonitis (acute chylous peritonitis) in a patient suffering from acute pancreatitis due to hypertriglyceridemia and alcohol abuse. The development of chylous ascites is usually a chronic process mostly involving malignancy, trauma or surgery, and symptoms arise as a result of progressive abdominal distention. However, when accumulation of “chyle” occurs rapidly, the patient may present with signs of peritonitis. Preoperative...
Acute alcohol consumption, alcohol outlets, and gun suicide.
Branas, Charles C; Richmond, Therese S; Ten Have, Thomas R; Wiebe, Douglas J
2011-01-01
A case-control study of 149 intentionally self-inflicted gun injury cases (including completed gun suicides) and 302 population-based controls was conducted from 2003 to 2006 in a major US city. Two focal independent variables, acute alcohol consumption and alcohol outlet availability, were measured. Conditional logistic regression was adjusted for confounding variables. Gun suicide risk to individuals in areas of high alcohol outlet availability was less than the gun suicide risk they incurred from acute alcohol consumption, especially to excess. This corroborates prior work but also uncovers new information about the relationships between acute alcohol consumption, alcohol outlets, and gun suicide. Study limitations and implications are discussed. PMID:21929327
Use of Pollutant Load Regression Models with Various Sampling Frequencies for Annual Load Estimation
Youn Shik Park
2014-06-01
Full Text Available Water quality data are collected by various sampling frequencies, and the data may not be collected at a high frequency nor over the range of streamflow conditions. Therefore, regression models are used to estimate pollutant data for days on which water quality data were not measured. Pollutant load regression models were evaluated with six sampling frequencies for daily nitrogen, phosphorus, and sediment data. Annual pollutant load estimates exhibited various behaviors by sampling frequency and also by the regression model used. Several distinct sampling frequency features were observed in the study. The first was that more frequent sampling did not necessarily lead to more accurate and precise annual pollutant load estimates. The second was that use of water quality data collected from storm events improved both accuracy and precision in annual pollutant load estimates for all water quality parameters. The third was that the pollutant regression model automatically selected by LOADEST did not necessarily lead to more accurate and precise annual pollutant load estimates. The fourth was that pollutant regression models displayed different behaviors for different water quality parameters in annual pollutant load estimation.
Supporting Regularized Logistic Regression Privately and Efficiently.
Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei
2016-01-01
As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc. PMID:27271738
Bayesian nonlinear regression for large small problems
Chakraborty, Sounak
2012-07-01
Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. This is known as large p small n problem. Furthermore, the problem is more complicated when we have multiple correlated responses. We develop multivariate nonlinear regression models in this setup for accurate prediction. In this paper, we introduce a full Bayesian support vector regression model with Vapnik\\'s ε-insensitive loss function, based on reproducing kernel Hilbert spaces (RKHS) under the multivariate correlated response setup. This provides a full probabilistic description of support vector machine (SVM) rather than an algorithm for fitting purposes. We have also introduced a multivariate version of the relevance vector machine (RVM). Instead of the original treatment of the RVM relying on the use of type II maximum likelihood estimates of the hyper-parameters, we put a prior on the hyper-parameters and use Markov chain Monte Carlo technique for computation. We have also proposed an empirical Bayes method for our RVM and SVM. Our methods are illustrated with a prediction problem in the near-infrared (NIR) spectroscopy. A simulation study is also undertaken to check the prediction accuracy of our models. © 2012 Elsevier Inc.
Regression models for convex ROC curves.
Lloyd, C J
2000-09-01
The performance of a diagnostic test is summarized by its receiver operating characteristic (ROC) curve. Under quite natural assumptions about the latent variable underlying the test, the ROC curve is convex. Empirical data on a test's performance often comes in the form of observed true positive and false positive relative frequencies under varying conditions. This paper describes a family of regression models for analyzing such data. The underlying ROC curves are specified by a quality parameter delta and a shape parameter mu and are guaranteed to be convex provided delta > 1. Both the position along the ROC curve and the quality parameter delta are modeled linearly with covariates at the level of the individual. The shape parameter mu enters the model through the link functions log(p mu) - log(1 - p mu) of a binomial regression and is estimated either by search or from an appropriate constructed variate. One simple application is to the meta-analysis of independent studies of the same diagnostic test, illustrated on some data of Moses, Shapiro, and Littenberg (1993). A second application, to so-called vigilance data, is given, where ROC curves differ across subjects and modeling of the position along the ROC curve is of primary interest. PMID:10985227
Regression Models For Saffron Yields in Iran
S. H, Sanaeinejad; S. N, Hosseini
Saffron is an important crop in social and economical aspects in Khorassan Province (Northeast of Iran). In this research wetried to evaluate trends of saffron yield in recent years and to study the relationship between saffron yield and the climate change. A regression analysis was used to predict saffron yield based on 20 years of yield data in Birjand, Ghaen and Ferdows cities.Climatologically data for the same periods was provided by database of Khorassan Climatology Center. Climatologically data includedtemperature, rainfall, relative humidity and sunshine hours for ModelI, and temperature and rainfall for Model II. The results showed the coefficients of determination for Birjand, Ferdows and Ghaen for Model I were 0.69, 0.50 and 0.81 respectively. Also coefficients of determination for the same cities for model II were 0.53, 0.50 and 0.72 respectively. Multiple regression analysisindicated that among weather variables, temperature was the key parameter for variation ofsaffron yield. It was concluded that increasing temperature at spring was the main cause of declined saffron yield during recent years across the province. Finally, yield trend was predicted for the last 5 years using time series analysis.
A Gibbs sampler for multivariate linear regression
Mantz, Adam B.
2016-04-01
Kelly described an efficient algorithm, using Gibbs sampling, for performing linear regression in the fairly general case where non-zero measurement errors exist for both the covariates and response variables, where these measurements may be correlated (for the same data point), where the response variable is affected by intrinsic scatter in addition to measurement error, and where the prior distribution of covariates is modelled by a flexible mixture of Gaussians rather than assumed to be uniform. Here, I extend the Kelly algorithm in two ways. First, the procedure is generalized to the case of multiple response variables. Secondly, I describe how to model the prior distribution of covariates using a Dirichlet process, which can be thought of as a Gaussian mixture where the number of mixture components is learned from the data. I present an example of multivariate regression using the extended algorithm, namely fitting scaling relations of the gas mass, temperature, and luminosity of dynamically relaxed galaxy clusters as a function of their mass and redshift. An implementation of the Gibbs sampler in the R language, called LRGS, is provided.
Multivariate Regression with Block-structured Predictors
Ye, Saier
We study the problem of predicting multiple responses with a common set of predicting variables. Applying generalized Ordinary Least Squares (OLS) criterion on the responses altogether is practically equivalent to OLS estimation on the responses separately. Possible correlations between the response variables are overlooked. In order to take advantage of these interrelationships, Reduced-Rank Regression (RRR) imposes rank constraint on the coefficient matrix. RRR constructs latent factors from the original predicting variables, and the latent factors are the effective predictors. RRR reduces number of parameters to be estimated, and improves estimation efficiency. In the present work, we explore a novel regression model to incorporate "block-structured" predicting variables, where the predictors can be naturally partitioned into several groups or blocks. Variables in the same block share similar characteristics. It is reasonable to assume that in addition to an overall impact, predictors also have block-specific effects on the responses. Furthermore, we impose rank constraints on the coefficient matrices. In our framework, we construct two types of latent factors that drive the variation in the responses. We have joint factors, which are formed by all predictors across all blocks; and individual factors, which are formed by variables within individual blocks. The proposed method exceeds RRR in terms of prediction accuracy and ease of interpretation in the presence of block structure in the predicting variables.
Revisit of Sheppard corrections in linear regression
无
2010-01-01
Dempster and Rubin(D&R) in their JRSSB paper considered the statistical error caused by data rounding in a linear regression model and compared the Sheppard correction,BRB correction and the ordinary LSE by simulations.Some asymptotic results when the rounding scale tends to 0 were also presented.In a previous research,we found that the ordinary sample variance of rounded data from normal populations is always inconsistent while the sample mean of rounded data is consistent if and only if the true mean is a multiple of the half rounding scale.In the light of these results,in this paper we further investigate the rounding errors in linear regressions.We notice that these results form the basic reasons that the Sheppard corrections perform better than other methods in D&R examples and their conclusion in general cases is incorrect.Examples in which the Sheppard correction works worse than the BRB correction are also given.Furthermore,we propose a new approach to estimate the parameters,called "two-stage estimator",and establish the consistency and asymptotic normality of the new estimators.
Variable Selection in Logistic Regression Mo del
ZHANG Shangli; ZHANG Lili; QIU Kuanmin; LU Ying; CAI Baigen
2015-01-01
Variable selection is one of the most impor-tant problems in pattern recognition. In linear regression model, there are many methods can solve this problem, such as Least absolute shrinkage and selection operator (LASSO) and many improved LASSO methods, but there are few variable selection methods in generalized linear models. We study the variable selection problem in logis-tic regression model. We propose a new variable selection method–the logistic elastic net, prove that it has grouping eff ect which means that the strongly correlated predictors tend to be in or out of the model together. The logistic elastic net is particularly useful when the number of pre-dictors (p) is much bigger than the number of observations (n). By contrast, the LASSO is not a very satisfactory vari-able selection method in the case when p is more larger than n. The advantage and eff ectiveness of this method are demonstrated by real leukemia data and a simulation study.
Multiple Linear Regression Models in Outlier Detection
S.M.A.Khaleelur Rahman
2012-02-01
Full Text Available Identifying anomalous values in the real-world database is important both for improving the quality of original data and for reducing the impact of anomalous values in the process of knowledge discovery in databases. Such anomalous values give useful information to the data analyst in discovering useful patterns. Through isolation, these data may be separated and analyzed. The analysis of outliers and influential points is an important step of the regression diagnostics. In this paper, our aim is to detect the points which are very different from the others points. They do not seem to belong to a particular population and behave differently. If these influential points are to be removed it will lead to a different model. Distinction between these points is not always obvious and clear. Hence several indicators are used for identifying and analyzing outliers. Existing methods of outlier detection are based on manual inspection of graphically represented data. In this paper, we present a new approach in automating the process of detecting and isolating outliers. Impact of anomalous values on the dataset has been established by using two indicators DFFITS and Cook’sD. The process is based on modeling the human perception of exceptional values by using multiple linear regression analysis.
Dyslipidemia and Outcome in Patients with Acute Ischemic Stroke
XU Tian; ZHANG Jin Tao; YANG Mei; ZHANG Huan; LIU Wen Qing; KONG Yan; XU Tan; ZHANG Yong Hong
2014-01-01
ObjectiveTo study the relationship between dyslipidemia and outcome in patients with acute ischemic stroke. MethodsData about 1 568 patients with acute ischemic stroke werecollected from 4 hospitals in Shandong Province from January 2006 to December 2008. National Institute of Health Stroke Scale (NIHSS) >10 at discharge or death was defined as the outcome. Effect of dyslipidemia on outcome in patients with acute ischemic stroke was analyzed by multivariate logistic regression analysis and propensity score-adjusted analysis, respectively. ResultsThe serum levels of TC, LDL-C, and HDL-C were significantly associated with the outcome in patients with acute ischemic stroke. Multivariate logistic regression analysis and propensity score-adjusted analysis showed that the ORs and 95% CIs were 3.013 (1.259, 7.214)/2.655 (1.298, 5.43), 3.157(1.306, 7.631)/3.405(1.621, 7.154), and 0.482 (0.245, 0.946)/0.51 (0.282, 0.921), respectively, for patients with acute ischemic stroke. Hosmer-Lemeshow goodness-of-fit test showed no significant difference in observed and predicted risk in patients with acute ischemic stroke (chi-square=8.235, P=0.411). ConclusionSerum levels of TC, LDL-C, and HDL-C are positively related with the outcome in patients with acute ischemic stroke.
Xu, Qian; Chai, Shou-jie; Qian, Ying-Ying; Zhang, Min; Wang, Kai
2012-01-01
Aim: To determine the roles of breast regression protein-39 (BRP-39) in regulating dendritic cell maturation and in pathology of acute asthma. Methods: Mouse bone marrow-derived dendritic cells (BMDCs) were prepared, and infected with adenovirus over-expressing BRP-39. Ovalbumin (OVA)-induced murine model of acute asthma was made in female BALB/c mice by sensitizing and challenging with chicken OVA and Imject Alum. The transfected BMDCs were adoptively transferred into OVA-treated mice via in...
Logistic regression applied to natural hazards: rare event logistic regression with replications
M. Guns
2012-06-01
Full Text Available Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.
David Darmon
2014-12-01
Full Text Available A popular approach in the investigation of the short-term behavior of a non-stationary time series is to assume that the time series decomposes additively into a long-term trend and short-term fluctuations. A first step towards investigating the short-term behavior requires estimation of the trend, typically via smoothing in the time domain. We propose a method for time-domain smoothing, called complexity-regularized regression (CRR. This method extends recent work, which infers a regression function that makes residuals from a model “look random”. Our approach operationalizes non-randomness in the residuals by applying ideas from computational mechanics, in particular the statistical complexity of the residual process. The method is compared to generalized cross-validation (GCV, a standard approach for inferring regression functions, and shown to outperform GCV when the error terms are serially correlated. Regression under serially-correlated residuals has applications to time series analysis, where the residuals may represent short timescale activity. We apply CRR to a time series drawn from the Dow Jones Industrial Average and examine how both the long-term and short-term behavior of the market have changed over time.
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.
On relationship between regression models and interpretation of multiple regression coefficients
Varaksin, A N
2012-01-01
In this paper, we consider the problem of treating linear regression equation coefficients in the case of correlated predictors. It is shown that in general there are no natural ways of interpreting these coefficients similar to the case of single predictor. Nevertheless we suggest linear transformations of predictors, reducing multiple regression to a simple one and retaining the coefficient at variable of interest. The new variable can be treated as the part of the old variable that has no linear statistical dependence on other presented variables.
Adaptive nonparametric instrumental regression by model selection
Johannes, Jan
2010-01-01
We consider the problem of estimating the structural function in nonparametric instrumental regression, where in the presence of an instrument W a response Y is modeled in dependence of an endogenous explanatory variable Z. The proposed estimator is based on dimension reduction and additional thresholding. The minimax optimal rate of convergence of the estimator is derived assuming that the structural function belongs to some ellipsoids which are in a certain sense linked to the conditional expectation operator of Z given W. We illustrate these results by considering classical smoothness assumptions. However, the proposed estimator requires an optimal choice of a dimension parameter depending on certain characteristics of the unknown structural function and the conditional expectation operator of Z given W, which are not known in practice. The main issue addressed in our work is a fully adaptive choice of this dimension parameter using a model selection approach under the restriction that the conditional expe...
Logistic regression against a divergent Bayesian network
Noel Antonio Sánchez Trujillo
2015-01-01
Full Text Available This article is a discussion about two statistical tools used for prediction and causality assessment: logistic regression and Bayesian networks. Using data of a simulated example from a study assessing factors that might predict pulmonary emphysema (where fingertip pigmentation and smoking are considered; we posed the following questions. Is pigmentation a confounding, causal or predictive factor? Is there perhaps another factor, like smoking, that confounds? Is there a synergy between pigmentation and smoking? The results, in terms of prediction, are similar with the two techniques; regarding causation, differences arise. We conclude that, in decision-making, the sum of both: a statistical tool, used with common sense, and previous evidence, taking years or even centuries to develop; is better than the automatic and exclusive use of statistical resources.
Nonparametric additive regression for repeatedly measured data
Carroll, R. J.
2009-05-20
We develop an easily computed smooth backfitting algorithm for additive model fitting in repeated measures problems. Our methodology easily copes with various settings, such as when some covariates are the same over repeated response measurements. We allow for a working covariance matrix for the regression errors, showing that our method is most efficient when the correct covariance matrix is used. The component functions achieve the known asymptotic variance lower bound for the scalar argument case. Smooth backfitting also leads directly to design-independent biases in the local linear case. Simulations show our estimator has smaller variance than the usual kernel estimator. This is also illustrated by an example from nutritional epidemiology. © 2009 Biometrika Trust.
Adaptive Rank Penalized Estimators in Multivariate Regression
Bunea, Florentina; Wegkamp, Marten
2010-01-01
We introduce a new criterion, the Rank Selection Criterion (RSC), for selecting the optimal reduced rank estimator of the coefficient matrix in multivariate response regression models. The corresponding RSC estimator minimizes the Frobenius norm of the fit plus a regularization term proportional to the number of parameters in the reduced rank model. The rank of the RSC estimator provides a consistent estimator of the rank of the coefficient matrix. The consistency results are valid not only in the classic asymptotic regime, when the number of responses $n$ and predictors $p$ stays bounded, and the number of observations $m$ grows, but also when either, or both, $n$ and $p$ grow, possibly much faster than $m$. Our finite sample prediction and estimation performance bounds show that the RSC estimator achieves the optimal balance between the approximation error and the penalty term. Furthermore, our procedure has very low computational complexity, linear in the number of candidate models, making it particularly ...
Conjoined legs: Sirenomelia or caudal regression syndrome?
Sakti Prasad Das
2013-01-01
Full Text Available Presence of single umbilical persistent vitelline artery distinguishes sirenomelia from caudal regression syndrome. We report a case of a12-year-old boy who had bilateral umbilical arteries presented with fusion of both legs in the lower one third of leg. Both feet were rudimentary. The right foot had a valgus rocker-bottom deformity. All toes were present but rudimentary. The left foot showed absence of all toes. Physical examination showed left tibia vara. The chest evaluation in sitting revealed pigeon chest and elevated right shoulder. Posterior examination of the trunk showed thoracic scoliosis with convexity to right. The patient was operated and at 1 year followup the boy had two separate legs with a good aesthetic and functional results.
Remaining Phosphorus Estimate Through Multiple Regression Analysis
M. E. ALVES; A. LAVORENTI
2006-01-01
The remaining phosphorus (Prem), P concentration that remains in solution after shaking soil with 0.01 mol L-1 CaCl2 containing 60 μg mL-1 P, is a very useful index for studies related to the chemistry of variable charge soils. Although the Prem determination is a simple procedure, the possibility of estimating accurate values of this index from easily and/or routinely determined soil properties can be very useful for practical purposes. The present research evaluated the Premestimation through multiple regression analysis in which routinely determined soil chemical data, soil clay content and soil pH measured in 1 mol L-1 NaF (pHNaF) figured as Prem predictor variables. The Prem can be estimated with acceptable accuracy using the above-mentioned approach, and PHNaF not only substitutes for clay content as a predictor variable but also confers more accuracy to the Prem estimates.
Support vector machines for classification and regression.
Brereton, Richard G; Lloyd, Gavin R
2010-02-01
The increasing interest in Support Vector Machines (SVMs) over the past 15 years is described. Methods are illustrated using simulated case studies, and 4 experimental case studies, namely mass spectrometry for studying pollution, near infrared analysis of food, thermal analysis of polymers and UV/visible spectroscopy of polyaromatic hydrocarbons. The basis of SVMs as two-class classifiers is shown with extensive visualisation, including learning machines, kernels and penalty functions. The influence of the penalty error and radial basis function radius on the model is illustrated. Multiclass implementations including one vs. all, one vs. one, fuzzy rules and Directed Acyclic Graph (DAG) trees are described. One-class Support Vector Domain Description (SVDD) is described and contrasted to conventional two- or multi-class classifiers. The use of Support Vector Regression (SVR) is illustrated including its application to multivariate calibration, and why it is useful when there are outliers and non-linearities. PMID:20098757
Acute arterial occlusion - kidney
... arterial thrombosis; Renal artery embolism; Acute renal artery occlusion; Embolism - renal artery ... often result in permanent kidney failure. Acute arterial occlusion of the renal artery can occur after injury ...
Acute Pancreatitis in Children
... a feeding tube or an IV to prevent malnutrition and improve healing. Does my child have to ... intestines. Can my child die from acute pancreatitis? Death from acute pancreatitis is quite rare in children– ...
Logistic Regression Applied to Seismic Discrimination
BG Amindan; DN Hagedorn
1998-10-08
The usefulness of logistic discrimination was examined in an effort to learn how it performs in a regional seismic setting. Logistic discrimination provides an easily understood method, works with user-defined models and few assumptions about the population distributions, and handles both continuous and discrete data. Seismic event measurements from a data set compiled by Los Alamos National Laboratory (LANL) of Chinese events recorded at station WMQ were used in this demonstration study. PNNL applied logistic regression techniques to the data. All possible combinations of the Lg and Pg measurements were tried, and a best-fit logistic model was created. The best combination of Lg and Pg frequencies for predicting the source of a seismic event (earthquake or explosion) used Lg{sub 3.0-6.0} and Pg{sub 3.0-6.0} as the predictor variables. A cross-validation test was run, which showed that this model was able to correctly predict 99.7% earthquakes and 98.0% explosions for this given data set. Two other models were identified that used Pg and Lg measurements from the 1.5 to 3.0 Hz frequency range. Although these other models did a good job of correctly predicting the earthquakes, they were not as effective at predicting the explosions. Two possible biases were discovered which affect the predicted probabilities for each outcome. The first bias was due to this being a case-controlled study. The sampling fractions caused a bias in the probabilities that were calculated using the models. The second bias is caused by a change in the proportions for each event. If at a later date the proportions (a priori probabilities) of explosions versus earthquakes change, this would cause a bias in the predicted probability for an event. When using logistic regression, the user needs to be aware of the possible biases and what affect they will have on the predicted probabilities.
Collaborative regression-based anatomical landmark detection
Gao, Yaozong; Shen, Dinggang
2015-12-01
Anatomical landmark detection plays an important role in medical image analysis, e.g. for registration, segmentation and quantitative analysis. Among the various existing methods for landmark detection, regression-based methods have recently attracted much attention due to their robustness and efficiency. In these methods, landmarks are localised through voting from all image voxels, which is completely different from the classification-based methods that use voxel-wise classification to detect landmarks. Despite their robustness, the accuracy of regression-based landmark detection methods is often limited due to (1) the inclusion of uninformative image voxels in the voting procedure, and (2) the lack of effective ways to incorporate inter-landmark spatial dependency into the detection step. In this paper, we propose a collaborative landmark detection framework to address these limitations. The concept of collaboration is reflected in two aspects. (1) Multi-resolution collaboration. A multi-resolution strategy is proposed to hierarchically localise landmarks by gradually excluding uninformative votes from faraway voxels. Moreover, for informative voxels near the landmark, a spherical sampling strategy is also designed at the training stage to improve their prediction accuracy. (2) Inter-landmark collaboration. A confidence-based landmark detection strategy is proposed to improve the detection accuracy of ‘difficult-to-detect’ landmarks by using spatial guidance from ‘easy-to-detect’ landmarks. To evaluate our method, we conducted experiments extensively on three datasets for detecting prostate landmarks and head & neck landmarks in computed tomography images, and also dental landmarks in cone beam computed tomography images. The results show the effectiveness of our collaborative landmark detection framework in improving landmark detection accuracy, compared to other state-of-the-art methods.
Diagnosing and Treating Acute Bronchitis
... Lung Disease Lookup > Acute Bronchitis Diagnosing and Treating Acute Bronchitis It is important to get your questions about ... Symptoms that last a few weeks How Is Acute Bronchitis Diagnosed? Healthcare providers diagnose acute bronchitis by asking ...
Vargas, M.; Crossa, J.; Eeuwijk, van F.A.; Ramirez, M.E.; Sayre, K.
1999-01-01
Partial least squares (PLS) and factorial regression (FR) are statistical models that incorporate external environmental and/or cultivar variables for studying and interpreting genotype × environment interaction (GEl). The Additive Main effect and Multiplicative Interaction (AMMI) model uses only th
Metformin induced acute pancreatitis
Alsubaie, Sadeem; Almalki, Mussa H.
2013-01-01
Acute pancreatitis frequently presents with abdomen pain but may presents with various skin manifestations as rash and rarely, pancreatic panniculitis. Metformin, one of the most effective and valuable oral hypoglycemic agents in the biguanide class was linked to acute pancreatitis in few cases. Here, we report a case of metformin induce acute pancreatitis in young healthy man with normal renal function.
Anthonsen, Kristian; Høstmark, Karianne; Hansen, Søren;
2013-01-01
Conservative treatment of acute otitis media may lead to more complications. This study evaluates changes in incidence, the clinical and microbiological findings, the complications and the outcome of acute mastoiditis in children in a country employing conservative guidelines in treating acute ot...
Satish, S.; Rajesh, R.; Kurian, G.; Seethalekshmi, N. V.; Unni, M.; Unni, V. N.
2010-01-01
While acute renal failure secondary to intravascular hemolysis is well described in hemolytic anemias, recurrent acute renal failure as the presenting manifestation of a hemolytic anemia is rare. We report a patient with recurrent acute renal failure who was found to have paroxysmal nocturnal hemoglobinuria (PNH), on evaluation.
Security Regression Testing Framework For Web Application Development
Waheed, Usman
2014-01-01
A framework and process that explains how to perform security regression testing for web applications. This paper discusses and proposes a framework based on open source tools that can be used to perform automated security regression testing of web applications.
An Additive-Multiplicative Cox-Aalen Regression Model
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...
Dimension Reduction and Discretization in Stochastic Problems by Regression Method
Ditlevsen, Ove Dalager
1996-01-01
The chapter mainly deals with dimension reduction and field discretizations based directly on the concept of linear regression. Several examples of interesting applications in stochastic mechanics are also given.Keywords: Random fields discretization, Linear regression, Stochastic interpolation...
Acute Glucose Response Properties Beyond Feeding.
Burnett, C Joseph; Krashes, Michael J
2016-05-01
Hypothalamic AgRP neurons potently coordinate feeding behavior to ensure an organism's viability. However, their acute role in glucose-regulatory function remains to be addressed. Steculorum et al. now report that activation of a specific set of AgRP neurons results in an impairment of insulin-stimulated glucose uptake in brown fat through a myogenic signature program. PMID:27052261
Greening, Leilani; Stoppelbein, Laura; Luebbe, Aaron
2010-04-01
Given that parenting practices have been linked to suicidal behavior in adolescence, examining the moderating effect of parenting styles on suicidal behavior early in development could offer potential insight into possible buffers as well as directions for suicide prevention and intervention later in adolescence. Hence, the moderating effects of parenting styles, including authoritarian, permissive, and features of authoritative parenting, on depressed and aggressive children's suicidal behavior, including ideation and attempts, were evaluated with young children (N = 172; 72% male, 28% female) ranging from 6 to 12 years of age. African American (69%) and Caucasian (31%) children admitted for acute psychiatric inpatient care completed standardized measures of suicidal behavior, depressive symptoms, and proactive and reaction aggression. Their parents also completed standardized measures of parental distress and parenting style. Hierarchical regression analyses revealed that, while statistically controlling for age and gender, children who endorsed more depressive symptoms or reactive aggression reported more current and past suicidal behavior than children who endorsed fewer depressive or aggressive symptoms. The significant positive relationship observed between depressive symptoms and childhood suicidal behavior, however, was attenuated by parental use of authoritarian parenting practices for African-American and older children but not for younger and Caucasian children. The ethnic/racial difference observed for the buffering effect of authoritarian parenting practices offers potential theoretical and clinical implications for conceptualizing the moderating effects of parenting styles on African-American and Caucasian children's suicidal behavior. PMID:19806443
Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned
Bettina Grün; Ioannis Kosmidis; Achim Zeileis
2012-01-01
Beta regression - an increasingly popular approach for modeling rates and proportions - is extended in various directions: (a) bias correction/reduction of the maximum likelihood estimator, (b) beta regression tree models by means of recursive partitioning, (c) latent class beta regression by means of finite mixture models. All three extensions may be of importance for enhancing the beta regression toolbox in practice to provide more reliable inference and capture both observed and unobserved...
Shrinkage Estimation and Selection for Multiple Functional Regression
LIAN, HENG
2011-01-01
Functional linear regression is a useful extension of simple linear regression and has been investigated by many researchers. However, functional variable selection problems when multiple functional observations exist, which is the counterpart in the functional context of multiple linear regression, is seldom studied. Here we propose a method using group smoothly clipped absolute deviation penalty (gSCAD) which can perform regression estimation and variable selection simultaneously. We show t...
Multiple Linear Regression Model Used in Economic Analyses
Constantin ANGHELACHE; Madalina Gabriela ANGHEL; Ligia PRODAN; Cristina SACALA; Marius POPOVICI
2014-01-01
The multiple regression is a tool that offers the possibility to analyze the correlations between more than two variables, situation which account for most cases in macro-economic studies. The best known method of estimation for multiple regression is the method of least squares. As in the two-variable regression, we choose the regression function of sample and minimize the sum of squared residual values. Another method that allows us to take into account the number of variables factor when d...
Synthesis analysis of regression models with a continuous outcome
Zhou, Xiao-Hua; Hu, Nan; Hu, Guizhou; Root, Martin
2009-01-01
To estimate the multivariate regression model from multiple individual studies, it would be challenging to obtain results if the input from individual studies only provide univariate or incomplete multivariate regression information. Samsa et al. (J. Biomed. Biotechnol. 2005; 2:113–123) proposed a simple method to combine coefficients from univariate linear regression models into a multivariate linear regression model, a method known as synthesis analysis. However, the validity of this method...
Regression Benchmarking: An Approach to Quality Assurance in Performance
Bulej, Lubomír
2005-01-01
The paper presents a short summary of our work in the area of regression benchmarking and its application to software development. Specially, we explain the concept of regression benchmarking, the requirements for employing regression testing in a software project, and methods used for analyzing the vast amounts of data resulting from repeated benchmarking. We present the application of regression benchmarking on a real software project and conclude with a glimpse at the challenges for the fu...
Many regression algorithms, one unified model — A review
Stulp, Freek; Sigaud, Olivier
2015-01-01
Regression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. The history of regression is closely related to the history of artificial neural networks since the seminal work of Rosenblatt (1958). The aims of this paper are to provide an overview of many regression algorithms, and to demonstrate how the function representation whose parameters they regress fall into two classes: a weighted sum of basis ...
Kernel regression for fMRI pattern prediction
Chu, Carlton; Ni, Yizhao; Tan, Geoffrey; Saunders, Craig J.; Ashburner, John
2011-01-01
This paper introduces two kernel-based regression schemes to decode or predict brain states from functional brain scans as part of the Pittsburgh Brain Activity Interpretation Competition (PBAIC) 2007, in which our team was awarded first place. Our procedure involved image realignment, spatial smoothing, detrending of low-frequency drifts, and application of multivariate linear and non-linear kernel regression methods: namely kernel ridge regression (KRR) and relevance vector regression (RVR)...
A note on constrained M-estimation and its recursive analog in multivariate linear regression models
RAO; Calyampudi; R
2009-01-01
In this paper,the constrained M-estimation of the regression coeffcients and scatter parameters in a general multivariate linear regression model is considered.Since the constrained M-estimation is not easy to compute,an up-dating recursion procedure is proposed to simplify the com-putation of the estimators when a new observation is obtained.We show that,under mild conditions,the recursion estimates are strongly consistent.In addition,the asymptotic normality of the recursive constrained M-estimators of regression coeffcients is established.A Monte Carlo simulation study of the recursion estimates is also provided.Besides,robustness and asymptotic behavior of constrained M-estimators are briefly discussed.
Stahel-Donoho kernel estimation for fixed design nonparametric regression models
LIN Lu; CUI Xia
2006-01-01
This paper reports a robust kernel estimation for fixed design nonparametric regression models.A Stahel-Donoho kernel estimation is introduced,in which the weight functions depend on both the depths of data and the distances between the design points and the estimation points.Based on a local approximation,a computational technique is given to approximate to the incomputable depths of the errors.As a result the new estimator is computationally efficient.The proposed estimator attains a high breakdown point and has perfect asymptotic behaviors such as the asymptotic normality and convergence in the mean squared error.Unlike the depth-weighted estimator for parametric regression models,this depth-weighted nonparametric estimator has a simple variance structure and then we can compare its efficiency with the original one.Some simulations show that the new method can smooth the regression estimation and achieve some desirable balances between robustness and efficiency.