International Nuclear Information System (INIS)
Siddiqui, M.R.S.; Gormly, K.L.; Bhoday, J.; Balyansikova, S.; Battersby, N.J.; Chand, M.; Rao, S.; Tekkis, P.; Abulafi, A.M.; Brown, G.
2016-01-01
Aim: To investigate whether the magnetic resonance imaging (MRI) tumour regression grading (mrTRG) scale can be taught effectively resulting in a clinically reasonable interobserver agreement (>0.4; moderate to near perfect agreement). Materials and methods: This study examines the interobserver agreement of mrTRG, between 35 radiologists and a central reviewer. Two workshops were organised for radiologists to assess regression of rectal cancers on MRI staging scans. A range of mrTRGs on 12 patient scans were used for assessment. Results: Kappa agreement ranged from 0.14–0.82 with a median value of 0.57 (95% CI: 0.37–0.77) indicating good overall agreement. Eight (26%) radiologists had very good/near perfect agreement (κ>0.8). Six (19%) radiologists had good agreement (0.8≥κ>0.6) and a further 12 (39%) had moderate agreement (0.6≥κ>0.4). Five (16%) radiologists had a fair agreement (0.4≥κ>0.2) and two had poor agreement (0.2>κ). There was a tendency towards good agreement (skewness: 0.92). In 65.9% and 90% of cases the radiologists were able to correctly highlight good and poor responders, respectively. Conclusions: The assessment of the response of rectal cancers to chemoradiation therapy may be performed effectively using mrTRG. Radiologists can be taught the mrTRG scale. Even with minimal training, good agreement with the central reviewer along with effective differentiation between good and intermediate/poor responders can be achieved. Focus should be on facilitating the identification of good responders. It is predicted that with more intensive interactive case-based learning a κ>0.8 is likely to be achieved. Testing and retesting is recommended. - Highlights: • Inter-observer agreement of radiologists was assessed using MRI rectal tumour regression scale. • Kappa agreement had a median value of 0.57 (95% CI: 0.37–0.77) indicating an overall good agreement. • In 65.9% and 90% of cases the radiologists were able to correctly highlight
LENUS (Irish Health Repository)
Abdul-Jalil, K I
2014-01-01
To date, there is no uniform consensus on whether tumour regression grade (TRG) is predictive of outcome in rectal cancer. Furthermore, the lack of standardization of TRG grading is a major source of variability in published studies. The aim of this study was to evaluate the prognostic impact of TRG in a cohort of patients with locally advanced rectal cancer treated with neoadjuvant chemoradiation therapy (CRT). In addition to the Mandard TRG, we utilized four TRG systems modified from the Mandard TRG system and applied them to the cohort to assess which TRG system is most informative.
Energy Technology Data Exchange (ETDEWEB)
Lee, Hong Seok; Choi, Doo Ho; Park, Hee Chul; Park, Won; Yu, Jeong Il; Chung, Kwang Zoo [Dept. of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of)
2016-09-15
To determine whether large rectal volume on planning computed tomography (CT) results in lower tumor regression grade (TRG) after neoadjuvant concurrent chemoradiotherapy (CCRT) in rectal cancer patients. We reviewed medical records of 113 patients treated with surgery following neoadjuvant CCRT for rectal cancer between January and December 2012. Rectal volume was contoured on axial images in which gross tumor volume was included. Average axial rectal area (ARA) was defined as rectal volume divided by longitudinal tumor length. The impact of rectal volume and ARA on TRG was assessed. Average rectal volume and ARA were 11.3 mL and 2.9 cm². After completion of neoadjuvant CCRT in 113 patients, pathologic results revealed total regression (TRG 4) in 28 patients (25%), good regression (TRG 3) in 25 patients (22%), moderate regression (TRG 2) in 34 patients (30%), minor regression (TRG 1) in 24 patients (21%), and no regression (TRG0) in 2 patients (2%). No difference of rectal volume and ARA was found between each TRG groups. Linear correlation existed between rectal volume and TRG (p = 0.036) but not between ARA and TRG (p = 0.058). Rectal volume on planning CT has no significance on TRG in patients receiving neoadjuvant CCRT for rectal cancer. These results indicate that maintaining minimal rectal volume before each treatment may not be necessary.
International Nuclear Information System (INIS)
Lee, Hong Seok; Choi, Doo Ho; Park, Hee Chul; Park, Won; Yu, Jeong Il; Chung, Kwang Zoo
2016-01-01
To determine whether large rectal volume on planning computed tomography (CT) results in lower tumor regression grade (TRG) after neoadjuvant concurrent chemoradiotherapy (CCRT) in rectal cancer patients. We reviewed medical records of 113 patients treated with surgery following neoadjuvant CCRT for rectal cancer between January and December 2012. Rectal volume was contoured on axial images in which gross tumor volume was included. Average axial rectal area (ARA) was defined as rectal volume divided by longitudinal tumor length. The impact of rectal volume and ARA on TRG was assessed. Average rectal volume and ARA were 11.3 mL and 2.9 cm². After completion of neoadjuvant CCRT in 113 patients, pathologic results revealed total regression (TRG 4) in 28 patients (25%), good regression (TRG 3) in 25 patients (22%), moderate regression (TRG 2) in 34 patients (30%), minor regression (TRG 1) in 24 patients (21%), and no regression (TRG0) in 2 patients (2%). No difference of rectal volume and ARA was found between each TRG groups. Linear correlation existed between rectal volume and TRG (p = 0.036) but not between ARA and TRG (p = 0.058). Rectal volume on planning CT has no significance on TRG in patients receiving neoadjuvant CCRT for rectal cancer. These results indicate that maintaining minimal rectal volume before each treatment may not be necessary
Grades, Gender, and Encouragement: A Regression Discontinuity Analysis
Owen, Ann L.
2010-01-01
The author employs a regression discontinuity design to provide direct evidence on the effects of grades earned in economics principles classes on the decision to major in economics and finds a differential effect for male and female students. Specifically, for female students, receiving an A for a final grade in the first economics class is…
Zarb, Francis; McEntee, Mark F; Rainford, Louise
2015-06-01
To evaluate visual grading characteristics (VGC) and ordinal regression analysis during head CT optimisation as a potential alternative to visual grading assessment (VGA), traditionally employed to score anatomical visualisation. Patient images (n = 66) were obtained using current and optimised imaging protocols from two CT suites: a 16-slice scanner at the national Maltese centre for trauma and a 64-slice scanner in a private centre. Local resident radiologists (n = 6) performed VGA followed by VGC and ordinal regression analysis. VGC alone indicated that optimised protocols had similar image quality as current protocols. Ordinal logistic regression analysis provided an in-depth evaluation, criterion by criterion allowing the selective implementation of the protocols. The local radiology review panel supported the implementation of optimised protocols for brain CT examinations (including trauma) in one centre, achieving radiation dose reductions ranging from 24 % to 36 %. In the second centre a 29 % reduction in radiation dose was achieved for follow-up cases. The combined use of VGC and ordinal logistic regression analysis led to clinical decisions being taken on the implementation of the optimised protocols. This improved method of image quality analysis provided the evidence to support imaging protocol optimisation, resulting in significant radiation dose savings. • There is need for scientifically based image quality evaluation during CT optimisation. • VGC and ordinal regression analysis in combination led to better informed clinical decisions. • VGC and ordinal regression analysis led to dose reductions without compromising diagnostic efficacy.
2010-10-25
... DEPARTMENT OF LABOR Employment and Training Administration [TA-W-74,063] TRG Insurance Solutions... petitioners requested administrative reconsideration of the Department's negative determination regarding the... workers in the group threatened with total or partial separation from employment on date of certification...
Spontaneous regression of residual low-grade cerebellar pilocytic astrocytomas in children
International Nuclear Information System (INIS)
Gunny, Roxana S.; Saunders, Dawn E.; Hayward, Richard D.; Phipps, Kim P.; Harding, Brian N.
2005-01-01
Cerebellar low-grade astrocytomas (CLGAs) of childhood are benign tumours and are usually curable by surgical resection alone or combined with adjuvant radiotherapy. To undertake a retrospective study of our children with CLGA to determine the optimum schedule for surveillance imaging following initial surgery. In this report we describe the phenomenon of spontaneous regression of residual tumour and discuss its prognostic significance regarding future imaging. A retrospective review was conducted of children treated for histologically proven CLGA at Great Ormond Street Hospital from 1988 to 1998. Of 83 children with CLGA identified, 13 (15.7%) had incomplete resections. Two children with large residual tumours associated with persistent symptoms underwent additional treatment. Eleven children were followed by surveillance imaging alone for a mean of 6.83 years (range 2-13.25 years). Spontaneous tumour regression was seen in 5 (45.5%) of the 11 children. There were no differences in age, gender, symptomatology, histological grade or Ki-67 fractions between those with spontaneous tumour regression and those with progression. There was a non-significant trend that larger volume residual tumours progressed. Residual tumour followed by surveillance imaging may either regress or progress. For children with residual disease we recommend surveillance imaging every 6 months for the first 2 years, every year for years 3, 4 and 5, then every second year if residual tumour is still present 5 years after initial surgery. This would detect not only progressive or recurrent disease, but also spontaneous regression which can occur later than disease progression. (orig.)
Cho, Eunsoo; Capin, Philip; Roberts, Greg; Vaughn, Sharon
2017-07-01
Within multitiered instructional delivery models, progress monitoring is a key mechanism for determining whether a child demonstrates an adequate response to instruction. One measure commonly used to monitor the reading progress of students is oral reading fluency (ORF). This study examined the extent to which ORF slope predicts reading comprehension outcomes for fifth-grade struggling readers ( n = 102) participating in an intensive reading intervention. Quantile regression models showed that ORF slope significantly predicted performance on a sentence-level fluency and comprehension assessment, regardless of the students' reading skills, controlling for initial ORF performance. However, ORF slope was differentially predictive of a passage-level comprehension assessment based on students' reading skills when controlling for initial ORF status. Results showed that ORF explained unique variance for struggling readers whose posttest performance was at the upper quantiles at the end of the reading intervention, but slope was not a significant predictor of passage-level comprehension for students whose reading problems were the most difficult to remediate.
Cortés-Alaguero, Caterina; González-Mirasol, Esteban; Morales-Roselló, José; Poblet-Martinez, Enrique
2017-03-15
To determine whether medical history, clinical examination and human papilloma virus (HPV) genotype influence spontaneous regression in cervical intraepithelial neoplasia grade I (CIN-I). We retrospectively evaluated 232 women who were histologically diagnosed as have CIN-I by means of Kaplan-Meier curves, the pattern of spontaneous regression according to the medical history, clinical examination, and HPV genotype. Spontaneous regression occurred in most patients and was influenced by the presence of multiple HPV genotypes but not by the HPV genotype itself. In addition, regression frequency was diminished when more than 50% of the cervix surface was affected or when an abnormal cytology was present at the beginning of follow-up. The frequency of regression in CIN-I is high, making long-term follow-up and conservative management advisable. Data from clinical examination and HPV genotyping might help to anticipate which lesions will regress.
Fremond, L; Bouché, O; Diébold, M D; Demange, L; Zeitoun, P; Thiefin, G
1995-01-01
Barrett's oesophagus is a premalignant condition. The possibility of eradicating at least partially the metaplastic epithelium has been reported recently. In this case report, a patient with Barrett's oesophagus complicated by high grade dysplasia and focal adenocarcinoma was treated by Nd:Yag laser then high dose rate intraluminal irradiation while on omeprazole 40 mg/day. A partial eradication of Barrett's oesophagus and a transient tumoural regression were obtained. Histologically, residual specialized-type glandular tissue was observed beneath regenerative squamous epithelium. Four months after intraluminal irradiation, a local tumoural recurrence was detected while the area of restored squamous epithelium was unchanged on omeprazole 40 mg/day. This indicates that physical destruction of Barrett's oesophagus associated with potent antisecretory treatment can induce a regression of the metaplastic epithelium, even in presence of high grade dysplasia. The persistence of specialized-type glands beneath the squamous epithelium raises important issues about its potential malignant degeneration.
Koeneman, Margot M; van Lint, Freyja H M; van Kuijk, Sander M J; Smits, Luc J M; Kooreman, Loes F S; Kruitwagen, Roy F P M; Kruse, Arnold J
2017-01-01
This study aims to develop a prediction model for spontaneous regression of cervical intraepithelial neoplasia grade 2 (CIN 2) lesions based on simple clinicopathological parameters. The study was conducted at Maastricht University Medical Center, the Netherlands. The prediction model was developed in a retrospective cohort of 129 women with a histologic diagnosis of CIN 2 who were managed by watchful waiting for 6 to 24months. Five potential predictors for spontaneous regression were selected based on the literature and expert opinion and were analyzed in a multivariable logistic regression model, followed by backward stepwise deletion based on the Wald test. The prediction model was internally validated by the bootstrapping method. Discriminative capacity and accuracy were tested by assessing the area under the receiver operating characteristic curve (AUC) and a calibration plot. Disease regression within 24months was seen in 91 (71%) of 129 patients. A prediction model was developed including the following variables: smoking, Papanicolaou test outcome before the CIN 2 diagnosis, concomitant CIN 1 diagnosis in the same biopsy, and more than 1 biopsy containing CIN 2. Not smoking, Papanicolaou class predictive of disease regression. The AUC was 69.2% (95% confidence interval, 58.5%-79.9%), indicating a moderate discriminative ability of the model. The calibration plot indicated good calibration of the predicted probabilities. This prediction model for spontaneous regression of CIN 2 may aid physicians in the personalized management of these lesions. Copyright © 2016 Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Adel T. Abbas
2017-01-01
Full Text Available The Grade-H high strength steel is used in the manufacturing of many civilian and military products. The procedures of manufacturing these parts have several turning operations. The key factors for the manufacturing of these parts are the accuracy, surface roughness (Ra, and material removal rate (MRR. The production line of these parts contains many CNC turning machines to get good accuracy and repeatability. The manufacturing engineer should fulfill the required surface roughness value according to the design drawing from first trail (otherwise these parts will be rejected as well as keeping his eye on maximum metal removal rate. The rejection of these parts at any processing stage will represent huge problems to any factory because the processing and raw material of these parts are very expensive. In this paper the artificial neural network was used for predicting the surface roughness for different cutting parameters in CNC turning operations. These parameters were investigated to get the minimum surface roughness. In addition, a mathematical model for surface roughness was obtained from the experimental data using a regression analysis method. The experimental data are then compared with both the regression analysis results and ANFIS (Adaptive Network-based Fuzzy Inference System estimations.
Cakir, Ebru; Kucuk, Ulku; Pala, Emel Ebru; Sezer, Ozlem; Ekin, Rahmi Gokhan; Cakmak, Ozgur
2017-05-01
Conventional cytomorphologic assessment is the first step to establish an accurate diagnosis in urinary cytology. In cytologic preparations, the separation of low-grade urothelial carcinoma (LGUC) from reactive urothelial proliferation (RUP) can be exceedingly difficult. The bladder washing cytologies of 32 LGUC and 29 RUP were reviewed. The cytologic slides were examined for the presence or absence of the 28 cytologic features. The cytologic criteria showing statistical significance in LGUC were increased numbers of monotonous single (non-umbrella) cells, three-dimensional cellular papillary clusters without fibrovascular cores, irregular bordered clusters, atypical single cells, irregular nuclear overlap, cytoplasmic homogeneity, increased N/C ratio, pleomorphism, nuclear border irregularity, nuclear eccentricity, elongated nuclei, and hyperchromasia (p ˂ 0.05), and the cytologic criteria showing statistical significance in RUP were inflammatory background, mixture of small and large urothelial cells, loose monolayer aggregates, and vacuolated cytoplasm (p ˂ 0.05). When these variables were subjected to a stepwise logistic regression analysis, four features were selected to distinguish LGUC from RUP: increased numbers of monotonous single (non-umbrella) cells, increased nuclear cytoplasmic ratio, hyperchromasia, and presence of small and large urothelial cells (p = 0.0001). By this logistic model of the 32 cases with proven LGUC, the stepwise logistic regression analysis correctly predicted 31 (96.9%) patients with this diagnosis, and of the 29 patients with RUP, the logistic model correctly predicted 26 (89.7%) patients as having this disease. There are several cytologic features to separate LGUC from RUP. Stepwise logistic regression analysis is a valuable tool for determining the most useful cytologic criteria to distinguish these entities. © 2017 APMIS. Published by John Wiley & Sons Ltd.
DEFF Research Database (Denmark)
Lindebjerg, J; Spindler, Karen-Lise Garm; Ploen, J
2009-01-01
to the tumour regression grade system and lymph node status in the surgical specimen was assessed. The prognostic value of clinico-pathological parameters was analysed using univariate analysis and Kaplan-Meier methods for comparison of groups. RESULTS: All patients responded to treatment and 47% had a major......OBJECTIVE: The purpose of the present study was to investigate the impact of tumour regression and the post-treatment lymph node status on the prognosis of rectal cancer treated by preoperative neoadjuvant chemoradiotherapy. METHOD: One hundred and thirty-five patients with locally advanced T3.......01). CONCLUSION: The combined assessment of lymph-node status and tumour response has strong prognostic value in locally advanced rectal cancer patient treated with preoperative long-course chemoradiation....
Farhadian, Maryam; Aliabadi, Mohsen; Darvishi, Ebrahim
2015-01-01
Prediction models are used in a variety of medical domains, and they are frequently built from experience which constitutes data acquired from actual cases. This study aimed to analyze the potential of artificial neural networks and logistic regression techniques for estimation of hearing impairment among industrial workers. A total of 210 workers employed in a steel factory (in West of Iran) were selected, and their occupational exposure histories were analyzed. The hearing loss thresholds of the studied workers were determined using a calibrated audiometer. The personal noise exposures were also measured using a noise dosimeter in the workstations. Data obtained from five variables, which can influence the hearing loss, were used as input features, and the hearing loss thresholds were considered as target feature of the prediction methods. Multilayer feedforward neural networks and logistic regression were developed using MATLAB R2011a software. Based on the World Health Organization classification for the grades of hearing loss, 74.2% of the studied workers have normal hearing thresholds, 23.4% have slight hearing loss, and 2.4% have moderate hearing loss. The accuracy and kappa coefficient of the best developed neural networks for prediction of the grades of hearing loss were 88.6 and 66.30, respectively. The accuracy and kappa coefficient of the logistic regression were also 84.28 and 51.30, respectively. Neural networks could provide more accurate predictions of the hearing loss than logistic regression. The prediction method can provide reliable and comprehensible information for occupational health and medicine experts.
Hay, Peter D; Smith, Julie; O'Connor, Richard A
2016-02-01
The aim of this study was to evaluate the benefits to SPECT bone scan image quality when applying resolution recovery (RR) during image reconstruction using software provided by a third-party supplier. Bone SPECT data from 90 clinical studies were reconstructed retrospectively using software supplied independent of the gamma camera manufacturer. The current clinical datasets contain 120×10 s projections and are reconstructed using an iterative method with a Butterworth postfilter. Five further reconstructions were created with the following characteristics: 10 s projections with a Butterworth postfilter (to assess intraobserver variation); 10 s projections with a Gaussian postfilter with and without RR; and 5 s projections with a Gaussian postfilter with and without RR. Two expert observers were asked to rate image quality on a five-point scale relative to our current clinical reconstruction. Datasets were anonymized and presented in random order. The benefits of RR on image scores were evaluated using ordinal logistic regression (visual grading regression). The application of RR during reconstruction increased the probability of both observers of scoring image quality as better than the current clinical reconstruction even where the dataset contained half the normal counts. Type of reconstruction and observer were both statistically significant variables in the ordinal logistic regression model. Visual grading regression was found to be a useful method for validating the local introduction of technological developments in nuclear medicine imaging. RR, as implemented by the independent software supplier, improved bone SPECT image quality when applied during image reconstruction. In the majority of clinical cases, acquisition times for bone SPECT intended for the purposes of localization can safely be halved (from 10 s projections to 5 s) when RR is applied.
Tzeng, I-Shiang; Liu, Su-Hsun; Chen, Kuan-Fu; Wu, Chin-Chieh; Chen, Jih-Chang
2016-10-01
To reduce patient boarding time at the emergency department (ED) and to improve the overall quality of the emergent care system in Taiwan, the Minister of Health and Welfare of Taiwan (MOHW) piloted the Grading Responsible Hospitals for Acute Care (GRHAC) audit program in 2007-2009.The aim of the study was to evaluate the impact of the GRHAC audit program on the identification and management of acute myocardial infarction (AMI)-associated ED visits by describing and comparing the incidence of AMI-associated ED visits before (2003-2007), during (2007-2009), and after (2009-2012) the initial audit program implementation.Using aggregated data from the MOHW of Taiwan, we estimated the annual incidence of AMI-associated ED visits by Poisson regression models. We used segmented regression techniques to evaluate differences in the annual rates and in the year-to-year changes in AMI-associated ED visits between 2003 and 2012. Medical comorbidities such as diabetes mellitus, hyperlipidemia, and hypertensive disease were considered as potential confounders.Overall, the number of AMI-associated patient visits increased from 8130 visits in 2003 to 12,695 visits in 2012 (P-value for trend capacity for timely and correctly diagnosing and managing patients presenting with AMI-associated symptoms or signs at the ED.
Directory of Open Access Journals (Sweden)
Artur Wnorowski
2017-06-01
Full Text Available Tree saps are nourishing biological media commonly used for beverage and syrup production. Although the nutritional aspect of tree saps is widely acknowledged, the exact relationship between the sap composition, origin, and effect on the metabolic rate of human cells is still elusive. Thus, we collected saps from seven different tree species and conducted composition-activity analysis. Saps from trees of Betulaceae, but not from Salicaceae, Sapindaceae, nor Juglandaceae families, were increasing the metabolic rate of HepG2 cells, as measured using tetrazolium-based assay. Content of glucose, fructose, sucrose, chlorides, nitrates, sulphates, fumarates, malates, and succinates in sap samples varied across different tree species. Grade correspondence analysis clustered trees based on the saps’ chemical footprint indicating its usability in chemotaxonomy. Multiple regression modeling showed that glucose and fumarate present in saps from silver birch (Betula pendula Roth., black alder (Alnus glutinosa Gaertn., and European hornbeam (Carpinus betulus L. are positively affecting the metabolic activity of HepG2 cells.
Linguiti, Giovanna; Antonacci, Rachele; Tasco, Gianluca; Grande, Francesco; Casadio, Rita; Massari, Serafina; Castelli, Vito; Consiglio, Arianna; Lefranc, Marie-Paule; Ciccarese, Salvatrice
2016-08-15
The bottlenose dolphin (Tursiops truncatus) is a mammal that belongs to the Cetartiodactyla and have lived in marine ecosystems for nearly 60 millions years. Despite its popularity, our knowledge about its adaptive immunity and evolution is very limited. Furthermore, nothing is known about the genomics and evolution of dolphin antigen receptor immunity. Here we report a evolutionary and expression study of Tursiops truncatus T cell receptor gamma (TRG) and alpha/delta (TRA/TRD) genes. We have identified in silico the TRG and TRA/TRD genes and analyzed the relevant mature transcripts in blood and in skin from four subjects. The dolphin TRG locus is the smallest and simplest of all mammalian loci as yet studied. It shows a genomic organization comprising two variable (V1 and V2), three joining (J1, J2 and J3) and a single constant (C), genes. Despite the fragmented nature of the genome assemblies, we deduced the TRA/TRD locus organization, with the recent TRDV1 subgroup genes duplications, as it is expected in artiodactyls. Expression analysis from blood of a subject allowed us to assign unambiguously eight TRAV genes to those annotated in the genomic sequence and to twelve new genes, belonging to five different subgroups. All transcripts were productive and no relevant biases towards TRAV-J rearrangements are observed. Blood and skin from four unrelated subjects expression data provide evidence for an unusual ratio of productive/unproductive transcripts which arise from the TRG V-J gene rearrangement and for a "public" gamma delta TR repertoire. The productive cDNA sequences, shared both in the same and in different individuals, include biases of the TRGV1 and TRGJ2 genes. The high frequency of TRGV1-J2/TRDV1- D1-J4 productive rearrangements in dolphins may represent an interesting oligo-clonal population comparable to that found in human with the TRGV9- JP/TRDV2-D-J T cells and in primates. Although the features of the TRG and TRA/TRD loci organization reflect
Senetta, Rebecca; Duregon, Eleonora; Sonetto, Cristina; Spadi, Rossella; Mistrangelo, Massimiliano; Racca, Patrizia; Chiusa, Luigi; Munoz, Fernando H; Ricardi, Umberto; Arezzo, Alberto; Cassenti, Adele; Castellano, Isabella; Papotti, Mauro; Morino, Mario; Risio, Mauro; Cassoni, Paola
2015-01-01
Neoadjuvant chemo-radiotherapy (CRT) followed by surgical resection is the standard treatment for locally advanced rectal cancer, although complete tumor pathological regression is achieved in only up to 30% of cases. A clinicopathological and molecular predictive stratification of patients with advanced rectal cancer is still lacking. Here, c-Met and YKL-40 have been studied as putative predictors of CRT response in rectal cancer, due to their reported involvement in chemoradioresistance in various solid tumors. A multicentric study was designed to assess the role of c-Met and YKL-40 expression in predicting chemoradioresistance and to correlate clinical and pathological features with CRT response. Immunohistochemistry and fluorescent in situ hybridization for c-Met were performed on 81 rectal cancer biopsies from patients with locally advanced rectal adenocarcinoma. All patients underwent standard (50.4 gy in 28 fractions + concurrent capecitabine 825 mg/m2) neoadjuvant CRT or the XELOXART protocol. CRT response was documented on surgical resection specimens and recorded as tumor regression grade (TRG) according to the Mandard criteria. A significant correlation between c-Met and YKL-40 expression was observed (R = 0.43). The expressions of c-Met and YKL-40 were both significantly associated with a lack of complete response (86% and 87% of c-Met and YKL-40 positive cases, prectal cancer. Targeted therapy protocols could take advantage of prior evaluations of c-MET and YKL-40 expression levels to increase therapeutic efficacy.
Baum, Thierry-Pascal; Hierle, Vivien; Pasqual, Nicolas; Bellahcene, Fatena; Chaume, Denys; Lefranc, Marie-Paule; Jouvin-Marche, Evelyne; Marche, Patrice Noël; Demongeot, Jacques
2006-01-01
Background Adaptative immune repertoire diversity in vertebrate species is generated by recombination of variable (V), diversity (D) and joining (J) genes in the immunoglobulin (IG) loci of B lymphocytes and in the T cell receptor (TR) loci of T lymphocytes. These V-J and V-D-J gene rearrangements at the DNA level involve recombination signal sequences (RSS). Whereas many data exist, they are scattered in non specialized resources with different nomenclatures (eg. flat files) and are difficult to extract. Description IMGT/GeneInfo is an online information system that provides, through a user-friendly interface, exhaustive information resulting from the complex mechanisms of T cell receptor V-J and V-D-J recombinations. T cells comprise two populations which express the αβ and γδ TR, respectively. The first version of the system dealt with the Homo sapiens and Mus musculus TRA and TRB loci whose gene rearrangements allow the synthesis of the αβ TR chains. In this paper, we present the second version of IMGT/GeneInfo where we complete the database for the Homo sapiens and Mus musculus TRG and TRD loci along with the introduction of a quality control procedure for existing and new data. We also include new functionalities to the four loci analysis, giving, to date, a very informative tool which allows to work on V(D)J genes of all TR loci in both human and mouse species. IMGT/GeneInfo provides more than 59,000 rearrangement combinations with a full gene description which is freely available at . Conclusion IMGT/GeneInfo allows all TR information sequences to be in the same spot, and are now available within two computer-mouse clicks. This is useful for biologists and bioinformaticians for the study of T lymphocyte V(D)J gene rearrangements and their applications in immune response analysis. PMID:16640788
Directory of Open Access Journals (Sweden)
Jouvin-Marche Evelyne
2006-04-01
Full Text Available Abstract Background Adaptative immune repertoire diversity in vertebrate species is generated by recombination of variable (V, diversity (D and joining (J genes in the immunoglobulin (IG loci of B lymphocytes and in the T cell receptor (TR loci of T lymphocytes. These V-J and V-D-J gene rearrangements at the DNA level involve recombination signal sequences (RSS. Whereas many data exist, they are scattered in non specialized resources with different nomenclatures (eg. flat files and are difficult to extract. Description IMGT/GeneInfo is an online information system that provides, through a user-friendly interface, exhaustive information resulting from the complex mechanisms of T cell receptor V-J and V-D-J recombinations. T cells comprise two populations which express the αβ and γδ TR, respectively. The first version of the system dealt with the Homo sapiens and Mus musculus TRA and TRB loci whose gene rearrangements allow the synthesis of the αβ TR chains. In this paper, we present the second version of IMGT/GeneInfo where we complete the database for the Homo sapiens and Mus musculus TRG and TRD loci along with the introduction of a quality control procedure for existing and new data. We also include new functionalities to the four loci analysis, giving, to date, a very informative tool which allows to work on V(DJ genes of all TR loci in both human and mouse species. IMGT/GeneInfo provides more than 59,000 rearrangement combinations with a full gene description which is freely available at http://imgt.cines.fr/GeneInfo. Conclusion IMGT/GeneInfo allows all TR information sequences to be in the same spot, and are now available within two computer-mouse clicks. This is useful for biologists and bioinformaticians for the study of T lymphocyte V(DJ gene rearrangements and their applications in immune response analysis.
Spady, Richard; Stouli, Sami
2012-01-01
We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution f...
Lete, Bernard; Peereman, Ronald; Fayol, Michel
2008-01-01
We describe a large-scale regression study that examines the influence of lexical (word frequency, lexical neighborhood) and sublexical (feedforward and feedback consistency) variables on spelling accuracy among first, second, and third- to fifth-graders. The wordset analyzed contained 3430 French words. Predictors in the stepwise regression…
Directory of Open Access Journals (Sweden)
Lefranc Marie-Paule
2008-10-01
Full Text Available Abstract Background Nucleotides are trimmed from the ends of variable (V, diversity (D and joining (J genes during immunoglobulin (IG and T cell receptor (TR rearrangements in B cells and T cells of the immune system. This trimming is followed by addition of nucleotides at random, forming the N regions (N for nucleotides of the V-J and V-D-J junctions. These processes are crucial for creating diversity in the immune response since the number of trimmed nucleotides and the number of added nucleotides vary in each B or T cell. IMGT® sequence analysis tools, IMGT/V-QUEST and IMGT/JunctionAnalysis, are able to provide detailed and accurate analysis of the final observed junction nucleotide sequences (tool "output". However, as trimmed nucleotides can potentially be replaced by identical N region nucleotides during the process, the observed "output" represents a biased estimate of the "true trimming process." Results A probabilistic approach based on an analysis of the standardized tool "output" is proposed to infer the probability distribution of the "true trimmming process" and to provide plausible biological hypotheses explaining this process. We collated a benchmark dataset of TR alpha (TRA and TR gamma (TRG V-J rearranged sequences and junctions analysed with IMGT/V-QUEST and IMGT/JunctionAnalysis, the nucleotide sequence analysis tools from IMGT®, the international ImMunoGeneTics information system®, http://imgt.cines.fr. The standardized description of the tool output is based on the IMGT-ONTOLOGY axioms and concepts. We propose a simple first-order model that attempts to transform the observed "output" probability distribution into an estimate closer to the "true trimming process" probability distribution. We use this estimate to test the hypothesis that Poisson processes are involved in trimming. This hypothesis was not rejected at standard confidence levels for three of the four trimming processes: TRAV, TRAJ and TRGV. Conclusion By
Bleakley, Kevin; Lefranc, Marie-Paule; Biau, Gérard
2008-10-02
Nucleotides are trimmed from the ends of variable (V), diversity (D) and joining (J) genes during immunoglobulin (IG) and T cell receptor (TR) rearrangements in B cells and T cells of the immune system. This trimming is followed by addition of nucleotides at random, forming the N regions (N for nucleotides) of the V-J and V-D-J junctions. These processes are crucial for creating diversity in the immune response since the number of trimmed nucleotides and the number of added nucleotides vary in each B or T cell. IMGT sequence analysis tools, IMGT/V-QUEST and IMGT/JunctionAnalysis, are able to provide detailed and accurate analysis of the final observed junction nucleotide sequences (tool "output"). However, as trimmed nucleotides can potentially be replaced by identical N region nucleotides during the process, the observed "output" represents a biased estimate of the "true trimming process." A probabilistic approach based on an analysis of the standardized tool "output" is proposed to infer the probability distribution of the "true trimmming process" and to provide plausible biological hypotheses explaining this process. We collated a benchmark dataset of TR alpha (TRA) and TR gamma (TRG) V-J rearranged sequences and junctions analysed with IMGT/V-QUEST and IMGT/JunctionAnalysis, the nucleotide sequence analysis tools from IMGT, the international ImMunoGeneTics information system, http://imgt.cines.fr. The standardized description of the tool output is based on the IMGT-ONTOLOGY axioms and concepts. We propose a simple first-order model that attempts to transform the observed "output" probability distribution into an estimate closer to the "true trimming process" probability distribution. We use this estimate to test the hypothesis that Poisson processes are involved in trimming. This hypothesis was not rejected at standard confidence levels for three of the four trimming processes: TRAV, TRAJ and TRGV. By using trimming of rearranged TR genes as a benchmark, we
Zhang, Hongyang; Welch, William J.; Zamar, Ruben H.
2017-01-01
Tomal et al. (2015) introduced the notion of "phalanxes" in the context of rare-class detection in two-class classification problems. A phalanx is a subset of features that work well for classification tasks. In this paper, we propose a different class of phalanxes for application in regression settings. We define a "Regression Phalanx" - a subset of features that work well together for prediction. We propose a novel algorithm which automatically chooses Regression Phalanxes from high-dimensi...
Matson, Johnny L.; Kozlowski, Alison M.
2010-01-01
Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…
Olive, David J
2017-01-01
This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...
Differentiating regressed melanoma from regressed lichenoid keratosis.
Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A
2017-04-01
Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
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.…
DEFF Research Database (Denmark)
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...
Viganò, Luca; Capussotti, Lorenzo; De Rosa, Giovanni; De Saussure, Wassila Oulhaci; Mentha, Gilles; Rubbia-Brandt, Laura
2013-11-01
We analyzed the impact of chemotherapy-related liver injuries (CALI), pathological tumor regression grade (TRG), and micrometastases on long-term prognosis in patients undergoing liver resection for colorectal metastases after preoperative chemotherapy. CALI worsen the short-term outcomes of liver resection, but their impact on long-term prognosis is unknown. Recently, a prognostic role of TRG has been suggested. Micrometastases (microscopic vascular or biliary invasion) are reduced by preoperative chemotherapy, but their impact on survival is unclear. Patients undergoing liver resection for colorectal metastases between 1998 and 2011 and treated with oxaliplatin and/or irinotecan-based preoperative chemotherapy were eligible for the study. Patients with operative mortality or incomplete resection (R2) were excluded. All specimens were reviewed to assess CALI, TRG, and micrometastases. A total of 323 patients were included. Grade 2-3 sinusoidal obstruction syndrome (SOS) was present in 124 patients (38.4%), grade 2-3 steatosis in 73 (22.6%), and steatohepatitis in 30 (9.3%). Among all patients, 22.9% had TRG 1-2 (major response), whereas 55.7% had TRG 4-5 (no response). Microvascular invasion was detected in 37.8% of patients and microscopic biliary infiltration in 5.6%.The higher the SOS grade the lower the pathological response: TRG 1-2 occurred in 16.9% of patients with grade 2-3 SOS versus 26.6% of patients with grade 0-1 SOS (P = 0.032).After a median follow-up of 36.9 months, 5-year survival was 38.6%. CALI did not negatively impact survival. Multivariate analysis showed that grade 2-3 steatosis was associated with better survival than grade 0-1 steatosis (5-year survival rate of 52.5% vs 35.2%, P = 0.002). TRG better than the percentage of viable cells stratified patient prognosis: 5-year survival rate of 60.4% in TRG 1-2, 40.2% in TRG 3, and 29.8% in TRG 4-5 (P = 0.0001). Microscopic vascular and biliary invasion negatively impacted outcome (5-year survival
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
DEFF Research Database (Denmark)
Fitzenberger, Bernd; Wilke, Ralf Andreas
2015-01-01
if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......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 modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...
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...
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
Alternative Methods of Regression
Birkes, David
2011-01-01
Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s
Directory of Open Access Journals (Sweden)
Matthias Schmid
Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.
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 examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.
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
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-
Understanding poisson regression.
Hayat, Matthew J; Higgins, Melinda
2014-04-01
Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.
Directory of Open Access Journals (Sweden)
Mok Tik
2014-06-01
Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.
Multicollinearity and Regression Analysis
Daoud, Jamal I.
2017-12-01
In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.
DEFF Research Database (Denmark)
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....
DEFF Research Database (Denmark)
Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas
2017-01-01
In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface...... for predicting the covariate specific absolute risks, their confidence intervals, and their confidence bands based on right censored time to event data. We provide explicit formulas for our implementation of the estimator of the (stratified) baseline hazard function in the presence of tied event times. As a by...... functionals. The software presented here is implemented in the riskRegression package....
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.
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
Seber, George A F
2012-01-01
Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.
Ritz, Christian; Parmigiani, Giovanni
2009-01-01
R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. This book provides a coherent treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology.
Bayesian ARTMAP for regression.
Sasu, L M; Andonie, R
2013-10-01
Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification. The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons. Copyright © 2013 Elsevier Ltd. All rights reserved.
Bounded Gaussian process regression
DEFF Research Database (Denmark)
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...... with the proposed explicit noise-model extension....
and Multinomial Logistic Regression
African Journals Online (AJOL)
This work presented the results of an experimental comparison of two models: Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) for classifying students based on their academic performance. The predictive accuracy for each model was measured by their average Classification Correct Rate (CCR).
Mechanisms of neuroblastoma regression
Brodeur, Garrett M.; Bagatell, Rochelle
2014-01-01
Recent genomic and biological studies of neuroblastoma have shed light on the dramatic heterogeneity in the clinical behaviour of this disease, which spans from spontaneous regression or differentiation in some patients, to relentless disease progression in others, despite intensive multimodality therapy. This evidence also suggests several possible mechanisms to explain the phenomena of spontaneous regression in neuroblastomas, including neurotrophin deprivation, humoral or cellular immunity, loss of telomerase activity and alterations in epigenetic regulation. A better understanding of the mechanisms of spontaneous regression might help to identify optimal therapeutic approaches for patients with these tumours. Currently, the most druggable mechanism is the delayed activation of developmentally programmed cell death regulated by the tropomyosin receptor kinase A pathway. Indeed, targeted therapy aimed at inhibiting neurotrophin receptors might be used in lieu of conventional chemotherapy or radiation in infants with biologically favourable tumours that require treatment. Alternative approaches consist of breaking immune tolerance to tumour antigens or activating neurotrophin receptor pathways to induce neuronal differentiation. These approaches are likely to be most effective against biologically favourable tumours, but they might also provide insights into treatment of biologically unfavourable tumours. We describe the different mechanisms of spontaneous neuroblastoma regression and the consequent therapeutic approaches. PMID:25331179
International Nuclear Information System (INIS)
Yeo, Seung-Gu; Kim, Dae Yong; Kim, Tae Hyun; Jung, Kyung Hae; Hong, Yong Sang; Chang, Hee Jin; Park, Ji Won; Lim, Seok-Byung; Choi, Hyo Seong; Jeong, Seung-Yong
2010-01-01
Purpose: To determine whether the tumor volume reduction rate (TVRR) measured using three-dimensional region-of-interest magnetic resonance volumetry correlates with the pathologic tumor response after preoperative chemoradiotherapy (CRT) for locally advanced rectal cancer. Methods and Materials: The study included 405 patients with locally advanced rectal cancer (cT3-T4) who had undergone preoperative CRT and radical proctectomy. The tumor volume was measured using three-dimensional region-of-interest magnetic resonance volumetry before and after CRT but before surgery. We analyzed the correlation between the TVRR and the pathologic tumor response in terms of downstaging and tumor regression grade (TRG). Downstaging was defined as ypStage 0-I (ypT0-T2N0M0), and the TRG proposed by Dworak et al. was used. Results: The mean TVRR was 65.0% ± 22.3%. Downstaging and complete regression occurred in 167 (41.2%) and 58 (14.3%) patients, respectively. The TVRRs according to ypT classification (ypT0-T2 vs. ypT3-T4), ypN classification (ypN0 vs. ypN1-N2), downstaging (ypStage 0-I vs. ypStage II-III), good regression (TRG 3-4 vs. TRG 1-2), and complete regression (TRG 4 vs. TRG 1-3) were all significantly different (p 80%), the rates of ypT0-T2, ypN0, downstaging, and good regression were all significantly greater for patients with a TVRR of ≥60%, as was the complete regression rate for patients with a TVRR >80% (p <.05). Conclusion: The TVRR measured using three-dimensional region-of-interest magnetic resonance volumetry correlated significantly with the pathologic tumor response in terms of downstaging and TRG after preoperative CRT for locally advanced rectal cancer.
Ridge Regression Signal Processing
Kuhl, Mark R.
1990-01-01
The introduction of the Global Positioning System (GPS) into the National Airspace System (NAS) necessitates the development of Receiver Autonomous Integrity Monitoring (RAIM) techniques. In order to guarantee a certain level of integrity, a thorough understanding of modern estimation techniques applied to navigational problems is required. The extended Kalman filter (EKF) is derived and analyzed under poor geometry conditions. It was found that the performance of the EKF is difficult to predict, since the EKF is designed for a Gaussian environment. A novel approach is implemented which incorporates ridge regression to explain the behavior of an EKF in the presence of dynamics under poor geometry conditions. The basic principles of ridge regression theory are presented, followed by the derivation of a linearized recursive ridge estimator. Computer simulations are performed to confirm the underlying theory and to provide a comparative analysis of the EKF and the recursive ridge estimator.
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...
Better Autologistic Regression
Directory of Open Access Journals (Sweden)
Mark A. Wolters
2017-11-01
Full Text Available Autologistic regression is an important probability model for dichotomous random variables observed along with covariate information. It has been used in various fields for analyzing binary data possessing spatial or network structure. The model can be viewed as an extension of the autologistic model (also known as the Ising model, quadratic exponential binary distribution, or Boltzmann machine to include covariates. It can also be viewed as an extension of logistic regression to handle responses that are not independent. Not all authors use exactly the same form of the autologistic regression model. Variations of the model differ in two respects. First, the variable coding—the two numbers used to represent the two possible states of the variables—might differ. Common coding choices are (zero, one and (minus one, plus one. Second, the model might appear in either of two algebraic forms: a standard form, or a recently proposed centered form. Little attention has been paid to the effect of these differences, and the literature shows ambiguity about their importance. It is shown here that changes to either coding or centering in fact produce distinct, non-nested probability models. Theoretical results, numerical studies, and analysis of an ecological data set all show that the differences among the models can be large and practically significant. Understanding the nature of the differences and making appropriate modeling choices can lead to significantly improved autologistic regression analyses. The results strongly suggest that the standard model with plus/minus coding, which we call the symmetric autologistic model, is the most natural choice among the autologistic variants.
Regression in organizational leadership.
Kernberg, O F
1979-02-01
The choice of good leaders is a major task for all organizations. Inforamtion regarding the prospective administrator's personality should complement questions regarding his previous experience, his general conceptual skills, his technical knowledge, and the specific skills in the area for which he is being selected. The growing psychoanalytic knowledge about the crucial importance of internal, in contrast to external, object relations, and about the mutual relationships of regression in individuals and in groups, constitutes an important practical tool for the selection of leaders.
Classification and regression trees
Breiman, Leo; Olshen, Richard A; Stone, Charles J
1984-01-01
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Hilbe, Joseph M
2009-01-01
This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...
Energy Technology Data Exchange (ETDEWEB)
Kochhar, Rohit [The Christie NHS Foundation Trust, Department of Radiology, Manchester (United Kingdom); The University of Manchester, Manchester Academic Health Science Centre, Institute of Cancer Sciences, Manchester (United Kingdom); Renehan, Andrew G. [The University of Manchester, Manchester Academic Health Science Centre, Institute of Cancer Sciences, Manchester (United Kingdom); The Christie NHS Foundation Trust, Department of Surgery, Manchester (United Kingdom); Mullan, Damian; Carrington, Bernadette M. [The Christie NHS Foundation Trust, Department of Radiology, Manchester (United Kingdom); Chakrabarty, Bipasha [The Christie NHS Foundation Trust, Department of Histopathology, Manchester (United Kingdom); Saunders, Mark P. [The Christie NHS Foundation Trust, Department of Clinical Oncology, Manchester (United Kingdom)
2017-02-15
To assess the use of MRI-determined tumour regression grading (TRG) in local response assessment and detection of salvageable early local relapse after chemoradiotherapy (CRT) in patients with anal squamous cell carcinoma (ASCC). From a prospective database of patients with ASCC managed through a centralised multidisciplinary team, 74 patients who completed routine post-CRT 3- and 6-month MRIs (2009-2012) were reviewed. Two radiologists blinded to the outcomes consensus read and retrospectively assigned TRG scores [1 (complete response) to 5 (no response)] and related these to early local relapse (within 12 months) and disease-free survival (DFS). Seven patients had early local relapse. TRG 1/2 scores at 3 and 6 months had a 100 % negative predictive value; TRG 4/5 scores at 6 months had a 100 % positive predictive value. All seven patients underwent salvage R0 resections. We identified a novel 'tram-track' sign on MRI in over half of patients, with an NPV for early local relapse of 83 % at 6 months. No imaging characteristic or TRG score independently prognosticated for late relapse or 3-year DFS. Post-CRT 3- and 6-month MRI-determined TRG scores predicted salvageable R0 early local relapses in patients with ASCC, challenging current clinical guidelines. (orig.)
International Nuclear Information System (INIS)
Lim, Joon Seok; Baek, Song-Ee; Kim, Myeong-Jin; Suh, Jinsuk; Kim, Ki Whang; Kim, Daehong; Myoung, Sungmin; Choi, Junjeong; Shin, Sang Joon; Kim, Nam Kyu; Keum, Ki Chang
2012-01-01
To evaluate the utility of perfusion MRI as a potential biomarker for predicting response to chemoradiotherapy (CRT) in locally advanced rectal cancer. Thirty-nine patients with primary rectal carcinoma who were scheduled for preoperative CRT were prospectively recruited. Perfusion MRI was performed with a 3.0-T MRI system in all patients before therapy, at the end of the 2nd week of therapy, and before surgery. The K trans (volume transfer constant) and V e (extracellular extravascular space fraction) were calculated. Before CRT, the mean tumour K trans in the downstaged group was significantly higher than that in the non-downstaged group (P = 0.0178), but there was no significant difference between tumour regression grade (TRG) responders and TRG non-responders (P = 0.1392). Repeated-measures analysis of variance (ANOVA) showed significant differences for evolution of K trans values both between downstaged and non-downstaged groups (P = 0.0215) and between TRG responders and TRG non-responders (P = 0.0001). Regarding V e , no significant differences were observed both between downstaged and non-downstaged groups (P = 0.689) or between TRG responders and TRG non-responders (P = 0.887). Perfusion MRI of rectal cancer can be useful for assessing tumoural K trans changes by CRT. Tumours with high pre-CRT K trans values tended to respond favourably to CRT, particularly in terms of downstaging criteria. (orig.)
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.
SEPARATION PHENOMENA LOGISTIC REGRESSION
Directory of Open Access Journals (Sweden)
Ikaro Daniel de Carvalho Barreto
2014-03-01
Full Text Available This paper proposes an application of concepts about the maximum likelihood estimation of the binomial logistic regression model to the separation phenomena. It generates bias in the estimation and provides different interpretations of the estimates on the different statistical tests (Wald, Likelihood Ratio and Score and provides different estimates on the different iterative methods (Newton-Raphson and Fisher Score. It also presents an example that demonstrates the direct implications for the validation of the model and validation of variables, the implications for estimates of odds ratios and confidence intervals, generated from the Wald statistics. Furthermore, we present, briefly, the Firth correction to circumvent the phenomena of separation.
DEFF Research Database (Denmark)
Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas
2017-01-01
In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface......-product we obtain fast access to the baseline hazards (compared to survival::basehaz()) and predictions of survival probabilities, their confidence intervals and confidence bands. Confidence intervals and confidence bands are based on point-wise asymptotic expansions of the corresponding statistical...
Adaptive metric kernel regression
DEFF Research Database (Denmark)
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...
Adaptive Metric Kernel Regression
DEFF Research Database (Denmark)
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...
West, M A; Dimitrov, B D; Moyses, H E; Kemp, G J; Loughney, L; White, D; Grocott, M P W; Jack, S; Brown, G
2016-09-01
There is wide inter-institutional variation in the interval between neoadjuvant chemoradiotherapy (NACRT) and surgery for locally advanced rectal cancer. We aimed to assess the association of magnetic resonance imaging (MRI) at 9 and 14 weeks post-NACRT; T-staging (ymrT) and post-NACRT tumour regression grading (ymrTRG) with histopathological outcomes; histopathological T-stage (ypT) and histopathological tumour regression grading (ypTRG) in order to inform decision-making about timing of surgery. We prospectively studied 35 consecutive patients (26 males) with MRI-defined resection margin threatened rectal cancer who had completed standardized NACRT. Patients underwent a MRI at Weeks 9 and 14 post-NACRT, and surgery at Week 15. Two readers independently assessed MRIs for ymrT, ymrTRG and volume change. ymrT and ymrTRG were analysed against histopathological ypT and ypTRG as predictors by logistic regression modelling and receiver operating characteristic (ROC) curve analyses. Thirty-five patients were recruited. Inter-observer agreement was good for all MR variables (Kappa > 0.61). Considering ypT as an outcome variable, a stronger association of favourable ymrTRG and volume change at Week 14 compared to Week 9 was found (ymrTRG - p = 0.064 vs. p = 0.010; Volume change - p = 0.062 vs. p = 0.007). Similarly, considering ypTRG as an outcome variable, a greater association of favourable ymrTRG and volume change at Week 14 compared to Week 9 was found (ymrTRG - p = 0.005 vs. p = 0.042; Volume change - p = 0.004 vs. 0.055). Following NACRT, greater tumour down-staging and volume reduction was observed at Week 14. Timing of surgery, in relation to NACRT, merits further investigation. NCT01325909. Copyright © 2016 Elsevier Ltd. All rights reserved.
DEFF Research Database (Denmark)
Hansen, Henrik; Tarp, Finn
2001-01-01
This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy....... investment. We conclude by stressing the need for more theoretical work before this kind of cross-country regressions are used for policy purposes.......This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy...
Modified Regression Correlation Coefficient for Poisson Regression Model
Kaengthong, Nattacha; Domthong, Uthumporn
2017-09-01
This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).
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. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
The Causal Effects of Grade Retention on Behavioral Outcomes
Martorell, Paco; Mariano, Louis T.
2018-01-01
This study examines the impact of grade retention on behavioral outcomes under a comprehensive assessment-based student promotion policy in New York City. To isolate the causal effect of grade retention, we implement a fuzzy regression discontinuity (RD) design that exploits the fact that grade retention is largely determined by whether a student…
Polynomial regression analysis and significance test of the regression function
International Nuclear Information System (INIS)
Gao Zhengming; Zhao Juan; He Shengping
2012-01-01
In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)
MRI differentiation of low-grade from high-grade appendicular chondrosarcoma
International Nuclear Information System (INIS)
Douis, Hassan; Singh, Leanne; Saifuddin, Asif
2014-01-01
To identify magnetic resonance imaging (MRI) features which differentiate low-grade chondral lesions (atypical cartilaginous tumours/grade 1 chondrosarcoma) from high-grade chondrosarcomas (grade 2, grade 3 and dedifferentiated chondrosarcoma) of the major long bones. We identified all patients treated for central atypical cartilaginous tumours and central chondrosarcoma of major long bones (humerus, femur, tibia) over a 13-year period. The MRI studies were assessed for the following features: bone marrow oedema, soft tissue oedema, bone expansion, cortical thickening, cortical destruction, active periostitis, soft tissue mass and tumour length. The MRI-features were compared with the histopathological tumour grading using univariate, multivariate logistic regression and receiver operating characteristic curve (ROC) analyses. One hundred and seventy-nine tumours were included in this retrospective study. There were 28 atypical cartilaginous tumours, 79 grade 1 chondrosarcomas, 36 grade 2 chondrosarcomas, 13 grade 3 chondrosarcomas and 23 dedifferentiated chondrosarcomas. Multivariate analysis demonstrated that bone expansion (P = 0.001), active periostitis (P = 0.001), soft tissue mass (P < 0.001) and tumour length (P < 0.001) were statistically significant differentiating factors between low-grade and high-grade chondral lesions with an area under the ROC curve of 0.956. On MRI, bone expansion, active periostitis, soft tissue mass and tumour length can reliably differentiate high-grade chondrosarcomas from low-grade chondral lesions of the major long bones. (orig.)
MRI differentiation of low-grade from high-grade appendicular chondrosarcoma
Energy Technology Data Exchange (ETDEWEB)
Douis, Hassan; Singh, Leanne; Saifuddin, Asif [The Royal National Orthopaedic Hospital NHS Trust, Department of Radiology, Stanmore, Middlesex (United Kingdom)
2014-01-15
To identify magnetic resonance imaging (MRI) features which differentiate low-grade chondral lesions (atypical cartilaginous tumours/grade 1 chondrosarcoma) from high-grade chondrosarcomas (grade 2, grade 3 and dedifferentiated chondrosarcoma) of the major long bones. We identified all patients treated for central atypical cartilaginous tumours and central chondrosarcoma of major long bones (humerus, femur, tibia) over a 13-year period. The MRI studies were assessed for the following features: bone marrow oedema, soft tissue oedema, bone expansion, cortical thickening, cortical destruction, active periostitis, soft tissue mass and tumour length. The MRI-features were compared with the histopathological tumour grading using univariate, multivariate logistic regression and receiver operating characteristic curve (ROC) analyses. One hundred and seventy-nine tumours were included in this retrospective study. There were 28 atypical cartilaginous tumours, 79 grade 1 chondrosarcomas, 36 grade 2 chondrosarcomas, 13 grade 3 chondrosarcomas and 23 dedifferentiated chondrosarcomas. Multivariate analysis demonstrated that bone expansion (P = 0.001), active periostitis (P = 0.001), soft tissue mass (P < 0.001) and tumour length (P < 0.001) were statistically significant differentiating factors between low-grade and high-grade chondral lesions with an area under the ROC curve of 0.956. On MRI, bone expansion, active periostitis, soft tissue mass and tumour length can reliably differentiate high-grade chondrosarcomas from low-grade chondral lesions of the major long bones. (orig.)
Recursive Algorithm For Linear Regression
Varanasi, S. V.
1988-01-01
Order of model determined easily. Linear-regression algorithhm includes recursive equations for coefficients of model of increased order. Algorithm eliminates duplicative calculations, facilitates search for minimum order of linear-regression model fitting set of data satisfactory.
Teachers' Grading Decision Making
Isnawati, Ida; Saukah, Ali
2017-01-01
This study investigated teachers' grading decision making, focusing on their beliefs underlying their grading decision making, their grading practices and assessment types, and factors they considered in grading decision making. Two teachers from two junior high schools applying different curriculum policies in grade reporting in Indonesian…
Student Attitudes Toward Grades and Grading Practices.
Stallings, William M.; Leslie, Elwood K.
The result of a study designed to assess student attitudes toward grading practices are discussed. Questionnaire responses of 3439 students in three institutions were tabulated. Responses were generally negative toward conventional grading systems. (MS)
Combining Alphas via Bounded Regression
Directory of Open Access Journals (Sweden)
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 in autistic spectrum disorders.
Stefanatos, Gerry A
2008-12-01
A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.
Linear regression in astronomy. I
Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh
1990-01-01
Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.
Advanced statistics: linear regression, part I: simple linear regression.
Marill, Keith A
2004-01-01
Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.
Linear regression in astronomy. II
Feigelson, Eric D.; Babu, Gutti J.
1992-01-01
A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.
Time-adaptive quantile regression
DEFF Research Database (Denmark)
Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik
2008-01-01
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......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...... 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....
Retro-regression--another important multivariate regression improvement.
Randić, M
2001-01-01
We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.
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
Panel Smooth Transition Regression Models
DEFF Research Database (Denmark)
González, Andrés; Terasvirta, Timo; Dijk, Dick van
We introduce the panel smooth transition regression model. This new model is intended for characterizing heterogeneous panels, allowing the regression coefficients to vary both across individuals and over time. Specifically, heterogeneity is allowed for by assuming that these coefficients are bou...
Testing discontinuities in nonparametric regression
Dai, Wenlin
2017-01-19
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Testing discontinuities in nonparametric regression
Dai, Wenlin; Zhou, Yuejin; Tong, Tiejun
2017-01-01
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Logistic Regression: Concept and Application
Cokluk, Omay
2010-01-01
The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…
Fungible weights in logistic regression.
Jones, Jeff A; Waller, Niels G
2016-06-01
In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
International Nuclear Information System (INIS)
Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei
2007-01-01
Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age
The Stability of School Effectiveness Indices across Grade Levels and Subject Areas.
Mandeville, Garrett K.; Anderson, Lorin W.
1987-01-01
School effectiveness indices based on regressing achievement test scores onto earlier scores and a socioeconomic status measure were obtained for South Carolina students in grades one to four. Results were unstable across grades, and grade-to-grade correlations were more significant for mathematics achievement than for reading. (Author/GDC)
Tumor regression patterns in retinoblastoma
International Nuclear Information System (INIS)
Zafar, S.N.; Siddique, S.N.; Zaheer, N.
2016-01-01
To observe the types of tumor regression after treatment, and identify the common pattern of regression in our patients. Study Design: Descriptive study. Place and Duration of Study: Department of Pediatric Ophthalmology and Strabismus, Al-Shifa Trust Eye Hospital, Rawalpindi, Pakistan, from October 2011 to October 2014. Methodology: Children with unilateral and bilateral retinoblastoma were included in the study. Patients were referred to Pakistan Institute of Medical Sciences, Islamabad, for chemotherapy. After every cycle of chemotherapy, dilated funds examination under anesthesia was performed to record response of the treatment. Regression patterns were recorded on RetCam II. Results: Seventy-four tumors were included in the study. Out of 74 tumors, 3 were ICRB group A tumors, 43 were ICRB group B tumors, 14 tumors belonged to ICRB group C, and remaining 14 were ICRB group D tumors. Type IV regression was seen in 39.1% (n=29) tumors, type II in 29.7% (n=22), type III in 25.6% (n=19), and type I in 5.4% (n=4). All group A tumors (100%) showed type IV regression. Seventeen (39.5%) group B tumors showed type IV regression. In group C, 5 tumors (35.7%) showed type II regression and 5 tumors (35.7%) showed type IV regression. In group D, 6 tumors (42.9%) regressed to type II non-calcified remnants. Conclusion: The response and success of the focal and systemic treatment, as judged by the appearance of different patterns of tumor regression, varies with the ICRB grouping of the tumor. (author)
Regression to Causality : Regression-style presentation influences causal attribution
DEFF Research Database (Denmark)
Bordacconi, Mats Joe; Larsen, Martin Vinæs
2014-01-01
of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...
Regression analysis with categorized regression calibrated exposure: some interesting findings
Directory of Open Access Journals (Sweden)
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
Advanced statistics: linear regression, part II: multiple linear regression.
Marill, Keith A
2004-01-01
The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.
Logic regression and its extensions.
Schwender, Holger; Ruczinski, Ingo
2010-01-01
Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.
International Nuclear Information System (INIS)
Smith, Fraser M.; Reynolds, John V.; Kay, Elaine W.; Crotty, Paul; Murphy, James O.; Hollywood, Donal; Gaffney, Eoin F.; Stephens, Richard B.; Kennedy, M. John
2006-01-01
Purpose: To determine the utility of COX-2 expression as a response predictor for patients with rectal cancer who are undergoing neoadjuvant radiochemotherapy (RCT). Methods and Materials: Pretreatment biopsies (PTB) from 49 patients who underwent RCT were included. COX-2 and proliferation in PTB were assessed by immunohistochemistry (IHC) and apoptosis was detected by TUNEL stain. Response to treatment was assessed by a 5-point tumor-regression grade (TRG) based on the ratio of residual tumor to fibrosis. Results: Good response (TRG 1 + 2), moderate response (TRG 3), and poor response (TRG 4 + 5) were seen in 21 patients (42%), 11 patients (22%), and 17 patients (34%), respectively. Patients with COX-2 overexpression in PTB were more likely to demonstrate moderate or poor response (TRG 3 + 4) to treatment than were those with normal COX-2 expression (p = 0.026, chi-square test). Similarly, poor response was more likely if patients had low levels of spontaneous apoptosis in PTBs (p = 0.0007, chi-square test). Conclusions: COX-2 overexpression and reduced apoptosis in PTB can predict poor response of rectal cancer to RCT. As COX-2 inhibitors are commercially available, their administration to patients who overexpress COX-2 warrants assessment in clinical trials in an attempt to increase overall response rates
International Nuclear Information System (INIS)
Kim, Tae Hyun; Chang, Hee Jin; Kim, Dae Yong
2010-01-01
Purpose: We retrospectively evaluated the effects of clinical and pathologic factors on disease-free survival (DFS) with the aim of identifying the most discriminating factor predicting DFS in rectal cancer patients treated with preoperative chemoradiotherapy (CRT) and curative resection. Methods and Materials: The study involved 420 patients who underwent preoperative CRT and curative resection between August 2001 and October 2006. Gender, age, distance from the anal verge, histologic type, histologic grade, pretreatment carcinoembryonic antigen (CEA) level, cT, cN, cStage, circumferential resection margin, type of surgery, preoperative chemotherapy, adjuvant chemotherapy, ypT, ypN, ypStage, and tumor regression grade (TRG) were analyzed to identify prognostic factors associated with DFS. To compare the discriminatory prognostic ability of four tumor response-related pathologic factors (ypT, ypN, ypStage, and TRG), the Akaike information criteria were calculated. Results: The 5-year DFS rate was 75.4%. On univariate analysis, distance from the anal verge, histologic type, histologic grade, pretreatment CEA level, cT, circumferential resection margin, type of surgery, preoperative chemotherapeutic regimen, ypT, ypN, ypStage, and TRG were significantly associated with DFS. Multivariate analysis showed that the four parameters ypT, ypN, ypStage, and TRG were, consistently, significant prognostic factors for DFS. The ypN showed the lowest Akaike information criteria value for DFS, followed by ypStage, ypT, and TRG, in that order. Conclusion: In our study, ypT, ypN, ypStage, and TRG were important prognostic factors for DFS, and ypN was the most discriminating factor.
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.
Quantile Regression With Measurement Error
Wei, Ying; Carroll, Raymond J.
2009-01-01
. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a
Predicting Performance on MOOC Assessments using Multi-Regression Models
Ren, Zhiyun; Rangwala, Huzefa; Johri, Aditya
2016-01-01
The past few years has seen the rapid growth of data min- ing approaches for the analysis of data obtained from Mas- sive Open Online Courses (MOOCs). The objectives of this study are to develop approaches to predict the scores a stu- dent may achieve on a given grade-related assessment based on information, considered as prior performance or prior ac- tivity in the course. We develop a personalized linear mul- tiple regression (PLMR) model to predict the grade for a student, prior to attempt...
From Rasch scores to regression
DEFF Research Database (Denmark)
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....
Testing Heteroscedasticity in Robust Regression
Czech Academy of Sciences Publication Activity Database
Kalina, Jan
2011-01-01
Roč. 1, č. 4 (2011), s. 25-28 ISSN 2045-3345 Grant - others: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
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
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.
Financial Aid and First-Year Collegiate GPA: A Regression Discontinuity Approach
Curs, Bradley R.; Harper, Casandra E.
2012-01-01
Using a regression discontinuity design, we investigate whether a merit-based financial aid program has a causal effect on the first-year grade point average of first-time out-of-state freshmen at the University of Oregon. Our results indicate that merit-based financial aid has a positive and significant effect on first-year collegiate grade point…
Indian Academy of Sciences (India)
58
paper is devoted to the study of arbitrary rings graded through arbitrary sets. .... which recover certain multiplicative relations among the homogeneous components ... instance the case in which the grading set A is an Abelian group, where the ...
Graded manifolds and supermanifolds
International Nuclear Information System (INIS)
Batchelor, M.
1984-01-01
In this paper, a review is presented on graded manifolds and supermanifolds. Many theorems, propositions, corrollaries, etc. are given with proofs or sketch proofs. Graded manifolds, supereuclidian space, Lie supergroups, etc. are dealt with
DEFF Research Database (Denmark)
Welch, Vivian A; Akl, Elie A; Pottie, Kevin
2017-01-01
OBJECTIVE: The aim of this paper is to describe a conceptual framework for how to consider health equity in the GRADE (Grading Recommendations Assessment and Development Evidence) guideline development process. STUDY DESIGN AND SETTING: Consensus-based guidance developed by the GRADE working grou...
Logistic regression for dichotomized counts.
Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W
2016-12-01
Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.
International Nuclear Information System (INIS)
Appelt, Ane L.; Pløen, John; Vogelius, Ivan R.; Bentzen, Søren M.; Jakobsen, Anders
2013-01-01
Purpose: Preoperative chemoradiation therapy (CRT) is part of the standard treatment of locally advanced rectal cancers. Tumor regression at the time of operation is desirable, but not much is known about the relationship between radiation dose and tumor regression. In the present study we estimated radiation dose-response curves for various grades of tumor regression after preoperative CRT. Methods and Materials: A total of 222 patients, treated with consistent chemotherapy and radiation therapy techniques, were considered for the analysis. Radiation therapy consisted of a combination of external-beam radiation therapy and brachytherapy. Response at the time of operation was evaluated from the histopathologic specimen and graded on a 5-point scale (TRG1-5). The probability of achieving complete, major, and partial response was analyzed by ordinal logistic regression, and the effect of including clinical parameters in the model was examined. The radiation dose-response relationship for a specific grade of histopathologic tumor regression was parameterized in terms of the dose required for 50% response, D 50,i , and the normalized dose-response gradient, γ 50,i . Results: A highly significant dose-response relationship was found (P=.002). For complete response (TRG1), the dose-response parameters were D 50,TRG1 = 92.0 Gy (95% confidence interval [CI] 79.3-144.9 Gy), γ 50,TRG1 = 0.982 (CI 0.533-1.429), and for major response (TRG1-2) D 50,TRG1 and 2 = 72.1 Gy (CI 65.3-94.0 Gy), γ 50,TRG1 and 2 = 0.770 (CI 0.338-1.201). Tumor size and N category both had a significant effect on the dose-response relationships. Conclusions: This study demonstrated a significant dose-response relationship for tumor regression after preoperative CRT for locally advanced rectal cancer for tumor dose levels in the range of 50.4-70 Gy, which is higher than the dose range usually considered.
Producing The New Regressive Left
DEFF Research Database (Denmark)
Crone, Christine
members, this thesis investigates a growing political trend and ideological discourse in the Arab world that I have called The New Regressive Left. On the premise that a media outlet can function as a forum for ideology production, the thesis argues that an analysis of this material can help to trace...... the contexture of The New Regressive Left. If the first part of the thesis lays out the theoretical approach and draws the contextual framework, through an exploration of the surrounding Arab media-and ideoscapes, the second part is an analytical investigation of the discourse that permeates the programmes aired...... becomes clear from the analytical chapters is the emergence of the new cross-ideological alliance of The New Regressive Left. This emerging coalition between Shia Muslims, religious minorities, parts of the Arab Left, secular cultural producers, and the remnants of the political,strategic resistance...
What Did You Get? A Faculty Grade Comparison
Cavanaugh, Joseph K.
2006-01-01
Purpose: This study investigates how the increased use of part-time and nontenure-track instructors may result in grade inflation. Design/methodology/approach: This research uses ten years of registrar data at a Midwest State (USA) institution to perform a multiple regression grade analysis. Findings: Evidence is found that part-time and…
Evaluating the Quality of Transfer versus Nontransfer Accounting Principles Grades.
Colley, J. R.; And Others
1996-01-01
Using 1989-92 student records from three colleges accepting large numbers of transfers from junior schools into accounting, regression analyses compared grades of transfer and nontransfer students. Quality of accounting principle grades of transfer students was not equivalent to that of nontransfer students. (SK)
A Matlab program for stepwise regression
Directory of Open Access Journals (Sweden)
Yanhong Qi
2016-03-01
Full Text Available The stepwise linear regression is a multi-variable regression for identifying statistically significant variables in the linear regression equation. In present study, we presented the Matlab program of stepwise regression.
Correlation and simple linear regression.
Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G
2003-06-01
In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.
Regression filter for signal resolution
International Nuclear Information System (INIS)
Matthes, W.
1975-01-01
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.)
Nonparametric Mixture of Regression Models.
Huang, Mian; Li, Runze; Wang, Shaoli
2013-07-01
Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.
International Nuclear Information System (INIS)
Kerner, R.
1983-01-01
The mathematical background for a graded extension of gauge theories is investigated. After discussing the general properties of graded Lie algebras and what may serve as a model for a graded Lie group, the graded fiber bundle is constructed. Its basis manifold is supposed to be the so-called superspace, i.e. the product of the Minkowskian space-time with the Grassmann algebra spanned by the anticommuting Lorentz spinors; the vertical subspaces tangent to the fibers are isomorphic with the graded extension of the SU(N) Lie algebra. The connection and curvature are defined then on this bundle; the two different gradings are either independent of each other, or may be unified in one common grading, which is equivalent to the choice of the spin-statistics dependence. The Yang-Mills lagrangian is investigated in the simplified case. The conformal symmetry breaking is discussed, as well as some other physical consequences of the model. (orig.)
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…
Regression Models for Repairable Systems
Czech Academy of Sciences Publication Activity Database
Novák, Petr
2015-01-01
Roč. 17, č. 4 (2015), s. 963-972 ISSN 1387-5841 Institutional support: RVO:67985556 Keywords : Reliability analysis * Repair models * Regression Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.782, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/novak-0450902.pdf
Survival analysis II: Cox regression
Stel, Vianda S.; Dekker, Friedo W.; Tripepi, Giovanni; Zoccali, Carmine; Jager, Kitty J.
2011-01-01
In contrast to the Kaplan-Meier method, Cox proportional hazards regression can provide an effect estimate by quantifying the difference in survival between patient groups and can adjust for confounding effects of other variables. The purpose of this article is to explain the basic concepts of the
Kernel regression with functional response
Ferraty, Frédéric; Laksaci, Ali; Tadj, Amel; Vieu, Philippe
2011-01-01
We consider kernel regression estimate when both the response variable and the explanatory one are functional. The rates of uniform almost complete convergence are stated as function of the small ball probability of the predictor and as function of the entropy of the set on which uniformity is obtained.
What Are the Odds of that? A Primer on Understanding Logistic Regression
Huang, Francis L.; Moon, Tonya R.
2013-01-01
The purpose of this Methodological Brief is to present a brief primer on logistic regression, a commonly used technique when modeling dichotomous outcomes. Using data from the National Education Longitudinal Study of 1988 (NELS:88), logistic regression techniques were used to investigate student-level variables in eighth grade (i.e., enrolled in a…
Quantile Regression With Measurement Error
Wei, Ying
2009-08-27
Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. © 2009 American Statistical Association.
Multivariate and semiparametric kernel regression
Härdle, Wolfgang; Müller, Marlene
1997-01-01
The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is pro...
Regression algorithm for emotion detection
Berthelon , Franck; Sander , Peter
2013-01-01
International audience; We present here two components of a computational system for emotion detection. PEMs (Personalized Emotion Maps) store links between bodily expressions and emotion values, and are individually calibrated to capture each person's emotion profile. They are an implementation based on aspects of Scherer's theoretical complex system model of emotion~\\cite{scherer00, scherer09}. We also present a regression algorithm that determines a person's emotional feeling from sensor m...
Directional quantile regression in R
Czech Academy of Sciences Publication Activity Database
Boček, Pavel; Šiman, Miroslav
2017-01-01
Roč. 53, č. 3 (2017), s. 480-492 ISSN 0023-5954 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : multivariate quantile * regression quantile * halfspace depth * depth contour Subject RIV: BD - Theory of Information OBOR OECD: Applied mathematics Impact factor: 0.379, year: 2016 http://library.utia.cas.cz/separaty/2017/SI/bocek-0476587.pdf
Polylinear regression analysis in radiochemistry
International Nuclear Information System (INIS)
Kopyrin, A.A.; Terent'eva, T.N.; Khramov, N.N.
1995-01-01
A number of radiochemical problems have been formulated in the framework of polylinear regression analysis, which permits the use of conventional mathematical methods for their solution. The authors have considered features of the use of polylinear regression analysis for estimating the contributions of various sources to the atmospheric pollution, for studying irradiated nuclear fuel, for estimating concentrations from spectral data, for measuring neutron fields of a nuclear reactor, for estimating crystal lattice parameters from X-ray diffraction patterns, for interpreting data of X-ray fluorescence analysis, for estimating complex formation constants, and for analyzing results of radiometric measurements. The problem of estimating the target parameters can be incorrect at certain properties of the system under study. The authors showed the possibility of regularization by adding a fictitious set of data open-quotes obtainedclose quotes from the orthogonal design. To estimate only a part of the parameters under consideration, the authors used incomplete rank models. In this case, it is necessary to take into account the possibility of confounding estimates. An algorithm for evaluating the degree of confounding is presented which is realized using standard software or regression analysis
Gaussian Process Regression Model in Spatial Logistic Regression
Sofro, A.; Oktaviarina, A.
2018-01-01
Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.
International Nuclear Information System (INIS)
Scheunert, M.
1982-10-01
We develop a graded tensor calculus corresponding to arbitrary Abelian groups of degrees and arbitrary commutation factors. The standard basic constructions and definitions like tensor products, spaces of multilinear mappings, contractions, symmetrization, symmetric algebra, as well as the transpose, adjoint, and trace of a linear mapping, are generalized to the graded case and a multitude of canonical isomorphisms is presented. Moreover, the graded versions of the classical Lie algebras are introduced and some of their basic properties are described. (orig.)
Shaw, David D.; Pease, Leonard F., III.
2014-01-01
Grading can be accelerated to make time for more effective instruction. This article presents specific time management strategies selected to decrease administrative time required of faculty and teaching assistants, including a multiple answer multiple choice interface for exams, a three-tier grading system for open ended problem solving, and a…
Rendleman, Matt; Legacy, James
This publication provides an introduction to grain grading and handling for adult students in vocational and technical education programs. Organized in five chapters, the booklet provides a brief overview of the jobs performed at a grain elevator and of the techniques used to grade grain. The first chapter introduces the grain industry and…
Spontaneous regression of pulmonary bullae
International Nuclear Information System (INIS)
Satoh, H.; Ishikawa, H.; Ohtsuka, M.; Sekizawa, K.
2002-01-01
The natural history of pulmonary bullae is often characterized by gradual, progressive enlargement. Spontaneous regression of bullae is, however, very rare. We report a case in which complete resolution of pulmonary bullae in the left upper lung occurred spontaneously. The management of pulmonary bullae is occasionally made difficult because of gradual progressive enlargement associated with abnormal pulmonary function. Some patients have multiple bulla in both lungs and/or have a history of pulmonary emphysema. Others have a giant bulla without emphysematous change in the lungs. Our present case had treated lung cancer with no evidence of local recurrence. He had no emphysematous change in lung function test and had no complaints, although the high resolution CT scan shows evidence of underlying minimal changes of emphysema. Ortin and Gurney presented three cases of spontaneous reduction in size of bulla. Interestingly, one of them had a marked decrease in the size of a bulla in association with thickening of the wall of the bulla, which was observed in our patient. This case we describe is of interest, not only because of the rarity with which regression of pulmonary bulla has been reported in the literature, but also because of the spontaneous improvements in the radiological picture in the absence of overt infection or tumor. Copyright (2002) Blackwell Science Pty Ltd
Quantum algorithm for linear regression
Wang, Guoming
2017-07-01
We present a quantum algorithm for fitting a linear regression model to a given data set using the least-squares approach. Differently from previous algorithms which yield a quantum state encoding the optimal parameters, our algorithm outputs these numbers in the classical form. So by running it once, one completely determines the fitted model and then can use it to make predictions on new data at little cost. Moreover, our algorithm works in the standard oracle model, and can handle data sets with nonsparse design matrices. It runs in time poly( log2(N ) ,d ,κ ,1 /ɛ ) , where N is the size of the data set, d is the number of adjustable parameters, κ is the condition number of the design matrix, and ɛ is the desired precision in the output. We also show that the polynomial dependence on d and κ is necessary. Thus, our algorithm cannot be significantly improved. Furthermore, we also give a quantum algorithm that estimates the quality of the least-squares fit (without computing its parameters explicitly). This algorithm runs faster than the one for finding this fit, and can be used to check whether the given data set qualifies for linear regression in the first place.
Vascular grading of angiogenesis
DEFF Research Database (Denmark)
Hansen, S; Grabau, D A; Sørensen, Flemming Brandt
2000-01-01
The study aimed to evaluate the prognostic value of angiogenesis by vascular grading of primary breast tumours, and to evaluate the prognostic impact of adding the vascular grade to the Nottingham Prognostic Index (NPI). The investigation included 836 patients. The median follow-up time was 11...... years and 4 months. The microvessels were immunohistochemically stained by antibodies against CD34. Angiogenesis was graded semiquantitatively by subjective scoring into three groups according to the expected number of microvessels in the most vascular tumour area. The vascular grading between observers...... for 24% of the patients, who had a shift in prognostic group, as compared to NPI, and implied a better prognostic dissemination. We concluded that the angiogenesis determined by vascular grading has independent prognostic value of clinical relevance for patients with breast cancer....
Vascular grading of angiogenesis
DEFF Research Database (Denmark)
Hansen, S; Grabau, D A; Sørensen, Flemming Brandt
2000-01-01
The study aimed to evaluate the prognostic value of angiogenesis by vascular grading of primary breast tumours, and to evaluate the prognostic impact of adding the vascular grade to the Nottingham Prognostic Index (NPI). The investigation included 836 patients. The median follow-up time was 11...... years and 4 months. The microvessels were immunohistochemically stained by antibodies against CD34. Angiogenesis was graded semiquantitatively by subjective scoring into three groups according to the expected number of microvessels in the most vascular tumour area. The vascular grading between observers...... impact for 24% of the patients, who had a shift in prognostic group, as compared to NPI, and implied a better prognostic dissemination. We concluded that the angiogenesis determined by vascular grading has independent prognostic value of clinical relevance for patients with breast cancer....
Interpretation of commonly used statistical regression models.
Kasza, Jessica; Wolfe, Rory
2014-01-01
A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.
Determinants of LSIL Regression in Women from a Colombian Cohort
International Nuclear Information System (INIS)
Molano, Monica; Gonzalez, Mauricio; Gamboa, Oscar; Ortiz, Natasha; Luna, Joaquin; Hernandez, Gustavo; Posso, Hector; Murillo, Raul; Munoz, Nubia
2010-01-01
Objective: To analyze the role of Human Papillomavirus (HPV) and other risk factors in the regression of cervical lesions in women from the Bogota Cohort. Methods: 200 HPV positive women with abnormal cytology were included for regression analysis. The time of lesion regression was modeled using methods for interval censored survival time data. Median duration of total follow-up was 9 years. Results: 80 (40%) women were diagnosed with Atypical Squamous Cells of Undetermined Significance (ASCUS) or Atypical Glandular Cells of Undetermined Significance (AGUS) while 120 (60%) were diagnosed with Low Grade Squamous Intra-epithelial Lesions (LSIL). Globally, 40% of the lesions were still present at first year of follow up, while 1.5% was still present at 5 year check-up. The multivariate model showed similar regression rates for lesions in women with ASCUS/AGUS and women with LSIL (HR= 0.82, 95% CI 0.59-1.12). Women infected with HR HPV types and those with mixed infections had lower regression rates for lesions than did women infected with LR types (HR=0.526, 95% CI 0.33-0.84, for HR types and HR=0.378, 95% CI 0.20-0.69, for mixed infections). Furthermore, women over 30 years had a higher lesion regression rate than did women under 30 years (HR1.53, 95% CI 1.03-2.27). The study showed that the median time for lesion regression was 9 months while the median time for HPV clearance was 12 months. Conclusions: In the studied population, the type of infection and the age of the women are critical factors for the regression of cervical lesions.
International Nuclear Information System (INIS)
Lamas, Maria J.; Duran, Goretti; Gomez, Antonio; Balboa, Emilia; Anido, Urbano; Bernardez, Beatriz; Rana-Diez, Pablo; Lopez, Rafael; Carracedo, Angel; Barros, Francisco
2012-01-01
Purpose: 5-Fluorouracil–based chemoradiotherapy before total mesorectal excision is currently the standard treatment of Stage II and III rectal cancer patients. We used known predictive pharmacogenetic biomarkers to identify the responders to preoperative chemoradiotherapy in our series. Methods and Materials: A total of 93 Stage II-III rectal cancer patients were genotyped using peripheral blood samples. The genes analyzed were X-ray cross-complementing group 1 (XRCC1), ERCC1, MTHFR, EGFR, DPYD, and TYMS. The patients were treated with 225 mg/m 2 /d continuous infusion of 5-fluorouracil concomitantly with radiotherapy (50.4 Gy) followed by total mesorectal excision. The outcomes were measured by tumor regression grade (TRG) as a major response (TRG 1 and TRG 2) or as a poor response (TRG3, TRG4, and TRG5). Results: The major histopathologic response rate was 47.3%. XRCC1 G/G carriers had a greater probability of response than G/A carriers (odds ratio, 4.18; 95% confidence interval, 1.62–10.74, p = .003) Patients with polymorphisms associated with high expression of thymidylate synthase (2R/3G, 3C/3G, and 3G/3G) showed a greater pathologic response rate compared with carriers of low expression (odds ratio, 2.65; 95% confidence interval, 1.10–6.39, p = .02) No significant differences were seen in the response according to EGFR, ERCC1, MTHFR C 677 and MTHFR A 1298 expression. Conclusions: XRCC1 G/G and thymidylate synthase (2R/3G, 3C/3G, and 3G/3G) are independent factors of a major response. Germline thymidylate synthase and XRCC1 polymorphisms might be useful as predictive markers of rectal tumor response to neoadjuvant chemoradiotherapy with 5-fluorouracil.
Energy Technology Data Exchange (ETDEWEB)
Maffione, Anna Margherita [Santa Maria della Misericordia Hospital, Nuclear Medicine Department, PET Unit, Rovigo (Italy); Santa Maria della Misericordia Hospital, SOC Medicina Nucleare, Rovigo (Italy); Ferretti, Alice [Santa Maria della Misericordia Hospital, Nuclear Medicine Department, PET Unit, Rovigo (Italy); Santa Maria della Misericordia Hospital, Medical Physics Department, Rovigo (Italy); Grassetto, Gaia; Chondrogiannis, Sotirios; Marzola, Maria Cristina; Rampin, Lucia; Bondesan, Claudia; Rubello, Domenico [Santa Maria della Misericordia Hospital, Nuclear Medicine Department, PET Unit, Rovigo (Italy); Bellan, Elena; Gava, Marcello [Santa Maria della Misericordia Hospital, Medical Physics Department, Rovigo (Italy); Capirci, Carlo [Santa Maria della Misericordia Hospital, Radiotherapy Department, Rovigo (Italy); Colletti, Patrick M. [University of Southern California, Department of Radiology, Los Angeles, CA (United States)
2013-06-15
The aim of this study was to correlate qualitative visual response and various PET quantification factors with the tumour regression grade (TRG) classification of pathological response to neoadjuvant chemoradiotherapy (CRT) proposed by Mandard. Included in this retrospective study were 69 consecutive patients with locally advanced rectal cancer (LARC). FDG PET/CT scans were performed at staging and after CRT (mean 6.7 weeks). Tumour SUVmax and its related arithmetic and percentage decrease (response index, RI) were calculated. Qualitative analysis was performed by visual response assessment (VRA), PERCIST 1.0 and response cut-off classification based on a new definition of residual disease. Metabolic tumour volume (MTV) was calculated using a 40 % SUVmax threshold, and the total lesion glycolysis (TLG) both before and after CRT and their arithmetic and percentage change were also calculated. We split the patients into responders (TRG 1 or 2) and nonresponders (TRG 3-5). SUVmax MTV and TLG after CRT, RI, {Delta}MTV% and {Delta}TLG% parameters were significantly correlated with pathological treatment response (p < 0.01) with a ROC curve cut-off values of 5.1, 2.1 cm{sup 3}, 23.4 cm{sup 3}, 61.8 %, 81.4 % and 94.2 %, respectively. SUVmax after CRT had the highest ROC AUC (0.846), with a sensitivity of 86 % and a specificity of 80 %. VRA and response cut-off classification were also significantly predictive of TRG response (VRA with the best accuracy: sensitivity 86 % and specificity 55 %). In contrast, assessment using PERCIST was not significantly correlated with TRG. FDG PET/CT can accurately stratify patients with LARC preoperatively, independently of the method chosen to interpret the images. Among many PET parameters, some of which are not immediately obtainable, the most commonly used in clinical practice (SUVmax after CRT and VRA) showed the best accuracy in predicting TRG. (orig.)
International Nuclear Information System (INIS)
Maffione, Anna Margherita; Ferretti, Alice; Grassetto, Gaia; Chondrogiannis, Sotirios; Marzola, Maria Cristina; Rampin, Lucia; Bondesan, Claudia; Rubello, Domenico; Bellan, Elena; Gava, Marcello; Capirci, Carlo; Colletti, Patrick M.
2013-01-01
The aim of this study was to correlate qualitative visual response and various PET quantification factors with the tumour regression grade (TRG) classification of pathological response to neoadjuvant chemoradiotherapy (CRT) proposed by Mandard. Included in this retrospective study were 69 consecutive patients with locally advanced rectal cancer (LARC). FDG PET/CT scans were performed at staging and after CRT (mean 6.7 weeks). Tumour SUVmax and its related arithmetic and percentage decrease (response index, RI) were calculated. Qualitative analysis was performed by visual response assessment (VRA), PERCIST 1.0 and response cut-off classification based on a new definition of residual disease. Metabolic tumour volume (MTV) was calculated using a 40 % SUVmax threshold, and the total lesion glycolysis (TLG) both before and after CRT and their arithmetic and percentage change were also calculated. We split the patients into responders (TRG 1 or 2) and nonresponders (TRG 3-5). SUVmax MTV and TLG after CRT, RI, ΔMTV% and ΔTLG% parameters were significantly correlated with pathological treatment response (p 3 , 23.4 cm 3 , 61.8 %, 81.4 % and 94.2 %, respectively. SUVmax after CRT had the highest ROC AUC (0.846), with a sensitivity of 86 % and a specificity of 80 %. VRA and response cut-off classification were also significantly predictive of TRG response (VRA with the best accuracy: sensitivity 86 % and specificity 55 %). In contrast, assessment using PERCIST was not significantly correlated with TRG. FDG PET/CT can accurately stratify patients with LARC preoperatively, independently of the method chosen to interpret the images. Among many PET parameters, some of which are not immediately obtainable, the most commonly used in clinical practice (SUVmax after CRT and VRA) showed the best accuracy in predicting TRG. (orig.)
International Nuclear Information System (INIS)
Weiss, Christian; Arnold, Dirk; Dellas, Kathrin; Liersch, Torsten; Hipp, Matthias; Fietkau, Rainer; Sauer, Rolf; Hinke, Axel; Roedel, Claus
2010-01-01
Purpose: A pooled analysis of three prospective trials of preoperative radiochemotherapy (RCT) for rectal cancer by using oxaliplatin and capecitabine with or without cetuximab was performed to evaluate the impact of additional cetuximab on pathologic complete response (pCR) rates and tumor regression (TRG) grades. Methods and Materials: Of 202 patients, 172 patients met the inclusion criteria (primary tumor stage II/III, M0). All patients received concurrent RCT, and 46 patients received additional cetuximab therapy. A correlation of pretreatment clinicopathologic factors and cetuximab treatment with early pCR rates (TRG > 50%) was performed with univariate and multivariate analyses. Toxicity data were recorded for all patients. Results: Of 172 patients, 24 (14%) patients achieved a pCR, and 84 of 172 (71%) patients showed a TRG of >50% in the surgical specimen assessment after preoperative treatment. Age, gender, and T/N stages, as well as localization of the tumor, were not associated with pCR or good TRG. The pCR rate was 16% after preoperative RCT alone and 9% with concurrent cetuximab therapy (p = 0.32). A significantly reduced TRG of >50% was found after RCT with cetuximab compared to RCT alone (p = 0.0035). This was validated by a multivariate analysis with all available clinical factors (p = 0.0037). Acute toxicity and surgical complications were not increased with additional cetuximab. Conclusions: Triple therapy with RCT and cetuximab seems to be feasible, with no unexpected toxicity. Early response assessment (TRG), however, suggests subadditive interaction. A longer follow-up (and finally randomized trials) is needed to draw any firm conclusions with respect to local and distant failure rates.
Energy Technology Data Exchange (ETDEWEB)
Lamas, Maria J., E-mail: mlamasd@yahoo.es [Oncology Pharmacy Unit, Complejo Hospitalario Universitario of Santiago (CHUS), Choupana S/N, Santiago de Compostela (Spain); Duran, Goretti [Oncology Pharmacy Unit, Complejo Hospitalario Universitario of Santiago (CHUS), Choupana S/N, Santiago de Compostela (Spain); Gomez, Antonio [Department of Oncology Radiotherapy, Complejo Hospitalario Universitario of Santiago (CHUS), Choupana S/N, Santiago de Compostela (Spain); Balboa, Emilia [Molecular Medicine Unit, Fundacion Publica Galega de Medicina Xenomica, Choupana S/N, Santiago de Compostela (Spain); Anido, Urbano [Department of Medical Oncology, Complejo Hospitalario Universitario of Santiago (CHUS), Choupana S/N, Santiago de Compostela (Spain); Bernardez, Beatriz [Oncology Pharmacy Unit, Complejo Hospitalario Universitario of Santiago (CHUS), Choupana S/N, Santiago de Compostela (Spain); Rana-Diez, Pablo [Molecular Medicine Unit, Fundacion Publica Galega de Medicina Xenomica, Choupana S/N, Santiago de Compostela (Spain); Lopez, Rafael [Department of Medical Oncology, Complejo Hospitalario Universitario of Santiago (CHUS), Choupana S/N, Santiago de Compostela (Spain); Carracedo, Angel; Barros, Francisco [Fundacion Publica Galega de Medicina Xenomica and Genomic Medicine Group-CIBERER, University of Santiago de Compostela, Calle San Fransisco S/N, Santiago de Compostela (Spain)
2012-01-01
Purpose: 5-Fluorouracil-based chemoradiotherapy before total mesorectal excision is currently the standard treatment of Stage II and III rectal cancer patients. We used known predictive pharmacogenetic biomarkers to identify the responders to preoperative chemoradiotherapy in our series. Methods and Materials: A total of 93 Stage II-III rectal cancer patients were genotyped using peripheral blood samples. The genes analyzed were X-ray cross-complementing group 1 (XRCC1), ERCC1, MTHFR, EGFR, DPYD, and TYMS. The patients were treated with 225 mg/m{sup 2}/d continuous infusion of 5-fluorouracil concomitantly with radiotherapy (50.4 Gy) followed by total mesorectal excision. The outcomes were measured by tumor regression grade (TRG) as a major response (TRG 1 and TRG 2) or as a poor response (TRG3, TRG4, and TRG5). Results: The major histopathologic response rate was 47.3%. XRCC1 G/G carriers had a greater probability of response than G/A carriers (odds ratio, 4.18; 95% confidence interval, 1.62-10.74, p = .003) Patients with polymorphisms associated with high expression of thymidylate synthase (2R/3G, 3C/3G, and 3G/3G) showed a greater pathologic response rate compared with carriers of low expression (odds ratio, 2.65; 95% confidence interval, 1.10-6.39, p = .02) No significant differences were seen in the response according to EGFR, ERCC1, MTHFR{sub C}677 and MTHFR{sub A}1298 expression. Conclusions: XRCC1 G/G and thymidylate synthase (2R/3G, 3C/3G, and 3G/3G) are independent factors of a major response. Germline thymidylate synthase and XRCC1 polymorphisms might be useful as predictive markers of rectal tumor response to neoadjuvant chemoradiotherapy with 5-fluorouracil.
Prediction, Regression and Critical Realism
DEFF Research Database (Denmark)
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...... 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...... seen as necessary in order to identify aggregate level effects of policy measures, but are questioned by many advocates of critical realist ontology. Using research into the relationship between urban structure and travel as an example, the paper discusses relevant research methods and the kinds...
On Weighted Support Vector Regression
DEFF Research Database (Denmark)
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...... shrinks the coefficient of each observation in the estimated functions; thus, it is widely used for minimizing influence of outliers. We propose to additionally add weights to the slack variables in the constraints (CF‐weights) and call the combination of weights the doubly weighted SVR. We illustrate...... the differences and similarities of the two types of weights by demonstrating the connection between the Least Absolute Shrinkage and Selection Operator (LASSO) and the SVR. We show that an SVR problem can be transformed to a LASSO problem plus a linear constraint and a box constraint. We demonstrate...
Kobrin, Jennifer L.; Sinharay, Sandip; Haberman, Shelby J.; Chajewski, Michael
2011-01-01
This study examined the adequacy of a multiple linear regression model for predicting first-year college grade point average (FYGPA) using SAT[R] scores and high school grade point average (HSGPA). A variety of techniques, both graphical and statistical, were used to examine if it is possible to improve on the linear regression model. The results…
Nougaret, Stephanie; Fujii, Shinya; Addley, Helen C; Bibeau, Frederic; Pandey, Himanshu; Mikhael, Hisham; Reinhold, Caroline; Azria, David; Rouanet, Philippe; Gallix, Benoit
2013-09-01
To evaluate rectal cancer volumetry in predicting initial neoadjuvant chemotherapy response. Sixteen consecutive patients who underwent neoadjuvant chemotherapy (CX) before chemoradiotherapy (CRT) and surgery were enrolled in this retrospective study. Tumor volume was evaluated at the first magnetic resonance imaging (MRI), after CX and after CRT. Tumor volume regression (TVR) and downstaging were compared with histological results according to Tumor Regression Grade (TRG) to assess CX and CRT response, respectively. The mean tumor volume was 132 cm(3) ± 166 before and 56 cm(3) ± 71 after CX. TVR after CX was significantly different between patients with poor histologic response (TRG1/2) and those with good histologic response (TRG3/4) (P = 0.001). An optimal cutoff of TVR >68% (area under the curve [AUC]: 0.9, 95% confidence interval [CI]: 0.65-0.98, P = 0.0001) to predict good histology response after CX was assessed by receiver operating characteristic curve. According to previous data and this study, we defined 70% as the best cutoff values according to sensitivity (86%), specificity (100%) of TVR for predicting good histology response. In contradistinction, MRI downstaging was associated with TRG only after CRT (P = 0.04). Our pilot study showed that MRI volumetry can predict early histological response after CX and before CRT. MRI volumetry could help the clinician to distinguish early responders in order to aid appropriate individually tailored therapies. Copyright © 2013 Wiley Periodicals, Inc., a Wiley company.
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....
Kenneth H. Thomas
2000-01-01
Community Reinvestment Act of 1977 (CRA) ratings and performance evaluations are the only bank and thrift exam findings disclosed by financial institution regulators. Inflation of CRA ratings has been alleged by community activists for two decades, but there has been no quantification or empirical investigation of grade inflation. Using a unique grade inflation methodology on actual ratings and evaluation data for 1,407 small banks and thrifts under the revised CRA regulations, this paper con...
Mahamood, Rasheedat Modupe
2017-01-01
This book presents the concept of functionally graded materials as well as their use and different fabrication processes. The authors describe the use of additive manufacturing technology for the production of very complex parts directly from the three dimension computer aided design of the part by adding material layer after layer. A case study is also presented in the book on the experimental analysis of functionally graded material using laser metal deposition process.
Regularized Label Relaxation Linear Regression.
Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung; Fang, Bingwu
2018-04-01
Linear regression (LR) and some of its variants have been widely used for classification problems. Most of these methods assume that during the learning phase, the training samples can be exactly transformed into a strict binary label matrix, which has too little freedom to fit the labels adequately. To address this problem, in this paper, we propose a novel regularized label relaxation LR method, which has the following notable characteristics. First, the proposed method relaxes the strict binary label matrix into a slack variable matrix by introducing a nonnegative label relaxation matrix into LR, which provides more freedom to fit the labels and simultaneously enlarges the margins between different classes as much as possible. Second, the proposed method constructs the class compactness graph based on manifold learning and uses it as the regularization item to avoid the problem of overfitting. The class compactness graph is used to ensure that the samples sharing the same labels can be kept close after they are transformed. Two different algorithms, which are, respectively, based on -norm and -norm loss functions are devised. These two algorithms have compact closed-form solutions in each iteration so that they are easily implemented. Extensive experiments show that these two algorithms outperform the state-of-the-art algorithms in terms of the classification accuracy and running time.
Spatial vulnerability assessments by regression kriging
Pásztor, László; Laborczi, Annamária; Takács, Katalin; Szatmári, Gábor
2016-04-01
Two fairly different complex environmental phenomena, causing natural hazard were mapped based on a combined spatial inference approach. The behaviour is related to various environmental factors and the applied approach enables the inclusion of several, spatially exhaustive auxiliary variables that are available for mapping. Inland excess water (IEW) is an interrelated natural and human induced phenomenon causes several problems in the flat-land regions of Hungary, which cover nearly half of the country. The term 'inland excess water' refers to the occurrence of inundations outside the flood levee that originate from sources differing from flood overflow, it is surplus surface water forming due to the lack of runoff, insufficient absorption capability of soil or the upwelling of groundwater. There is a multiplicity of definitions, which indicate the complexity of processes that govern this phenomenon. Most of the definitions have a common part, namely, that inland excess water is temporary water inundation that occurs in flat-lands due to both precipitation and groundwater emerging on the surface as substantial sources. Radon gas is produced in the radioactive decay chain of uranium, which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on soil physical and meteorological parameters and can enter and accumulate in the buildings. Health risk originating from indoor radon concentration attributed to natural factors is characterized by geogenic radon potential (GRP). In addition to geology and meteorology, physical soil properties play significant role in the determination of GRP. Identification of areas with high risk requires spatial modelling, that is mapping of specific natural hazards. In both cases external environmental factors determine the behaviour of the target process (occurrence/frequncy of IEW and grade of GRP respectively). Spatial auxiliary
Principal component regression analysis with SPSS.
Liu, R X; Kuang, J; Gong, Q; Hou, X L
2003-06-01
The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.
Unbalanced Regressions and the Predictive Equation
DEFF Research Database (Denmark)
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 theoreti......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...
Semiparametric regression during 2003–2007
Ruppert, David; Wand, M.P.; Carroll, Raymond J.
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.
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
Regression Analysis by Example. 5th Edition
Chatterjee, Samprit; Hadi, Ali S.
2012-01-01
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 and thoroughly…
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…
A Seemingly Unrelated Poisson Regression Model
King, Gary
1989-01-01
This article introduces a new estimator for the analysis of two contemporaneously correlated endogenous event count variables. This seemingly unrelated Poisson regression model (SUPREME) estimator combines the efficiencies created by single equation Poisson regression model estimators and insights from "seemingly unrelated" linear regression models.
Krishnan, Chethan; Raju, Avinash
2018-04-01
We note that large classes of contractions of algebras that arise in physics can be understood purely algebraically via identifying appropriate Zm-gradings (and their generalizations) on the parent algebra. This includes various types of flat space/Carroll limits of finite and infinite dimensional (A)dS algebras, as well as Galilean and Galilean conformal algebras. Our observations can be regarded as providing a natural context for the Grassmann approach of Krishnan et al. [J. High Energy Phys. 2014(3), 36]. We also introduce a related notion, which we call partial grading, that arises naturally in this context.
Photodynamic therapy of Cervical Intraepithelial Neoplasia (CIN) high grade
Carbinatto, Fernanda M.; Inada, Natalia M.; Lombardi, Welington; da Silva, Eduardo V.; Belotto, Renata; Kurachi, Cristina; Bagnato, Vanderlei S.
2016-02-01
Cervical intraepithelial neoplasia (CIN) is the precursor of invasive cervical cancer and associated with human papillomavirus (HPV) infection. Photodynamic therapy (PDT) is a technique that has been used for the treatment of tumors. PDT is based on the accumulation of a photosensitizer in target cells that will generate cytotoxic reactive oxygen species upon illumination, inducing the death of abnormal tissue and PDT with less damaging to normal tissues than surgery, radiation, or chemotherapy and seems to be a promising alternative procedure for CIN treatment. The CIN high grades (II and III) presents potential indications for PDT due the success of PDT for CIN low grade treatment. The patients with CIN high grade that were treated with new clinic protocol shows lesion regression to CIN low grade 60 days after the treatment. The new clinical protocol using for treatment of CIN high grade shows great potential to become a public health technique.
De Martini, Paolo; Ceresoli, Marco; Mari, Giulio M.; Costanzi, Andrea; Maggioni, Dario; Pugliese, Raffaele; Ferrari, Giovanni
2017-01-01
Background To verify the prognostic value of the pathologic and radiological tumor response after neoadjuvant chemotherapy in the treatment of locally advanced gastric adenocarcinoma. Methods A total of 67 patients with locally advanced gastric cancer (clinical ≥ T2 or nodal disease and without evidence of distant metastases) underwent perioperative chemotherapy (ECF or ECX regimen) from December 2009 through June 2015 in two surgical units. Histopathological and radiological response to chemotherapy were evaluated by using tumor regression grade (TRG) (Becker’s criteria) and volume change assessed by CT. Results Fifty-one (86%) patients completed all chemotherapy scheduled cycles successfully and surgery was curative (R0) in 64 (97%) subjects. The histopathological analysis showed 19 (29%) specimens with TRG1 (less than 10% of vital tumor left) and 25 (37%) patients had partial or complete response (CR) assessed by CT scan. Median disease free survival (DFS) and overall survival (OS) were 25.70 months (range, 14.52–36.80 months) and 36.60 months (range, 24.3–52.9 months), respectively. The median follow up was 27 months (range, 5.00–68.00 months). Radiological response and TRG were found to be a prognostic factor for OS and DFS, while tumor histology was not significantly related to survival. Conclusions Both radiological response and TRG have been shown as promising survival markers in patients treated with perioperative chemotherapy for locally advanced gastric cancer. Other predictive markers of response to chemotherapy are strongly required. PMID:29299362
Regression with Sparse Approximations of Data
DEFF Research Database (Denmark)
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...
Spontaneous regression of a congenital melanocytic nevus
Directory of Open Access Journals (Sweden)
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.
Endangered Animals. Second Grade.
Popp, Marcia
This second grade teaching unit centers on endangered animal species around the world. Questions addressed are: What is an endangered species? Why do animals become extinct? How do I feel about the problem? and What can I do? Students study the definition of endangered species and investigate whether it is a natural process. They explore topics…
Allswang, John M.
1986-01-01
This article provides two short microcomputer gradebook programs. The programs, written in BASIC for the IBM-PC and Apple II, provide statistical information about class performance and calculate grades either on a normal distribution or based on teacher-defined break points. (JDH)
Grant, Darren
2007-01-01
We determine how much observed student performance in microeconomics principles can be attributed, inferentially, to three kinds of student academic "productivity," the instructor, demographics, and unmeasurables. The empirical approach utilizes an ordered probit model that relates student performance in micro to grades in prior…
First Grade Baseline Evaluation
Center for Innovation in Assessment (NJ1), 2013
2013-01-01
The First Grade Baseline Evaluation is an optional tool that can be used at the beginning of the school year to help teachers get to know the reading and language skills of each student. The evaluation is composed of seven screenings. Teachers may use the entire evaluation or choose to use those individual screenings that they find most beneficial…
Hartman, Michael; And Others
An interdisciplinary design project report investigates the relationship of the fifth grade educational facility to the student and teacher needs in light of human and environmental factors. The classroom, activity and teaching spaces are analyzed with regard to the educational curriculum. Specifications and design criteria concerning equipment…
Taylor, Lewis A., III
2012-01-01
An accessible business school population of undergraduate students was investigated in three independent, but related studies to determine effects on grades due to cutting class and failing to take advantage of optional reviews and study quizzes. It was hypothesized that cutting classes harms exam scores, attending preexam reviews helps exam…
Peer Victimization in Fifth Grade and Health in Tenth Grade
Elliott, Marc N.; Klein, David J.; Tortolero, Susan R.; Mrug, Sylvie; Peskin, Melissa F.; Davies, Susan L.; Schink, Elizabeth T.; Schuster, Mark A.
2014-01-01
BACKGROUND AND OBJECTIVES: Children who experience bullying, a type of peer victimization, show worse mental and physical health cross-sectionally. Few studies have assessed these relationships longitudinally. We examined longitudinal associations of bullying with mental and physical health from elementary to high school, comparing effects of different bullying histories. METHODS: We analyzed data from 4297 children surveyed at 3 time points (fifth, seventh, and tenth grades) in 3 cities. We used multivariable regressions to test longitudinal associations of bullying with mental and physical health by comparing youth who experienced bullying in both the past and present, experienced bullying in the present only, experienced bullying in the past only, or did not experience bullying. RESULTS: Bullying was associated with worse mental and physical health, greater depression symptoms, and lower self-worth over time. Health was significantly worse for children with both past and present bullying experiences, followed by children with present-only experiences, children with past-only experiences, and children with no experiences. For example, 44.6% of children bullied in both the past and present were at the lowest decile of psychosocial health, compared with 30.7% of those bullied in the present only (P = .005), 12.1% of those bullied in the past only (P bullied (P bullying are associated with substantially worse health. Clinicians who recognize bullying when it first starts could intervene to reverse the downward health trajectory experienced by youth who are repeated targets. PMID:24534401
Role of cytologic grading in prognostication of invasive breast carcinoma
Directory of Open Access Journals (Sweden)
Khan Nazoora
2009-01-01
Full Text Available Background: Evaluation of cytologic features is indispensable in the preoperative diagnosis and grading of infiltrating ductal breast carcinoma (CA in fine-needle aspiration cytology (FNAC material and this method can also provide additional information regarding intrinsic features of the tumor as well as its prognosis. Aim: This study has been done to evaluate comparatively the cytologic and histomorphologic grading of infiltrating ductal carcinoma of breast with specific reference to lymph node metastasis and its role in prognostication. Materials and Methods: Forty three patients who underwent FNAC and mastectomy for infiltrating ductal carcinoma were cytologically and histologically graded (employing Robinson′s cytologic grading system and Elston′s modification of Bloom-Richardson system, respectively. Statistical analysis was done employing ′z′ test and c2 test to compare the two grading system and to examine the degree of correlation between the cytologic and histologic grades. Multiple regression analysis was done to assess the significance of every cytologic and histologic parameter. All 43 cases, graded cyto-histologically were also evaluated for presence or absence of metastasis to the regional lymph nodes employing c2 test. Results: With histologic grade taken as the standard, cytology was found to be fairly comparable, for grading breast carcinoma (overall sensitivity 89.1%, specificity 100%. Further comparison of the two grading systems by Z-test showed that difference between the cytologic and histologic grading was insignificant in all the three grade (p > 0.05. Of the six parameters studied, cell dissociation, nucleoli and chromatin pattern were the most influential features (p < 0.001. The statistically significant difference (p < 0.001 was found in incidences of axillary lymph node metastatic rate in three cytologic grades (15.4% in grade I vs. 83.3% in grade III as well. Conclusions: Apart from being simple and
Strecht, Pedro; Cruz, Luís; Soares, Carlos; Mendes-Moreira, João; Abreu, Rui
2015-01-01
Predicting the success or failure of a student in a course or program is a problem that has recently been addressed using data mining techniques. In this paper we evaluate some of the most popular classification and regression algorithms on this problem. We address two problems: prediction of approval/failure and prediction of grade. The former is…
International Nuclear Information System (INIS)
Avallone, Antonio; Casaretti, Rossana; Montano, Massimo; Silvestro, Lucrezia; Aloj, Luigi; Caraco, Corradina; Di Gennaro, Francesca; Lastoria, Secondo; Delrio, Paolo; Pecori, Biagio; Tatangelo, Fabiana; Scott, Nigel; Budillon, Alfredo
2012-01-01
The aim of the present study is to prospectively evaluate the prognostic value of previously defined [ 18 F]2-fluoro-2-deoxy-D-glucose positron emission tomography (FDG PET) criteria of early metabolic response in patients with locally advanced rectal cancer (LARC) after long-term follow-up. Forty-two patients with poor prognosis LARC underwent three biweekly courses of chemotherapy with oxaliplatin, raltitrexed and 5-fluorouracil modulated by levofolinic acid during pelvic radiotherapy. FDG PET studies were performed before and 12 days after the beginning of the chemoradiotherapy (CRT) treatment. Total mesorectal excision (TME) was carried out 8 weeks after completion of CRT. A previously identified cutoff value of ≥52 % reduction of the baseline mean FDG standardized uptake value (SUV mean ) was applied to differentiate metabolic responders from non-responders and correlated to tumour regression grade (TRG) and survival. Twenty-two metabolic responders showed complete (TRG1) or subtotal tumour regression (TRG2) and demonstrated a statistically significantly higher 5-year relapse-free survival (RFS) compared with the 20 non-responders (86 vs 55 %, p =.014) who showed TRG3 and TRG4 pathologic responses. A multivariate analysis demonstrated that early ∇SUV mean was the only pre-surgical parameter correlated to the likelihood of recurrence (p =.05). This study is the first prospective long-term evaluation demonstrating that FDG PET is not only an early predictor of pathologic response but is also a valuable prognostic tool. Our results indicate the potential of FDG PET for optimizing multidisciplinary management of patients with LARC. (orig.)
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...
Regression models of reactor diagnostic signals
International Nuclear Information System (INIS)
Vavrin, J.
1989-01-01
The application is described of an autoregression model as the simplest regression model of diagnostic signals in experimental analysis of diagnostic systems, in in-service monitoring of normal and anomalous conditions and their diagnostics. The method of diagnostics is described using a regression type diagnostic data base and regression spectral diagnostics. The diagnostics is described of neutron noise signals from anomalous modes in the experimental fuel assembly of a reactor. (author)
Bulcock, J. W.
The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…
Multivariate Regression Analysis and Slaughter Livestock,
AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY
[From clinical judgment to linear regression model.
Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O
2013-01-01
When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.
Inflated Grades, Enrollments & Budgets
Directory of Open Access Journals (Sweden)
J. E. Stone
1995-06-01
Full Text Available Reports of the past 13 years that call attention to deficient academic standards in American higher education are enumerated. Particular attention is given the Wingspread Group's recent An American Imperative: Higher Expectations for Higher Education. Low academic standards, grade inflation, and budgetary incentives for increased enrollment are analyzed and a call is made for research at the state level. Reported trends in achievement and GPAs are extrapolated to Tennessee and combined with local data to support the inference that 15% of the state's present day college graduates would not have earned a diploma by mid 1960s standards. A conspicuous lack of interest by public oversight bodies is noted despite a growing public awareness of low academic expectations and lenient grading and an implicit budgetary impact of over $100 million. Various academic policies and the dynamics of bureaucratic control are discussed in relationship to the maintenance of academic standards. The disincentives for challenging course requirements and responsible grading are examined, and the growing movement to address academic quality issues through better training and supervision of faculty are critiqued. Recommendations that would encourage renewed academic integrity and make learning outcomes visible to students, parents, employers, and the taxpaying public are offered and briefly discussed.
Toptaş, Tayfun; Peştereli, Elif; Bozkurt, Selen; Erdoğan, Gülgün; Şimşek, Tayup
2018-03-01
To examine correlations among nuclear, architectural, and International Federation of Gynecology and Obstetrics (FIGO) grading systems, and their relationships with lymph node (LN) involvement in endometrioid endometrial cancer. Histopathology slides of 135 consecutive patients were reviewed with respect to tumor grade and LN metastasis. Notable nuclear atypia was defined as grade 3 nuclei. FIGO grade was established by raising the architectural grade (AG) by one grade when the tumor was composed of cells with nuclear grade (NG) 3. Correlations between the grading systems were analyzed using Spearman's rank correlation coefficients, and relationships of grading systems with LN involvement were assessed using logistic regression analysis. Correlation analysis revealed a significant and strongly positive relationship between FIGO and architectural grading systems (r=0.885, p=0.001); however, correlations of nuclear grading with the architectural (r=0.535, p=0.165) and FIGO grading systems (r=0.589, p=0.082) were moderate and statistically non-significant. Twenty-five (18.5%) patients had LN metastasis. LN involvement rates differed significantly between tumors with AG 1 and those with AG 2, and tumors with FIGO grade 1 and those with FIGO grade 2. In contrast, although the difference in LN involvement rates failed to reach statistical significance between tumors with NG 1 and those with NG 2, it was significant between NG 2 and NG 3 (p=0.042). Although all three grading systems were associated with LN involvement in univariate analyses, an independent relationship could not be established after adjustment for other confounders in multivariate analysis. Nuclear grading is significantly correlated with neither architectural nor FIGO grading systems. The differences in LN involvement rates in the nuclear grading system reach significance only in the setting of tumor cells with NG 3; however, none of the grading systems was an independent predictor of LN involvement.
TCGA researchers analyzed nearly 300 cases of diffuse low- and intermediate-grade gliomas, which together comprise lower-grade gliomas. LGGs occur mainly in adults and include astrocytomas, oligodendrogliomas and oligoastrocytomas.
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,
Histopathologic grading of anaplasia in retinoblastoma.
Mendoza, Pia R; Specht, Charles S; Hubbard, G Baker; Wells, Jill R; Lynn, Michael J; Zhang, Qing; Kong, Jun; Grossniklaus, Hans E
2015-04-01
To determine whether the degree of tumor anaplasia has prognostic value by evaluating its correlation with high-risk histopathologic features and clinical outcomes in a series of retinoblastoma patients. Retrospective clinicopathologic study. The clinical and pathologic findings in 266 patients who underwent primary enucleation for retinoblastoma were reviewed. The histologic degree of anaplasia was graded as retinocytoma, mild, moderate, or severe as defined by increasing cellular pleomorphism, number of mitoses, nuclear size, and nuclear hyperchromatism. Nuclear morphometric characteristics were measured. The clinical and pathologic data of 125 patients were compared using Kaplan-Meier estimates of survival. Fisher exact test and multivariate regression were used to analyze the association between anaplasia grade and high-risk histologic features. Increasing grade of anaplasia was associated with decreased overall survival (P = .003) and increased risk of metastasis (P = .0007). Histopathologic features that were associated with anaplasia included optic nerve invasion (P anaplasia grading as predictors of distant metastasis and death showed that high-risk histopathology was statistically significant as an independent predictor (P = .01 for metastasis, P = .03 for death) but anaplasia was not (P = .63 for metastasis, P = .30 for death). In the absence of high-risk features, however, severe anaplasia identified an additional risk for metastasis (P = .0004) and death (P = .01). Grading of anaplasia may be a useful adjunct to standard histopathologic criteria in identifying retinoblastoma patients who do not have high-risk histologic features but still have an increased risk of metastasis and may need adjuvant therapy. Copyright © 2015 Elsevier Inc. All rights reserved.
Edwards, Clifford H.; Edwards, Laurie
1999-01-01
Argues that grades have negative effects on learning and self-concept. States that while grading has a long tradition of sorting children for college entrance, there is limited evidence that grades serve a valid purpose. Argues that this practice should be abolished and an evaluation system established that provides a more valid estimate of…
Guskey, Thomas R.; Jung, Lee Ann
2012-01-01
The field of education is moving rapidly toward a standards-based approach to grading. School leaders have become increasingly aware of the tremendous variation that exists in grading practices, even among teachers of the same courses in the same department in the same school. Consequently, students' grades often have little relation to their…
RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,
This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)
A Simulation Investigation of Principal Component Regression.
Allen, David E.
Regression analysis is one of the more common analytic tools used by researchers. However, multicollinearity between the predictor variables can cause problems in using the results of regression analyses. Problems associated with multicollinearity include entanglement of relative influences of variables due to reduced precision of estimation,…
Hierarchical regression analysis in structural Equation Modeling
de Jong, P.F.
1999-01-01
In a hierarchical or fixed-order regression analysis, the independent variables are entered into the regression equation in a prespecified order. Such an analysis is often performed when the extra amount of variance accounted for in a dependent variable by a specific independent variable is the main
Categorical regression dose-response modeling
The goal of this training is to provide participants with training on the use of the U.S. EPA’s Categorical Regression soft¬ware (CatReg) and its application to risk assessment. Categorical regression fits mathematical models to toxicity data that have been assigned ord...
Variable importance in latent variable regression models
Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.
2014-01-01
The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable
Stepwise versus Hierarchical Regression: Pros and Cons
Lewis, Mitzi
2007-01-01
Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…
Suppression Situations in Multiple Linear Regression
Shieh, Gwowen
2006-01-01
This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…
Gibrat’s law and quantile regressions
DEFF Research Database (Denmark)
Distante, Roberta; Petrella, Ivan; Santoro, Emiliano
2017-01-01
The nexus between firm growth, size and age in U.S. manufacturing is examined through the lens of quantile regression models. This methodology allows us to overcome serious shortcomings entailed by linear regression models employed by much of the existing literature, unveiling a number of important...
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.
Repeated Results Analysis for Middleware Regression Benchmarking
Czech Academy of Sciences Publication Activity Database
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
Principles of Quantile Regression and an Application
Chen, Fang; Chalhoub-Deville, Micheline
2014-01-01
Newer statistical procedures are typically introduced to help address the limitations of those already in practice or to deal with emerging research needs. Quantile regression (QR) is introduced in this paper as a relatively new methodology, which is intended to overcome some of the limitations of least squares mean regression (LMR). QR is more…
ON REGRESSION REPRESENTATIONS OF STOCHASTIC-PROCESSES
RUSCHENDORF, L; DEVALK, [No Value
We construct a.s. nonlinear regression representations of general stochastic processes (X(n))n is-an-element-of N. As a consequence we obtain in particular special regression representations of Markov chains and of certain m-dependent sequences. For m-dependent sequences we obtain a constructive
Regression of environmental noise in LIGO data
International Nuclear Information System (INIS)
Tiwari, V; Klimenko, S; Mitselmakher, G; Necula, V; Drago, M; Prodi, G; Frolov, V; Yakushin, I; Re, V; Salemi, F; Vedovato, G
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 GW channel from the PEM measurements. One of the most promising regression methods is based on the construction of Wiener–Kolmogorov (WK) filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the WK 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 present the first results on regression of the bi-coherent noise in the LIGO data. (paper)
Pathological assessment of liver fibrosis regression
Directory of Open Access Journals (Sweden)
WANG Bingqiong
2017-03-01
Full Text Available Hepatic fibrosis is the common pathological outcome of chronic hepatic diseases. An accurate assessment of fibrosis degree provides an important reference for a definite diagnosis of diseases, treatment decision-making, treatment outcome monitoring, and prognostic evaluation. At present, many clinical studies have proven that regression of hepatic fibrosis and early-stage liver cirrhosis can be achieved by effective treatment, and a correct evaluation of fibrosis regression has become a hot topic in clinical research. Liver biopsy has long been regarded as the gold standard for the assessment of hepatic fibrosis, and thus it plays an important role in the evaluation of fibrosis regression. This article reviews the clinical application of current pathological staging systems in the evaluation of fibrosis regression from the perspectives of semi-quantitative scoring system, quantitative approach, and qualitative approach, in order to propose a better pathological evaluation system for the assessment of fibrosis regression.
Should metacognition be measured by logistic regression?
Rausch, Manuel; Zehetleitner, Michael
2017-03-01
Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria. Copyright © 2017 Elsevier Inc. All rights reserved.
Quality of life in breast cancer patients--a quantile regression analysis.
Pourhoseingholi, Mohamad Amin; Safaee, Azadeh; Moghimi-Dehkordi, Bijan; Zeighami, Bahram; Faghihzadeh, Soghrat; Tabatabaee, Hamid Reza; Pourhoseingholi, Asma
2008-01-01
Quality of life study has an important role in health care especially in chronic diseases, in clinical judgment and in medical resources supplying. Statistical tools like linear regression are widely used to assess the predictors of quality of life. But when the response is not normal the results are misleading. The aim of this study is to determine the predictors of quality of life in breast cancer patients, using quantile regression model and compare to linear regression. A cross-sectional study conducted on 119 breast cancer patients that admitted and treated in chemotherapy ward of Namazi hospital in Shiraz. We used QLQ-C30 questionnaire to assessment quality of life in these patients. A quantile regression was employed to assess the assocciated factors and the results were compared to linear regression. All analysis carried out using SAS. The mean score for the global health status for breast cancer patients was 64.92+/-11.42. Linear regression showed that only grade of tumor, occupational status, menopausal status, financial difficulties and dyspnea were statistically significant. In spite of linear regression, financial difficulties were not significant in quantile regression analysis and dyspnea was only significant for first quartile. Also emotion functioning and duration of disease statistically predicted the QOL score in the third quartile. The results have demonstrated that using quantile regression leads to better interpretation and richer inference about predictors of the breast cancer patient quality of life.
Regression modeling of ground-water flow
Cooley, R.L.; Naff, R.L.
1985-01-01
Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)
Variable and subset selection in PLS regression
DEFF Research Database (Denmark)
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...
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
Automated Essay Grading using Machine Learning Algorithm
Ramalingam, V. V.; Pandian, A.; Chetry, Prateek; Nigam, Himanshu
2018-04-01
Essays are paramount for of assessing the academic excellence along with linking the different ideas with the ability to recall but are notably time consuming when they are assessed manually. Manual grading takes significant amount of evaluator’s time and hence it is an expensive process. Automated grading if proven effective will not only reduce the time for assessment but comparing it with human scores will also make the score realistic. The project aims to develop an automated essay assessment system by use of machine learning techniques by classifying a corpus of textual entities into small number of discrete categories, corresponding to possible grades. Linear regression technique will be utilized for training the model along with making the use of various other classifications and clustering techniques. We intend to train classifiers on the training set, make it go through the downloaded dataset, and then measure performance our dataset by comparing the obtained values with the dataset values. We have implemented our model using java.
International Nuclear Information System (INIS)
Barbaro, Brunella; Vitale, Renata; Valentini, Vincenzo; Illuminati, Sonia; Vecchio, Fabio M.; Rizzo, Gianluca; Gambacorta, Maria Antonietta; Coco, Claudio; Crucitti, Antonio; Persiani, Roberto; Sofo, Luigi; Bonomo, Lorenzo
2012-01-01
Purpose: To prospectively monitor the response in patients with locally advanced nonmucinous rectal cancer after chemoradiotherapy (CRT) using diffusion-weighted magnetic resonance imaging. The histopathologic finding was the reference standard. Methods and Materials: The institutional review board approved the present study. A total of 62 patients (43 men and 19 women; mean age, 64 years; range, 28–83) provided informed consent. T 2 - and diffusion-weighted magnetic resonance imaging scans (b value, 0 and 1,000 mm 2 /s) were acquired before, during (mean 12 days), and 6–8 weeks after CRT. We compared the median apparent diffusion coefficients (ADCs) between responders and nonresponders and examined the associations with the Mandard tumor regression grade (TRG). The postoperative nodal status (ypN) was evaluated. The Mann-Whitney/Wilcoxon two-sample test was used to evaluate the relationships among the pretherapy ADCs, extramural vascular invasion, early percentage of increases in ADCs, and preoperative ADCs. Results: Low pretreatment ADCs ( −3 mm 2 /s) were correlated with TRG 4 scores (p = .0011) and associated to extramural vascular invasion with ypN+ (85.7% positive predictive value for ypN+). During treatment, the mean percentage of increase in tumor ADC was significantly greater in the responders than in the nonresponders (p 23% ADC increase had a 96.3% negative predictive value for TRG 4. In 9 of 16 complete responders, CRT-related tumor downsizing prevented ADC evaluations. The preoperative ADCs were significantly different (p = .0012) between the patients with and without downstaging (preoperative ADC ≥1.4 × 10 −3 mm 2 /s showed a positive and negative predictive value of 78.9% and 61.8%, respectively, for response assessment). The TRG 1 and TRG 2–4 groups were not significantly different. Conclusion: Diffusion-weighted magnetic resonance imaging seems to be a promising tool for monitoring the response to CRT.
Affix Meaning Knowledge in First Through Third Grade Students.
Apel, Kenn; Henbest, Victoria Suzanne
2016-04-01
We examined grade-level differences in 1st- through 3rd-grade students' performance on an experimenter-developed affix meaning task (AMT) and determined whether AMT performance explained unique variance in word-level reading and reading comprehension, beyond other known contributors to reading development. Forty students at each grade level completed an assessment battery that included measures of phonological awareness, receptive vocabulary, word-level reading, reading comprehension, and affix meaning knowledge. On the AMT, 1st-grade students were significantly less accurate than 2nd- and 3rd-grade students; there was no significant difference in performance between the 2nd- and 3rd-grade students. Regression analyses revealed that the AMT accounted for 8% unique variance of students' performance on word-level reading measures and 6% unique variance of students' performance on the reading comprehension measure, after age, phonological awareness, and receptive vocabulary were explained. These results provide initial information on the development of affix meaning knowledge via an explicit measure in 1st- through 3rd-grade students and demonstrate that affix meaning knowledge uniquely contributes to the development of reading abilities above other known literacy predictors. These findings provide empirical support for how students might use morphological problem solving to read unknown multimorphemic words successfully.
Vectors, a tool in statistical regression theory
Corsten, L.C.A.
1958-01-01
Using linear algebra this thesis developed linear regression analysis including analysis of variance, covariance analysis, special experimental designs, linear and fertility adjustments, analysis of experiments at different places and times. The determination of the orthogonal projection, yielding
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 ...
Dynamic travel time estimation using regression trees.
2008-10-01
This report presents a methodology for travel time estimation by using regression trees. The dissemination of travel time information has become crucial for effective traffic management, especially under congested road conditions. In the absence of c...
Two Paradoxes in Linear Regression Analysis
FENG, Ge; PENG, Jing; TU, Dongke; ZHENG, Julia Z.; FENG, Changyong
2016-01-01
Summary Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection. PMID:28638214
Discriminative Elastic-Net Regularized Linear Regression.
Zhang, Zheng; Lai, Zhihui; Xu, Yong; Shao, Ling; Wu, Jian; Xie, Guo-Sen
2017-03-01
In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zero-one matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of these methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available data sets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html.
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.
Computing multiple-output regression quantile regions
Czech Academy of Sciences Publication Activity Database
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
There is No Quantum Regression Theorem
International Nuclear Information System (INIS)
Ford, G.W.; OConnell, R.F.
1996-01-01
The Onsager regression hypothesis states that the regression of fluctuations is governed by macroscopic equations describing the approach to equilibrium. It is here asserted that this hypothesis fails in the quantum case. This is shown first by explicit calculation for the example of quantum Brownian motion of an oscillator and then in general from the fluctuation-dissipation theorem. It is asserted that the correct generalization of the Onsager hypothesis is the fluctuation-dissipation theorem. copyright 1996 The American Physical Society
Caudal regression syndrome : a case report
International Nuclear Information System (INIS)
Lee, Eun Joo; Kim, Hi Hye; Kim, Hyung Sik; Park, So Young; Han, Hye Young; Lee, Kwang Hun
1998-01-01
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
Energy Technology Data Exchange (ETDEWEB)
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.
Spontaneous regression of metastatic Merkel cell carcinoma.
LENUS (Irish Health Repository)
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.
Forecasting exchange rates: a robust regression approach
Preminger, Arie; Franck, Raphael
2005-01-01
The least squares estimation method as well as other ordinary estimation method for regression models can be severely affected by a small number of outliers, thus providing poor out-of-sample forecasts. This paper suggests a robust regression approach, based on the S-estimation method, to construct forecasting models that are less sensitive to data contamination by outliers. A robust linear autoregressive (RAR) and a robust neural network (RNN) models are estimated to study the predictabil...
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.
Marginal longitudinal semiparametric regression via penalized splines
Al Kadiri, M.; Carroll, R.J.; Wand, M.P.
2010-01-01
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.
Post-processing through linear regression
van Schaeybroeck, B.; Vannitsem, S.
2011-03-01
Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.
Post-processing through linear regression
Directory of Open Access Journals (Sweden)
B. Van Schaeybroeck
2011-03-01
Full Text Available Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS method, a new time-dependent Tikhonov regularization (TDTR method, the total least-square method, a new geometric-mean regression (GM, a recently introduced error-in-variables (EVMOS method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified.
These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise. At long lead times the regression schemes (EVMOS, TDTR which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.
Unbalanced Regressions and the Predictive Equation
DEFF Research Database (Denmark)
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 theoreti......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 suggest...
Energy Technology Data Exchange (ETDEWEB)
Dang, Jun; Li, Guang; Ma, Lianghua; Han, Chong; Zhang, Shuo; Yao, Lei [Dept. of Radiation Oncology, The First Hospital of China Medical Univ., Shenyang (China)], e-mail: gl1963516@yahoo.cn; Diao, Rao [Dept. of Experimental Technology Center, China Medical Univ., Shenyang (China); Zang, Shuang [Dept. of Nursing, China Medical Univ., Shenyang (China)
2013-08-15
Grade {>=}3 radiation pneumonitis (RP) is generally severe and life-threatening. Predictors of grade {>=}2 are usually used for grade {>=}3 RP prediction, but it is unclear whether these predictors are appropriate. In this study, predictors of grade {>=}2 and grade {>=}3 RP were investigated separately. The increased risk of severe RP in elderly patients compared with younger patients was also evaluated. Material and methods: A total of 176 consecutive patients with locally advanced non-small cell lung cancer were followed up prospectively after three-dimensional conformal radiotherapy. RP was graded according to Common Terminology Criteria for Adverse Events version 3.0. Results: Mean lung dose (MLD), mean heart dose, ratio of planning target volume to total lung volume (PTV/Lung), and dose-volume histogram comprehensive value of both heart and lung were associated with both grade {>=}2 and grade {>=}3 RP in univariate analysis. In multivariate logistic regression analysis, age and MLD were predictors of both grade {>=}2 RP and grade {>=}3 RP; receipt of chemotherapy predicted grade {>=}3 RP only; and sex and PTV/Lung predicted grade {>=}2 RP only. Among patients who developed high-grade RP, MLD and PTV/Lung were significantly lower in patients aged {>=}70 years than in younger patients (p<0.05 for both comparisons). Conclusions: The predictors were not completely consistent between grade {>=}2 RP and grade {>=}3 RP. Elderly patients had a higher risk of severe RP than younger patients did, possibly due to lower tolerance of radiation to the lung.
Functionally Graded Materials Database
Kisara, Katsuto; Konno, Tomomi; Niino, Masayuki
2008-02-01
Functionally Graded Materials Database (hereinafter referred to as FGMs Database) was open to the society via Internet in October 2002, and since then it has been managed by the Japan Aerospace Exploration Agency (JAXA). As of October 2006, the database includes 1,703 research information entries with 2,429 researchers data, 509 institution data and so on. Reading materials such as "Applicability of FGMs Technology to Space Plane" and "FGMs Application to Space Solar Power System (SSPS)" were prepared in FY 2004 and 2005, respectively. The English version of "FGMs Application to Space Solar Power System (SSPS)" is now under preparation. This present paper explains the FGMs Database, describing the research information data, the sitemap and how to use it. From the access analysis, user access results and users' interests are discussed.
An Exploratory Study of Face-to-Face and Cyberbullying in Sixth Grade Students
Accordino, Denise B.; Accordino, Michael P.
2011-01-01
In a pilot study, sixth grade students (N = 124) completed a questionnaire assessing students' experience with bullying and cyberbullying, demographic information, quality of parent-child relationship, and ways they have dealt with bullying/cyberbullying in the past. Two multiple regression analyses were conducted. The multiple regression analysis…
The Implications of Grade Inflation
DEFF Research Database (Denmark)
Smith, David E.; Fleisher, Steven
2011-01-01
The authors review current and past practices of the grade inflation controversy and present ways to return to each institution’s established grading guidelines. Students are graded based on knowledge gathered. Certain faculty members use thorough evaluative methods, such as written and oral pres...... have been profiled in the news. The model is provided to ensure that degree candidates are academic experts in their field, having earned the credential through rigorous study....
Prostate malignancy grading using gland-related shape descriptors
Braumann, Ulf-Dietrich; Scheibe, Patrick; Loeffler, Markus; Kristiansen, Glen; Wernert, Nicolas
2014-03-01
A proof-of-principle study was accomplished assessing the descriptive potential of two simple geometric measures (shape descriptors) applied to sets of segmented glands within images of 125 prostate cancer tissue sections. Respective measures addressing glandular shapes were (i) inverse solidity and (ii) inverse compactness. Using a classifier based on logistic regression, Gleason grades 3 and 4/5 could be differentiated with an accuracy of approx. 95%. Results suggest not only good discriminatory properties, but also robustness against gland segmentation variations. False classifications in part were caused by inadvertent Gleason grade assignments, as a-posteriori re-inspections had turned out.
Regression analysis using dependent Polya trees.
Schörgendorfer, Angela; Branscum, Adam J
2013-11-30
Many commonly used models for linear regression analysis force overly simplistic shape and scale constraints on the residual structure of data. We propose a semiparametric Bayesian model for regression analysis that produces data-driven inference by using a new type of dependent Polya tree prior to model arbitrary residual distributions that are allowed to evolve across increasing levels of an ordinal covariate (e.g., time, in repeated measurement studies). By modeling residual distributions at consecutive covariate levels or time points using separate, but dependent Polya tree priors, distributional information is pooled while allowing for broad pliability to accommodate many types of changing residual distributions. We can use the proposed dependent residual structure in a wide range of regression settings, including fixed-effects and mixed-effects linear and nonlinear models for cross-sectional, prospective, and repeated measurement data. A simulation study illustrates the flexibility of our novel semiparametric regression model to accurately capture evolving residual distributions. In an application to immune development data on immunoglobulin G antibodies in children, our new model outperforms several contemporary semiparametric regression models based on a predictive model selection criterion. Copyright © 2013 John Wiley & Sons, Ltd.
Is past life regression therapy ethical?
Andrade, Gabriel
2017-01-01
Past life regression therapy is used by some physicians in cases with some mental diseases. Anxiety disorders, mood disorders, and gender dysphoria have all been treated using life regression therapy by some doctors on the assumption that they reflect problems in past lives. Although it is not supported by psychiatric associations, few medical associations have actually condemned it as unethical. In this article, I argue that past life regression therapy is unethical for two basic reasons. First, it is not evidence-based. Past life regression is based on the reincarnation hypothesis, but this hypothesis is not supported by evidence, and in fact, it faces some insurmountable conceptual problems. If patients are not fully informed about these problems, they cannot provide an informed consent, and hence, the principle of autonomy is violated. Second, past life regression therapy has the great risk of implanting false memories in patients, and thus, causing significant harm. This is a violation of the principle of non-malfeasance, which is surely the most important principle in medical ethics.
An endoscopic mucosal grading system is predictive of leak in stapled rectal anastomoses.
Sujatha-Bhaskar, Sarath; Jafari, Mehraneh D; Hanna, Mark; Koh, Christina Y; Inaba, Colette S; Mills, Steven D; Carmichael, Joseph C; Nguyen, Ninh T; Stamos, Michael J; Pigazzi, Alessio
2018-04-01
Anastomotic leak is a devastating postoperative complication following rectal anastomoses associated with significant clinical and oncological implications. As a result, there is a need for novel intraoperative methods that will help predict anastomotic leak. From 2011 to 2014, patient undergoing rectal anastomoses by colorectal surgeons at our institution underwent prospective application of intraoperative flexible endoscopy with mucosal grading. Retrospective review of patient medical records was performed. After creation of the colorectal anastomosis, application of a three-tier endoscopic mucosal grading system occurred. Grade 1 was defined as circumferentially normal appearing peri-anastomotic mucosa. Grade 2 was defined as ischemia or congestion involving 30% of the colon or rectal mucosa or ischemia/congestion involving both sides of the staple line. From 2011 to 2014, a total of 106 patients were reviewed. Grade 1 anastomoses were created in 92 (86.7%) patients and Grade 2 anastomoses were created in 10 (9.4%) patients. All 4 (3.8%) Grade 3 patients underwent immediate intraoperative anastomosis takedown and re-creation, with subsequent re-classification as Grade 1. Demographic and comorbidity data were similar between Grade 1 and Grade 2 patients. Anastomotic leak rate for the entire cohort was 12.2%. Grade 1 patients demonstrated a leak rate of 9.4% (9/96) and Grade 2 patients demonstrated a leak rate of 40% (4/10). Multivariate logistic regression associated Grade 2 classification with an increased risk of anastomotic leak (OR 4.09, 95% CI 1.21-13.63, P = 0.023). Endoscopic mucosal grading is a feasible intraoperative technique that has a role following creation of a rectal anastomosis. Identification of a Grade 2 or Grade 3 anastomosis should provoke strong consideration for immediate intraoperative revision.
On Solving Lq-Penalized Regressions
Directory of Open Access Journals (Sweden)
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.
Refractive regression after laser in situ keratomileusis.
Yan, Mabel K; Chang, John Sm; Chan, Tommy Cy
2018-04-26
Uncorrected refractive errors are a leading cause of visual impairment across the world. In today's society, laser in situ keratomileusis (LASIK) has become the most commonly performed surgical procedure to correct refractive errors. However, regression of the initially achieved refractive correction has been a widely observed phenomenon following LASIK since its inception more than two decades ago. Despite technological advances in laser refractive surgery and various proposed management strategies, post-LASIK regression is still frequently observed and has significant implications for the long-term visual performance and quality of life of patients. This review explores the mechanism of refractive regression after both myopic and hyperopic LASIK, predisposing risk factors and its clinical course. In addition, current preventative strategies and therapies are also reviewed. © 2018 Royal Australian and New Zealand College of Ophthalmologists.
Influence diagnostics in meta-regression model.
Shi, Lei; Zuo, ShanShan; Yu, Dalei; Zhou, Xiaohua
2017-09-01
This paper studies the influence diagnostics in meta-regression model including case deletion diagnostic and local influence analysis. We derive the subset deletion formulae for the estimation of regression coefficient and heterogeneity variance and obtain the corresponding influence measures. The DerSimonian and Laird estimation and maximum likelihood estimation methods in meta-regression are considered, respectively, to derive the results. Internal and external residual and leverage measure are defined. The local influence analysis based on case-weights perturbation scheme, responses perturbation scheme, covariate perturbation scheme, and within-variance perturbation scheme are explored. We introduce a method by simultaneous perturbing responses, covariate, and within-variance to obtain the local influence measure, which has an advantage of capable to compare the influence magnitude of influential studies from different perturbations. An example is used to illustrate the proposed methodology. Copyright © 2017 John Wiley & Sons, Ltd.
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 ...
Regression Models for Market-Shares
DEFF Research Database (Denmark)
Birch, Kristina; Olsen, Jørgen Kai; Tjur, Tue
2005-01-01
On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put on the interpretat......On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put...... on the interpretation of the parameters in relation to models for the total sales based on discrete choice models.Key words and phrases. MCI model, discrete choice model, market-shares, price elasitcity, regression model....
On directional multiple-output quantile regression
Czech Academy of Sciences Publication Activity Database
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 - others: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
Removing Malmquist bias from linear regressions
Verter, Frances
1993-01-01
Malmquist bias is present in all astronomical surveys where sources are observed above an apparent brightness threshold. Those sources which can be detected at progressively larger distances are progressively more limited to the intrinsically luminous portion of the true distribution. This bias does not distort any of the measurements, but distorts the sample composition. We have developed the first treatment to correct for Malmquist bias in linear regressions of astronomical data. A demonstration of the corrected linear regression that is computed in four steps is presented.
Robust median estimator in logisitc regression
Czech Academy of Sciences Publication Activity Database
Hobza, T.; Pardo, L.; Vajda, Igor
2008-01-01
Roč. 138, č. 12 (2008), s. 3822-3840 ISSN 0378-3758 R&D Projects: GA MŠk 1M0572 Grant - others:Instituto Nacional de Estadistica (ES) MPO FI - IM3/136; GA MŠk(CZ) MTM 2006-06872 Institutional research plan: CEZ:AV0Z10750506 Keywords : Logistic regression * Median * Robustness * Consistency and asymptotic normality * Morgenthaler * Bianco and Yohai * Croux and Hasellbroeck Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.679, year: 2008 http://library.utia.cas.cz/separaty/2008/SI/vajda-robust%20median%20estimator%20in%20logistic%20regression.pdf
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
Practicing Good Habits, Grade 4.
Van Cong Lau; And Others
This illustrated textbook was designed for teaching civics and values to fourth grade students in Vietnam. It is divided into six chapters: (1) At School (recapitulation of the grade three program, friendship, respect for the teacher, team work, discipline, honor); (2) In the Street: Traffic Regulations; (3) At Home (the extended family spirit,…
Graded geometry and Poisson reduction
Cattaneo, A S; Zambon, M
2009-01-01
The main result of [2] extends the Marsden-Ratiu reduction theorem [4] in Poisson geometry, and is proven by means of graded geometry. In this note we provide the background material about graded geometry necessary for the proof in [2]. Further, we provide an alternative algebraic proof for the main result. ©2009 American Institute of Physics
Compositionally Graded Multilayer Ceramic Capacitors.
Song, Hyun-Cheol; Zhou, Jie E; Maurya, Deepam; Yan, Yongke; Wang, Yu U; Priya, Shashank
2017-09-27
Multilayer ceramic capacitors (MLCC) are widely used in consumer electronics. Here, we provide a transformative method for achieving high dielectric response and tunability over a wide temperature range through design of compositionally graded multilayer (CGML) architecture. Compositionally graded MLCCs were found to exhibit enhanced dielectric tunability (70%) along with small dielectric losses (filters and power converters.
KELEŞ, Taliha; ALTUN, Murat
2016-01-01
Regression analysis is a statistical technique for investigating and modeling the relationship between variables. The purpose of this study was the trivial presentation of the equation for orthogonal regression (OR) and the comparison of classical linear regression (CLR) and OR techniques with respect to the sum of squared perpendicular distances. For that purpose, the analyses were shown by an example. It was found that the sum of squared perpendicular distances of OR is smaller. Thus, it wa...
Method for nonlinear exponential regression analysis
Junkin, B. G.
1972-01-01
Two computer programs developed according to two general types of exponential models for conducting nonlinear exponential regression analysis are described. Least squares procedure is used in which the nonlinear problem is linearized by expanding in a Taylor series. Program is written in FORTRAN 5 for the Univac 1108 computer.
Measurement Error in Education and Growth Regressions
Portela, Miguel; Alessie, Rob; Teulings, Coen
2010-01-01
The use of the perpetual inventory method for the construction of education data per country leads to systematic measurement error. This paper analyzes its effect on growth regressions. We suggest a methodology for correcting this error. The standard attenuation bias suggests that using these
The M Word: Multicollinearity in Multiple Regression.
Morrow-Howell, Nancy
1994-01-01
Notes that existence of substantial correlation between two or more independent variables creates problems of multicollinearity in multiple regression. Discusses multicollinearity problem in social work research in which independent variables are usually intercorrelated. Clarifies problems created by multicollinearity, explains detection of…
Regression Discontinuity Designs Based on Population Thresholds
DEFF Research Database (Denmark)
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...
Deriving the Regression Line with Algebra
Quintanilla, John A.
2017-01-01
Exploration with spreadsheets and reliance on previous skills can lead students to determine the line of best fit. To perform linear regression on a set of data, students in Algebra 2 (or, in principle, Algebra 1) do not have to settle for using the mysterious "black box" of their graphing calculators (or other classroom technologies).…
Piecewise linear regression splines with hyperbolic covariates
International Nuclear Information System (INIS)
Cologne, John B.; Sposto, Richard
1992-09-01
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)
Targeting: Logistic Regression, Special Cases and Extensions
Directory of Open Access Journals (Sweden)
Helmut Schaeben
2014-12-01
Full Text Available Logistic regression is a classical linear model for logit-transformed conditional probabilities of a binary target variable. It recovers the true conditional probabilities if the joint distribution of predictors and the target is of log-linear form. Weights-of-evidence is an ordinary logistic regression with parameters equal to the differences of the weights of evidence if all predictor variables are discrete and conditionally independent given the target variable. The hypothesis of conditional independence can be tested in terms of log-linear models. If the assumption of conditional independence is violated, the application of weights-of-evidence does not only corrupt the predicted conditional probabilities, but also their rank transform. Logistic regression models, including the interaction terms, can account for the lack of conditional independence, appropriate interaction terms compensate exactly for violations of conditional independence. Multilayer artificial neural nets may be seen as nested regression-like models, with some sigmoidal activation function. Most often, the logistic function is used as the activation function. If the net topology, i.e., its control, is sufficiently versatile to mimic interaction terms, artificial neural nets are able to account for violations of conditional independence and yield very similar results. Weights-of-evidence cannot reasonably include interaction terms; subsequent modifications of the weights, as often suggested, cannot emulate the effect of interaction terms.
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.
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.
Group-wise partial least square regression
Camacho, José; Saccenti, Edoardo
2018-01-01
This paper introduces the group-wise partial least squares (GPLS) regression. GPLS is a new sparse PLS technique where the sparsity structure is defined in terms of groups of correlated variables, similarly to what is done in the related group-wise principal component analysis. These groups are
Functional data analysis of generalized regression quantiles
Guo, Mengmeng; Zhou, Lan; Huang, Jianhua Z.; Hä rdle, Wolfgang Karl
2013-01-01
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.
Finite Algorithms for Robust Linear Regression
DEFF Research Database (Denmark)
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...
Function approximation with polynomial regression slines
International Nuclear Information System (INIS)
Urbanski, P.
1996-01-01
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)
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.
Predicting Social Trust with Binary Logistic Regression
Adwere-Boamah, Joseph; Hufstedler, Shirley
2015-01-01
This study used binary logistic regression to predict social trust with five demographic variables from a national sample of adult individuals who participated in The General Social Survey (GSS) in 2012. The five predictor variables were respondents' highest degree earned, race, sex, general happiness and the importance of personally assisting…
Yet another look at MIDAS regression
Ph.H.B.F. Franses (Philip Hans)
2016-01-01
textabstractA MIDAS regression involves a dependent variable observed at a low frequency and independent variables observed at a higher frequency. This paper relates a true high frequency data generating process, where also the dependent variable is observed (hypothetically) at the high frequency,
Revisiting Regression in Autism: Heller's "Dementia Infantilis"
Westphal, Alexander; Schelinski, Stefanie; Volkmar, Fred; Pelphrey, Kevin
2013-01-01
Theodor Heller first described a severe regression of adaptive function in normally developing children, something he termed dementia infantilis, over one 100 years ago. Dementia infantilis is most closely related to the modern diagnosis, childhood disintegrative disorder. We translate Heller's paper, Uber Dementia Infantilis, and discuss…
Fast multi-output relevance vector regression
Ha, Youngmin
2017-01-01
This paper aims to decrease the time complexity of multi-output relevance vector regression from O(VM^3) to O(V^3+M^3), where V is the number of output dimensions, M is the number of basis functions, and V
Regression Equations for Birth Weight Estimation using ...
African Journals Online (AJOL)
In this study, Birth Weight has been estimated from anthropometric measurements of hand and foot. Linear regression equations were formed from each of the measured variables. These simple equations can be used to estimate Birth Weight of new born babies, in order to identify those with low birth weight and referred to ...
Superquantile Regression: Theory, Algorithms, and Applications
2014-12-01
Highway, Suite 1204, Arlington, Va 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington DC 20503. 1...Navy submariners, reliability engineering, uncertainty quantification, and financial risk management . Superquantile, superquantile regression...Royset Carlos F. Borges Associate Professor of Operations Research Dissertation Supervisor Professor of Applied Mathematics Lyn R. Whitaker Javier
Measurement Error in Education and Growth Regressions
Portela, M.; Teulings, C.N.; Alessie, R.
The perpetual inventory method used for the construction of education data per country leads to systematic measurement error. This paper analyses the effect of this measurement error on GDP regressions. There is a systematic difference in the education level between census data and observations
Measurement error in education and growth regressions
Portela, Miguel; Teulings, Coen; Alessie, R.
2004-01-01
The perpetual inventory method used for the construction of education data per country leads to systematic measurement error. This paper analyses the effect of this measurement error on GDP regressions. There is a systematic difference in the education level between census data and observations
Panel data specifications in nonparametric kernel regression
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
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...
transformation of independent variables in polynomial regression ...
African Journals Online (AJOL)
Ada
preferable when possible to work with a simple functional form in transformed variables rather than with a more complicated form in the original variables. In this paper, it is shown that linear transformations applied to independent variables in polynomial regression models affect the t ratio and hence the statistical ...
Multiple Linear Regression: A Realistic Reflector.
Nutt, A. T.; Batsell, R. R.
Examples of the use of Multiple Linear Regression (MLR) techniques are presented. This is done to show how MLR aids data processing and decision-making by providing the decision-maker with freedom in phrasing questions and by accurately reflecting the data on hand. A brief overview of the rationale underlying MLR is given, some basic definitions…
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.
Simon, Paul A; Leslie, Phillip; Run, Grace; Jin, Ginger Zhe; Reporter, Roshan; Aguirre, Arturo; Fielding, Jonathan E
2005-03-01
Although health departments routinely inspect restaurants to assess compliance with established hygienic standards, few data are available on the effectiveness of these efforts in preventing foodborne disease. The study reported here assessed the impact on foodborne-disease hospitalizations in Los Angeles County of a restaurant hygiene grading system that utilized publicly posted grade cards. The grading systm was introduced in January 1998. Hospital discharge data on foodborne-disease hospitalizations were analyzed for Los Angeles County and, as a control, for the rest of California during the period 1993-2000. Ordinary least-squares regression analysis was done to measure the effect of the grading progam on these hospitalizations. After baseline temporal and geographic trends were adjusted for, the restaurant hygiene grading program was associated with a 13.1 percent decrease (p restaurant hygiene grading with public posting of results is an effective intervention for reducing the burden of foodborne disease.
Effect of Grade Retention in First Grade on Psychosocial Outcomes
Wu, Wei; West, Stephen G.; Hughes, Jan N.
2010-01-01
In a 4-year longitudinal study, the authors investigated effects of retention in first grade on children’s externalizing and internalizing behaviors; social acceptance; and behavioral, cognitive, and affective engagement. From a large multiethnic sample (n = 784) of children below the median on literacy at school entrance, 124 retained children were matched with 251 promoted children on the basis of propensity scores (probability of being retained in first grade estimated from 72 baseline var...
Vaeth, Michael; Skovlund, Eva
2004-06-15
For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.
Controlling attribute effect in linear regression
Calders, Toon; Karim, Asim A.; Kamiran, Faisal; Ali, Wasif Mohammad; Zhang, Xiangliang
2013-01-01
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.
Stochastic development regression using method of moments
DEFF Research Database (Denmark)
Kühnel, Line; Sommer, Stefan Horst
2017-01-01
This paper considers the estimation problem arising when inferring parameters in the stochastic development regression model for manifold valued non-linear data. Stochastic development regression captures the relation between manifold-valued response and Euclidean covariate variables using...... the stochastic development construction. It is thereby able to incorporate several covariate variables and random effects. The model is intrinsically defined using the connection of the manifold, and the use of stochastic development avoids linearizing the geometry. We propose to infer parameters using...... the Method of Moments procedure that matches known constraints on moments of the observations conditional on the latent variables. The performance of the model is investigated in a simulation example using data on finite dimensional landmark manifolds....
Beta-binomial regression and bimodal utilization.
Liu, Chuan-Fen; Burgess, James F; Manning, Willard G; Maciejewski, Matthew L
2013-10-01
To illustrate how the analysis of bimodal U-shaped distributed utilization can be modeled with beta-binomial regression, which is rarely used in health services research. Veterans Affairs (VA) administrative data and Medicare claims in 2001-2004 for 11,123 Medicare-eligible VA primary care users in 2000. We compared means and distributions of VA reliance (the proportion of all VA/Medicare primary care visits occurring in VA) predicted from beta-binomial, binomial, and ordinary least-squares (OLS) models. Beta-binomial model fits the bimodal distribution of VA reliance better than binomial and OLS models due to the nondependence on normality and the greater flexibility in shape parameters. Increased awareness of beta-binomial regression may help analysts apply appropriate methods to outcomes with bimodal or U-shaped distributions. © Health Research and Educational Trust.
Testing homogeneity in Weibull-regression models.
Bolfarine, Heleno; Valença, Dione M
2005-10-01
In survival studies with families or geographical units it may be of interest testing whether such groups are homogeneous for given explanatory variables. In this paper we consider score type tests for group homogeneity based on a mixing model in which the group effect is modelled as a random variable. As opposed to hazard-based frailty models, this model presents survival times that conditioned on the random effect, has an accelerated failure time representation. The test statistics requires only estimation of the conventional regression model without the random effect and does not require specifying the distribution of the random effect. The tests are derived for a Weibull regression model and in the uncensored situation, a closed form is obtained for the test statistic. A simulation study is used for comparing the power of the tests. The proposed tests are applied to real data sets with censored data.
Are increases in cigarette taxation regressive?
Borren, P; Sutton, M
1992-12-01
Using the latest published data from Tobacco Advisory Council surveys, this paper re-evaluates the question of whether or not increases in cigarette taxation are regressive in the United Kingdom. The extended data set shows no evidence of increasing price-elasticity by social class as found in a major previous study. To the contrary, there appears to be no clear pattern in the price responsiveness of smoking behaviour across different social classes. Increases in cigarette taxation, while reducing smoking levels in all groups, fall most heavily on men and women in the lowest social class. Men and women in social class five can expect to pay eight and eleven times more of a tax increase respectively, than their social class one counterparts. Taken as a proportion of relative incomes, the regressive nature of increases in cigarette taxation is even more pronounced.
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.
Regression Models For Multivariate Count Data.
Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei
2017-01-01
Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.
Model selection in kernel ridge regression
DEFF Research Database (Denmark)
Exterkate, Peter
2013-01-01
Kernel ridge regression is a technique to perform ridge regression with a potentially infinite number of nonlinear transformations of the independent variables as regressors. This method is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts....... The influence of the choice of kernel and the setting of tuning parameters on forecast accuracy is investigated. Several popular kernels are reviewed, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. The latter two kernels are interpreted in terms of their smoothing properties......, and the tuning parameters associated to all these kernels are related to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, guidelines are provided for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study...
Confidence bands for inverse regression models
International Nuclear Information System (INIS)
Birke, Melanie; Bissantz, Nicolai; Holzmann, Hajo
2010-01-01
We construct uniform confidence bands for the regression function in inverse, homoscedastic regression models with convolution-type operators. Here, the convolution is between two non-periodic functions on the whole real line rather than between two periodic functions on a compact interval, since the former situation arguably arises more often in applications. First, following Bickel and Rosenblatt (1973 Ann. Stat. 1 1071–95) we construct asymptotic confidence bands which are based on strong approximations and on a limit theorem for the supremum of a stationary Gaussian process. Further, we propose bootstrap confidence bands based on the residual bootstrap and prove consistency of the bootstrap procedure. A simulation study shows that the bootstrap confidence bands perform reasonably well for moderate sample sizes. Finally, we apply our method to data from a gel electrophoresis experiment with genetically engineered neuronal receptor subunits incubated with rat brain extract
Regressing Atherosclerosis by Resolving Plaque Inflammation
2017-07-01
regression requires the alteration of macrophages in the plaques to a tissue repair “alternatively” activated state. This switch in activation state... tissue repair “alternatively” activated state. This switch in activation state requires the action of TH2 cytokines interleukin (IL)-4 or IL-13. To...regulation of tissue macrophage and dendritic cell population dynamics by CSF-1. J Exp Med. 2011;208(9):1901–1916. 35. Xu H, Exner BG, Chilton PM
Determination of regression laws: Linear and nonlinear
International Nuclear Information System (INIS)
Onishchenko, A.M.
1994-01-01
A detailed mathematical determination of regression laws is presented in the article. Particular emphasis is place on determining the laws of X j on X l to account for source nuclei decay and detector errors in nuclear physics instrumentation. Both linear and nonlinear relations are presented. Linearization of 19 functions is tabulated, including graph, relation, variable substitution, obtained linear function, and remarks. 6 refs., 1 tab
Directional quantile regression in Octave (and MATLAB)
Czech Academy of Sciences Publication Activity Database
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: 0.379, year: 2016 http://library.utia.cas.cz/separaty/2016/SI/bocek-0458380.pdf
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.
Akilli, Mustafa
2015-01-01
The aim of this study is to demonstrate the science success regression levels of chosen emotional features of 8th grade students using Structural Equation Model. The study was conducted by the analysis of students' questionnaires and science success in TIMSS 2011 data using SEM. Initially, the factors that are thought to have an effect on science…
Multitask Quantile Regression under the Transnormal Model.
Fan, Jianqing; Xue, Lingzhou; Zou, Hui
2016-01-01
We consider estimating multi-task quantile regression under the transnormal model, with focus on high-dimensional setting. We derive a surprisingly simple closed-form solution through rank-based covariance regularization. In particular, we propose the rank-based ℓ 1 penalization with positive definite constraints for estimating sparse covariance matrices, and the rank-based banded Cholesky decomposition regularization for estimating banded precision matrices. By taking advantage of alternating direction method of multipliers, nearest correlation matrix projection is introduced that inherits sampling properties of the unprojected one. Our work combines strengths of quantile regression and rank-based covariance regularization to simultaneously deal with nonlinearity and nonnormality for high-dimensional regression. Furthermore, the proposed method strikes a good balance between robustness and efficiency, achieves the "oracle"-like convergence rate, and provides the provable prediction interval under the high-dimensional setting. The finite-sample performance of the proposed method is also examined. The performance of our proposed rank-based method is demonstrated in a real application to analyze the protein mass spectroscopy data.
Complex regression Doppler optical coherence tomography
Elahi, Sahar; Gu, Shi; Thrane, Lars; Rollins, Andrew M.; Jenkins, Michael W.
2018-04-01
We introduce a new method to measure Doppler shifts more accurately and extend the dynamic range of Doppler optical coherence tomography (OCT). The two-point estimate of the conventional Doppler method is replaced with a regression that is applied to high-density B-scans in polar coordinates. We built a high-speed OCT system using a 1.68-MHz Fourier domain mode locked laser to acquire high-density B-scans (16,000 A-lines) at high enough frame rates (˜100 fps) to accurately capture the dynamics of the beating embryonic heart. Flow phantom experiments confirm that the complex regression lowers the minimum detectable velocity from 12.25 mm / s to 374 μm / s, whereas the maximum velocity of 400 mm / s is measured without phase wrapping. Complex regression Doppler OCT also demonstrates higher accuracy and precision compared with the conventional method, particularly when signal-to-noise ratio is low. The extended dynamic range allows monitoring of blood flow over several stages of development in embryos without adjusting the imaging parameters. In addition, applying complex averaging recovers hidden features in structural images.
Linear regression and the normality assumption.
Schmidt, Amand F; Finan, Chris
2017-12-16
Researchers often perform arbitrary outcome transformations to fulfill the normality assumption of a linear regression model. This commentary explains and illustrates that in large data settings, such transformations are often unnecessary, and worse may bias model estimates. Linear regression assumptions are illustrated using simulated data and an empirical example on the relation between time since type 2 diabetes diagnosis and glycated hemoglobin levels. Simulation results were evaluated on coverage; i.e., the number of times the 95% confidence interval included the true slope coefficient. Although outcome transformations bias point estimates, violations of the normality assumption in linear regression analyses do not. The normality assumption is necessary to unbiasedly estimate standard errors, and hence confidence intervals and P-values. However, in large sample sizes (e.g., where the number of observations per variable is >10) violations of this normality assumption often do not noticeably impact results. Contrary to this, assumptions on, the parametric model, absence of extreme observations, homoscedasticity, and independency of the errors, remain influential even in large sample size settings. Given that modern healthcare research typically includes thousands of subjects focusing on the normality assumption is often unnecessary, does not guarantee valid results, and worse may bias estimates due to the practice of outcome transformations. Copyright © 2017 Elsevier Inc. All rights reserved.
Satellite rainfall retrieval by logistic regression
Chiu, Long S.
1986-01-01
The potential use of logistic regression in rainfall estimation from satellite measurements is investigated. Satellite measurements provide covariate information in terms of radiances from different remote sensors.The logistic regression technique can effectively accommodate many covariates and test their significance in the estimation. The outcome from the logistical model is the probability that the rainrate of a satellite pixel is above a certain threshold. By varying the thresholds, a rainrate histogram can be obtained, from which the mean and the variant can be estimated. A logistical model is developed and applied to rainfall data collected during GATE, using as covariates the fractional rain area and a radiance measurement which is deduced from a microwave temperature-rainrate relation. It is demonstrated that the fractional rain area is an important covariate in the model, consistent with the use of the so-called Area Time Integral in estimating total rain volume in other studies. To calibrate the logistical model, simulated rain fields generated by rainfield models with prescribed parameters are needed. A stringent test of the logistical model is its ability to recover the prescribed parameters of simulated rain fields. A rain field simulation model which preserves the fractional rain area and lognormality of rainrates as found in GATE is developed. A stochastic regression model of branching and immigration whose solutions are lognormally distributed in some asymptotic limits has also been developed.
Bayesian Inference of a Multivariate Regression Model
Directory of Open Access Journals (Sweden)
Marick S. Sinay
2014-01-01
Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.
Modeling oil production based on symbolic regression
International Nuclear Information System (INIS)
Yang, Guangfei; Li, Xianneng; Wang, Jianliang; Lian, Lian; Ma, Tieju
2015-01-01
Numerous models have been proposed to forecast the future trends of oil production and almost all of them are based on some predefined assumptions with various uncertainties. In this study, we propose a novel data-driven approach that uses symbolic regression to model oil production. We validate our approach on both synthetic and real data, and the results prove that symbolic regression could effectively identify the true models beneath the oil production data and also make reliable predictions. Symbolic regression indicates that world oil production will peak in 2021, which broadly agrees with other techniques used by researchers. Our results also show that the rate of decline after the peak is almost half the rate of increase before the peak, and it takes nearly 12 years to drop 4% from the peak. These predictions are more optimistic than those in several other reports, and the smoother decline will provide the world, especially the developing countries, with more time to orchestrate mitigation plans. -- Highlights: •A data-driven approach has been shown to be effective at modeling the oil production. •The Hubbert model could be discovered automatically from data. •The peak of world oil production is predicted to appear in 2021. •The decline rate after peak is half of the increase rate before peak. •Oil production projected to decline 4% post-peak
Face Alignment via Regressing Local Binary Features.
Ren, Shaoqing; Cao, Xudong; Wei, Yichen; Sun, Jian
2016-03-01
This paper presents a highly efficient and accurate regression approach for face alignment. Our approach has two novel components: 1) a set of local binary features and 2) a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently. The obtained local binary features are used to jointly learn a linear regression for the final output. This approach achieves the state-of-the-art results when tested on the most challenging benchmarks to date. Furthermore, because extracting and regressing local binary features are computationally very cheap, our system is much faster than previous methods. It achieves over 3000 frames per second (FPS) on a desktop or 300 FPS on a mobile phone for locating a few dozens of landmarks. We also study a key issue that is important but has received little attention in the previous research, which is the face detector used to initialize alignment. We investigate several face detectors and perform quantitative evaluation on how they affect alignment accuracy. We find that an alignment friendly detector can further greatly boost the accuracy of our alignment method, reducing the error up to 16% relatively. To facilitate practical usage of face detection/alignment methods, we also propose a convenient metric to measure how good a detector is for alignment initialization.
Geographically weighted regression model on poverty indicator
Slamet, I.; Nugroho, N. F. T. A.; Muslich
2017-12-01
In this research, we applied geographically weighted regression (GWR) for analyzing the poverty in Central Java. We consider Gaussian Kernel as weighted function. The GWR uses the diagonal matrix resulted from calculating kernel Gaussian function as a weighted function in the regression model. The kernel weights is used to handle spatial effects on the data so that a model can be obtained for each location. The purpose of this paper is to model of poverty percentage data in Central Java province using GWR with Gaussian kernel weighted function and to determine the influencing factors in each regency/city in Central Java province. Based on the research, we obtained geographically weighted regression model with Gaussian kernel weighted function on poverty percentage data in Central Java province. We found that percentage of population working as farmers, population growth rate, percentage of households with regular sanitation, and BPJS beneficiaries are the variables that affect the percentage of poverty in Central Java province. In this research, we found the determination coefficient R2 are 68.64%. There are two categories of district which are influenced by different of significance factors.
Mixed-effects regression models in linguistics
Heylen, Kris; Geeraerts, Dirk
2018-01-01
When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addres...
On logistic regression analysis of dichotomized responses.
Lu, Kaifeng
2017-01-01
We study the properties of treatment effect estimate in terms of odds ratio at the study end point from logistic regression model adjusting for the baseline value when the underlying continuous repeated measurements follow a multivariate normal distribution. Compared with the analysis that does not adjust for the baseline value, the adjusted analysis produces a larger treatment effect as well as a larger standard error. However, the increase in standard error is more than offset by the increase in treatment effect so that the adjusted analysis is more powerful than the unadjusted analysis for detecting the treatment effect. On the other hand, the true adjusted odds ratio implied by the normal distribution of the underlying continuous variable is a function of the baseline value and hence is unlikely to be able to be adequately represented by a single value of adjusted odds ratio from the logistic regression model. In contrast, the risk difference function derived from the logistic regression model provides a reasonable approximation to the true risk difference function implied by the normal distribution of the underlying continuous variable over the range of the baseline distribution. We show that different metrics of treatment effect have similar statistical power when evaluated at the baseline mean. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
General regression and representation model for classification.
Directory of Open Access Journals (Sweden)
Jianjun Qian
Full Text Available Recently, the regularized coding-based classification methods (e.g. SRC and CRC show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients and the specific information (weight matrix of image pixels to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR and robust general regression and representation classifier (R-GRR. The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms.
Image superresolution using support vector regression.
Ni, Karl S; Nguyen, Truong Q
2007-06-01
A thorough investigation of the application of support vector regression (SVR) to the superresolution problem is conducted through various frameworks. Prior to the study, the SVR problem is enhanced by finding the optimal kernel. This is done by formulating the kernel learning problem in SVR form as a convex optimization problem, specifically a semi-definite programming (SDP) problem. An additional constraint is added to reduce the SDP to a quadratically constrained quadratic programming (QCQP) problem. After this optimization, investigation of the relevancy of SVR to superresolution proceeds with the possibility of using a single and general support vector regression for all image content, and the results are impressive for small training sets. This idea is improved upon by observing structural properties in the discrete cosine transform (DCT) domain to aid in learning the regression. Further improvement involves a combination of classification and SVR-based techniques, extending works in resolution synthesis. This method, termed kernel resolution synthesis, uses specific regressors for isolated image content to describe the domain through a partitioned look of the vector space, thereby yielding good results.
Giger-Pabst, Urs; Demtröder, Cédric; Falkenstein, Thomas A; Ouaissi, Mehdi; Götze, Thorsten O; Rezniczek, Günther A; Tempfer, Clemens B
2018-04-18
Patients with recurrent malignant epithelioid mesothelioma (MM) after surgery and standard chemotherapy with cisplatin and pemetrexed have limited treatment options. We performed a retrospective cohort study of patients with recurrent MM undergoing Pressurized IntraPeritoneal/Thoracal Aerosol Chemotherapy (PIPAC/PITAC) with doxorubicin 1.5 mg/m 2 and cisplatin 7.5 mg/m 2 . Data were retrospectively collected in a prospective registry of patients undergoing PIPAC/PITAC. Study outcomes were microscopic tumor regression grade (TRG), survival and adverse events (v4.0 CTCAE). A total of 29 patients (m/f = 17/12) with MM with a mean age of 62.4 (range: 42 to 84) years were analyzed. A total of 74 PIPAC and 5 PITAC procedures were performed. The mean number of PIPAC applications was 2.5 (range: 0 to 10) per patient. Twenty patients (69%) had > 2 PIPAC procedure and were eligible for TRG analysis. TRG 1 to 4 was observed in 75% (15/20) of patients. Major regression (TRG 3) or complete regression (TRG 4) was observed in 20% and 10%, respectively. PIPAC induced significant tumor regression in 51.7% (15/29) of patients with a cumulative effect after repetitive PIPACs (PIPAC #1 vs. PIPAC #2: p = 0.001; PIPAC #1 vs. PIPAC #3: p = 0.001; PIPAC #1 vs. PIPAC #4: p = 0.001). Postoperative CTCAE grade 4 complications were observed in two patients (6.9%) who had cytoreductive surgery (CC2) and intraoperative PIPAC. One patient (3.4%) died due to postoperative kidney insufficiency. After a follow up of 14.4 (95% CI: 8.1 to 20.7) months after the last PIPAC/PITAC application, median overall survival was 26.6 (95% CI: 9.5 to 43.7) months (from the first application). After prior abdominal surgery and systemic chemotherapy, repetitive PIPAC applications are feasible and safe for patients with end-stage MM. Furthermore, PIPAC induces significant histological regression of malignant mesothelioma in the majority of patients. PITAC is feasible, but its safety and efficacy
International Nuclear Information System (INIS)
Ong, Aaron; Tan, Shu.; Gledhill, Samuel; Hennessy, Oliver; Lui, Belinda; Lee, Alan; Lemish, Wayne; Styles, Colin; Pun, Emma; Padmanabhan, Meenakshi; Pitman, Alexander G.; Tauro, Paul; Waugh, Paul
2011-01-01
Full text: Picture archiving and communication systems images designed to be viewed on high-resolution medical-grade monitors are routinely viewed on office-grade monitors on the wards or at home. This study aimed to determine whether a statistically significant difference in diagnostic (cancer detection) and perceptual (microcalcification detection) performance exists between 3MP grade and 1MP office-grade monitors. 3MP Dome medical-grade liquid crystal display (LCD) monitors (Planar, Beaverton, OR, USA) were compared to 1MP Dell office-grade LCD monitors (Dell Inc, Round Rock, TX, USA). Eight radiologists (reader experi ence 8-30 years) read the same set of 100 mammograms (23/100 with proven cancers and 52/100 with microcalcifications) presented in random order on three occasions separated by two time intervals of 12 weeks. Reads 1 and 3 utilised 3MP monitors and formed the baseline read. Read 2 utilised 1MP monitors and constituted the experimental read. Reading conditions were standardised. Readers were aware of which monitors they were using. Mul tivariate logistic regression analysis (to account for reader variability and monitor impact) was performed to assess for statistical significance. At a = 5%, confidence intervals analysis comparing the measured parameters between 1MP to 3MP monitors demonstrated no statistically significant difference in diagnostic and perceptual performance for the reader group. In cancer detection (the diagnostic task), reader accuracy remained high irrespective of monitor type. Regression analysis comparing performance with 1MP against 3MP monitors found P values of 0.693 and 0.324 for diagnostic and perceptual performance, respectively. There were no statistically and clinically significant differences between 3MP and 1MP monitors in mammographic diagnostic and perceptual performance. Comparable performance may be due to compensatory behav iour by readers.
Uvajanje funkcionalnega živila na slovenski trg: primer Pomurskih mlekarn
Hari, Sebjan
2009-01-01
V letu 2009 smo s pomočjo terenske in spletne ankete ugotavljali potencialni interes potrošnikov za novo funkcionalno živilo Pomurskih mlekarn — mleko z dodatki fitosterolov. Iz rezultatov obeh anket smo ugotovili, da je prepoznavnost pojma »funkcionalno živilo« zelo slaba, pozna ga le 20% anketirancev, kjer je poznavanje pojma odvisno od starosti in izobrazbe. Približno 50% anketirancev je pripravljenih kupiti izdelek (pripravljenost ni odvisna od starosti in izobrazbe), le približno 30% pa ...
Gender discrimination in exam grading?
DEFF Research Database (Denmark)
Rangvid, Beatrice Schindler
2018-01-01
Girls, on average, obtain higher test scores in school than boys, and recent research suggests that part of this difference may be due to discrimination against boys in grading. This bias is consequential if admission to subsequent education programs is based on exam scores. This study assesses t...... tendencies are in accordance with statistical discrimination as a mechanism for grading bias in essay writing and with gender-stereotyped beliefs of math being a male domain....... are scored twice (blind and non-blind). Both strategies use difference-in-differences methods. Although imprecisely estimated, the point estimates indicate a blind grading advantage for boys in essay writing of approximately 5-8% SD, corresponding to 9-15% of the gender gap in essay exam grades. The effect...
Progressive problems higher grade physics
Kennedy, William
2001-01-01
This book fully covers all three Units studied in Scotland's Higher Grade Physics course, providing a systematic array of problems (from the simplest to the most difficult) to lead variously abled pupils to examination success.
Grade 6 Science Curriculum Specifications.
Alberta Dept. of Education, Edmonton. Curriculum Branch.
This material describes curriculum specifications for grade 6 science in Alberta. Emphases recommended are: (1) process skills (50%); (2) psychomotor skills (10%); (3) attitudes (10%); and (4) subject matter (30%). Priorities within each category are identified. (YP)
International Nuclear Information System (INIS)
Jafri, Y.Z.; Kamal, L.
2007-01-01
Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)
Improving GRADE evidence tables part 2
DEFF Research Database (Denmark)
Langendam, Miranda; Carrasco-Labra, Alonso; Santesso, Nancy
2016-01-01
OBJECTIVES: The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) working group has developed GRADE evidence profiles (EP) and summary of findings (SoF) tables to present evidence summaries in systematic reviews, clinical guidelines, and health technology assessments. Exp...
Oke, J L; Stratton, I M; Aldington, S J; Stevens, R J; Scanlon, Peter H
2016-01-01
AIMS:\\ud We aimed to use longitudinal data from an established screening programme with good quality assurance and quality control procedures and a stable well-trained workforce to determine the accuracy of grading in diabetic retinopathy screening.\\ud METHODS:\\ud We used a continuous time-hidden Markov model with five states to estimate the probability of true progression or regression of retinopathy and the conditional probability of an observed grade given the true grade (misclassification...
Lee, Donggil; Lee, Kyounghoon; Kim, Seonghun; Yang, Yongsu
2015-04-01
An automatic abalone grading algorithm that estimates abalone weights on the basis of computer vision using 2D images is developed and tested. The algorithm overcomes the problems experienced by conventional abalone grading methods that utilize manual sorting and mechanical automatic grading. To design an optimal algorithm, a regression formula and R(2) value were investigated by performing a regression analysis for each of total length, body width, thickness, view area, and actual volume against abalone weights. The R(2) value between the actual volume and abalone weight was 0.999, showing a relatively high correlation. As a result, to easily estimate the actual volumes of abalones based on computer vision, the volumes were calculated under the assumption that abalone shapes are half-oblate ellipsoids, and a regression formula was derived to estimate the volumes of abalones through linear regression analysis between the calculated and actual volumes. The final automatic abalone grading algorithm is designed using the abalone volume estimation regression formula derived from test results, and the actual volumes and abalone weights regression formula. In the range of abalones weighting from 16.51 to 128.01 g, the results of evaluation of the performance of algorithm via cross-validation indicate root mean square and worst-case prediction errors of are 2.8 and ±8 g, respectively. © 2015 Institute of Food Technologists®
On the Relationship Between Confidence Sets and Exchangeable Weights in Multiple Linear Regression.
Pek, Jolynn; Chalmers, R Philip; Monette, Georges
2016-01-01
When statistical models are employed to provide a parsimonious description of empirical relationships, the extent to which strong conclusions can be drawn rests on quantifying the uncertainty in parameter estimates. In multiple linear regression (MLR), regression weights carry two kinds of uncertainty represented by confidence sets (CSs) and exchangeable weights (EWs). Confidence sets quantify uncertainty in estimation whereas the set of EWs quantify uncertainty in the substantive interpretation of regression weights. As CSs and EWs share certain commonalities, we clarify the relationship between these two kinds of uncertainty about regression weights. We introduce a general framework describing how CSs and the set of EWs for regression weights are estimated from the likelihood-based and Wald-type approach, and establish the analytical relationship between CSs and sets of EWs. With empirical examples on posttraumatic growth of caregivers (Cadell et al., 2014; Schneider, Steele, Cadell & Hemsworth, 2011) and on graduate grade point average (Kuncel, Hezlett & Ones, 2001), we illustrate the usefulness of CSs and EWs for drawing strong scientific conclusions. We discuss the importance of considering both CSs and EWs as part of the scientific process, and provide an Online Appendix with R code for estimating Wald-type CSs and EWs for k regression weights.
Cracks in functionally graded materials
International Nuclear Information System (INIS)
Bahr, H.-A.; Balke, H.; Fett, T.; Hofinger, I.; Kirchhoff, G.; Munz, D.; Neubrand, A.; Semenov, A.S.; Weiss, H.-J.; Yang, Y.Y.
2003-01-01
The weight function method is described to analyze the crack growth behavior in functionally graded materials and in particular materials with a rising crack growth resistance curve. Further, failure of graded thermal barrier coatings (TBCs) under cyclic surface heating by laser irradiation is modeled on the basis of fracture mechanics. The damage of both graded and non-graded TBCs is found to develop in several distinct stages: vertical cracking→delamination→blistering→spalling. This sequence can be understood as an effect of progressive shrinkage due to sintering and high-temperature creep during thermal cycling, which increases the energy-release rate for vertical cracks which subsequently turn into delamination cracks. The results of finite element modeling, taking into account the TBC damage mechanisms, are compatible with experimental data. An increase of interface fracture toughness due to grading and a decrease due to ageing have been measured in a four-point bending test modified by a stiffening layer. Correlation with the damage observed in cyclic heating is discussed. It is explained in which way grading is able to reduce the damage
Graded/Gradient Porous Biomaterials
Directory of Open Access Journals (Sweden)
Xigeng Miao
2009-12-01
Full Text Available Biomaterials include bioceramics, biometals, biopolymers and biocomposites and they play important roles in the replacement and regeneration of human tissues. However, dense bioceramics and dense biometals pose the problem of stress shielding due to their high Young’s moduli compared to those of bones. On the other hand, porous biomaterials exhibit the potential of bone ingrowth, which will depend on porous parameters such as pore size, pore interconnectivity, and porosity. Unfortunately, a highly porous biomaterial results in poor mechanical properties. To optimise the mechanical and the biological properties, porous biomaterials with graded/gradient porosity, pores size, and/or composition have been developed. Graded/gradient porous biomaterials have many advantages over graded/gradient dense biomaterials and uniform or homogenous porous biomaterials. The internal pore surfaces of graded/gradient porous biomaterials can be modified with organic, inorganic, or biological coatings and the internal pores themselves can also be filled with biocompatible and biodegradable materials or living cells. However, graded/gradient porous biomaterials are generally more difficult to fabricate than uniform or homogenous porous biomaterials. With the development of cost-effective processing techniques, graded/gradient porous biomaterials can find wide applications in bone defect filling, implant fixation, bone replacement, drug delivery, and tissue engineering.
International Nuclear Information System (INIS)
Garzon, Benjamin; Emblem, Kyrre E.; Mouridsen, Kim; Nedregaard, Baard; Due-Toennessen, Paulina; Nome, Terje; Hald, John K.; Bjoernerud, Atle; Haaberg, Asta K.; Kvinnsland, Yngve
2011-01-01
Background. A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking. Purpose. To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in glioma patients. Material and Methods. T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 glioma patients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression. Results. Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant (P < 0.0001). Conclusion. Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in glioma patients
Energy Technology Data Exchange (ETDEWEB)
Garzon, Benjamin (Dept. of Circulation and Medical Imaging, NTNU, Trondheim (Norway)), email: benjamin.garzon@ntnu.no; Emblem, Kyrre E. (The Interventional Center, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway); Dept. of Radiology, MGH-HST AA Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston (United States)); Mouridsen, Kim (Center of Functionally Integrative Neuroscience, Aarhus Univ., Aarhus (Denmark)); Nedregaard, Baard; Due-Toennessen, Paulina; Nome, Terje; Hald, John K. (Dept. of Radiology and Nuclear Medicine, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway)); Bjoernerud, Atle (The Interventional Center, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway)); Haaberg, Asta K. (Dept. of Circulation and Medical Imaging, NTNU, Trondheim (Norway); Dept. of Medical Imaging, St Olav' s Hospital, Trondheim (Norway)); Kvinnsland, Yngve (NordicImagingLab, Bergen (Norway))
2011-11-15
Background. A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking. Purpose. To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in glioma patients. Material and Methods. T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 glioma patients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression. Results. Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant (P < 0.0001). Conclusion. Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in glioma patients
Ski and snowboard school programs: Injury surveillance and risk factors for grade-specific injury.
Sran, R; Djerboua, M; Romanow, N; Mitra, T; Russell, K; White, K; Goulet, C; Emery, C; Hagel, B
2018-05-01
The objective of our study was to evaluate incidence rates and profile of school program ski and snowboard-related injuries by school grade group using a historical cohort design. Injuries were identified via Accident Report Forms completed by ski patrollers. Severe injury was defined as those with ambulance evacuation or recommending patient transport to hospital. Poisson regression analysis was used to examine the school grade group-specific injury rates adjusting for risk factors (sex, activity, ability, and socioeconomic status) and accounting for the effect of clustering by school. Forty of 107 (37%) injuries reported were severe. Adolescents (grades 7-12) had higher crude injury rates (91 of 10 000 student-days) than children (grades 1-3: 25 of 10 000 student-days; grades 4-6: 65 of 10 000 student-days). Those in grades 1-3 had no severe injuries. Although the rate of injury was lower in grades 1-3, there were no statistically significant grade group differences in adjusted analyses. Snowboarders had a higher rate of injury compared with skiers, while higher ability level was protective. Participants in grades 1-3 had the lowest crude and adjusted injury rates. Students in grades 7-12 had the highest rate of overall and severe injuries. These results will inform evidence-based guidelines for school ski/snowboard program participation by school-aged children. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Hardwood log grades and lumber grade yields for factory lumber logs
Leland F. Hanks; Glenn L. Gammon; Robert L. Brisbin; Everette D. Rast
1980-01-01
The USDA Forest Service Standard Grades for Hardwood Factory Lumber Logs are described, and lumber grade yields for 16 species and 2 species groups are presented by log grade and log diameter. The grades enable foresters, log buyers, and log sellers to select and grade those log suitable for conversion into standard factory grade lumber. By using the apropriate lumber...
7 CFR 810.2204 - Grades and grade requirements for wheat.
2010-01-01
... 7 Agriculture 7 2010-01-01 2010-01-01 false Grades and grade requirements for wheat. 810.2204... OFFICIAL UNITED STATES STANDARDS FOR GRAIN United States Standards for Wheat Principles Governing the Application of Standards § 810.2204 Grades and grade requirements for wheat. (a) Grades and grade requirements...
Energy Technology Data Exchange (ETDEWEB)
Park, Hee-Jin, E-mail: parkhiji@gmail.com [Department of Radiology, Sungkyunkwan University School of Medicine, Kangbuk Samsung Hospital, #108 Pyung-dong, Jongno-gu, Seoul 110-746 (Korea, Republic of); Department of Radiology, Kangwon National University School of Medicine, Baengnyeong-ro 156, Chuncheon-Si, Gangwon-Do Kangwon National University Hospital 200-722 (Korea, Republic of); Kim, Sam Soo, E-mail: samskim@kangwon.ac.kr [Department of Radiology, Kangwon National University School of Medicine, Baengnyeong-ro 156, Chuncheon-Si, Gangwon-Do Kangwon National University Hospital 200-722 (Korea, Republic of); Lee, So-Yeon, E-mail: parkhiji@kwandong.ac.kr [Department of Radiology, Sungkyunkwan University School of Medicine, Kangbuk Samsung Hospital, #108 Pyung-dong, Jongno-gu, Seoul 110-746 (Korea, Republic of); Park, Noh-Hyuck, E-mail: nhpark904@kwandong.ac.kr [Department of Radiology, Myongji Hospital, Kwandong University, College of Medicine, 697-24 Hwajung-dong, Dukyang-ku, Koyang, Kyunggi 412-270 (Korea, Republic of); Park, Ji-Yeon, E-mail: zzzz3@hanmail.net [Department of Radiology, Myongji Hospital, Kwandong University, College of Medicine, 697-24 Hwajung-dong, Dukyang-ku, Koyang, Kyunggi 412-270 (Korea, Republic of); Choi, Yoon-Jung, E-mail: yoonchoi99@gmail.com [Department of Radiology, Sungkyunkwan University School of Medicine, Kangbuk Samsung Hospital, #108 Pyung-dong, Jongno-gu, Seoul 110-746 (Korea, Republic of); Jeon, Hyun-Jun, E-mail: ostrich-13@hanmail.net [Department of Occupational Medicine, Dongsan Medical Center, Keimyung University School of Medicine, 194 Dongsan-Dong, Jung-ku, Taegu (Korea, Republic of)
2013-01-15
Purpose: To propose a reproducible and constant MR grading system for osteoarthritis of the knee joint that provides high interobserver and intraoberver agreement and that does not require complicated calculation procedures. Materials and methods: This retrospective study sample included 44 men and 65 women who underwent both MRI and plain radiography of the knee at our institution. All patients were older than 50 years of age (mean 57.7) and had clinically suspected osteoarthritis of the knee. The standard of 4 grades on the MR grade scale was based mainly on cartilage injury and additional findings. Kellgren–Lawrence grades were assessed for the same patient group. The relationship between the results was determined. Statistical analyses were performed including kappa statistics, categorical regression analysis and nonparametric correlation analysis. Results: The interobserver and intraoberver agreements between the two readers in the grading of osteoarthritis were found to be almost perfect. Interobserver and intraobserver agreements were slightly lower for the MR grading system than for the Kellgren–Lawrence grading scale. The correlation between the MR grade and Kellgren–Lawrence grade was very high and did not differ with patient age. The MR grades were highly correlated with the Kellgren–Lawrence grades and showed excellent interobserver and intraobserver agreements. Conclusion: This new MR grading system for osteoarthritis of the knee joint is reproducible and may be helpful for the grading of osteoarthritis of the knee without requiring reference to plain radiography.
International Nuclear Information System (INIS)
Park, Hee-Jin; Kim, Sam Soo; Lee, So-Yeon; Park, Noh-Hyuck; Park, Ji-Yeon; Choi, Yoon-Jung; Jeon, Hyun-Jun
2013-01-01
Purpose: To propose a reproducible and constant MR grading system for osteoarthritis of the knee joint that provides high interobserver and intraoberver agreement and that does not require complicated calculation procedures. Materials and methods: This retrospective study sample included 44 men and 65 women who underwent both MRI and plain radiography of the knee at our institution. All patients were older than 50 years of age (mean 57.7) and had clinically suspected osteoarthritis of the knee. The standard of 4 grades on the MR grade scale was based mainly on cartilage injury and additional findings. Kellgren–Lawrence grades were assessed for the same patient group. The relationship between the results was determined. Statistical analyses were performed including kappa statistics, categorical regression analysis and nonparametric correlation analysis. Results: The interobserver and intraoberver agreements between the two readers in the grading of osteoarthritis were found to be almost perfect. Interobserver and intraobserver agreements were slightly lower for the MR grading system than for the Kellgren–Lawrence grading scale. The correlation between the MR grade and Kellgren–Lawrence grade was very high and did not differ with patient age. The MR grades were highly correlated with the Kellgren–Lawrence grades and showed excellent interobserver and intraobserver agreements. Conclusion: This new MR grading system for osteoarthritis of the knee joint is reproducible and may be helpful for the grading of osteoarthritis of the knee without requiring reference to plain radiography
Kempe, P T; van Oppen, P; de Haan, E; Twisk, J W R; Sluis, A; Smit, J H; van Dyck, R; van Balkom, A J L M
2007-09-01
Two methods for predicting remissions in obsessive-compulsive disorder (OCD) treatment are evaluated. Y-BOCS measurements of 88 patients with a primary OCD (DSM-III-R) diagnosis were performed over a 16-week treatment period, and during three follow-ups. Remission at any measurement was defined as a Y-BOCS score lower than thirteen combined with a reduction of seven points when compared with baseline. Logistic regression models were compared with a Cox regression for recurrent events model. Logistic regression yielded different models at different evaluation times. The recurrent events model remained stable when fewer measurements were used. Higher baseline levels of neuroticism and more severe OCD symptoms were associated with a lower chance of remission, early age of onset and more depressive symptoms with a higher chance. Choice of outcome time affects logistic regression prediction models. Recurrent events analysis uses all information on remissions and relapses. Short- and long-term predictors for OCD remission show overlap.
Prognostic significance of multiple kallikreins in high-grade astrocytoma
International Nuclear Information System (INIS)
Drucker, Kristen L.; Gianinni, Caterina; Decker, Paul A.; Diamandis, Eleftherios P.; Scarisbrick, Isobel A.
2015-01-01
Kallikreins have clinical value as prognostic markers in a subset of malignancies examined to date, including kallikrein 3 (prostate specific antigen) in prostate cancer. We previously demonstrated that kallikrein 6 is expressed at higher levels in grade IV compared to grade III astrocytoma and is associated with reduced survival of GBM patients. In this study we determined KLK1, KLK6, KLK7, KLK8, KLK9 and KLK10 protein expression in two independent tissue microarrays containing 60 grade IV and 8 grade III astrocytoma samples. Scores for staining intensity, percent of tumor stained and immunoreactivity scores (IR, product of intensity and percent) were determined and analyzed for correlation with patient survival. Grade IV glioma was associated with higher levels of kallikrein-immunostaining compared to grade III specimens. Univariable Cox proportional hazards regression analysis demonstrated that elevated KLK6- or KLK7-IR was associated with poor patient prognosis. In addition, an increased percent of tumor immunoreactive for KLK6 or KLK9 was associated with decreased survival in grade IV patients. Kaplan-Meier survival analysis indicated that patients with KLK6-IR < 10, KLK6 percent tumor core stained < 3, or KLK7-IR < 9 had a significantly improved survival. Multivariable analysis indicated that the significance of these parameters was maintained even after adjusting for gender and performance score. These data suggest that elevations in glioblastoma KLK6, KLK7 and KLK9 protein have utility as prognostic markers of patient survival. The online version of this article (doi:10.1186/s12885-015-1566-5) contains supplementary material, which is available to authorized users
Seizure prognosis of patients with low-grade tumors.
Kahlenberg, Cynthia A; Fadul, Camilo E; Roberts, David W; Thadani, Vijay M; Bujarski, Krzysztof A; Scott, Rod C; Jobst, Barbara C
2012-09-01
Seizures frequently impact the quality of life of patients with low grade tumors. Management is often based on best clinical judgment. We examined factors that correlate with seizure outcome to optimize seizure management. Patients with supratentorial low-grade tumors evaluated at a single institution were retrospectively reviewed. Using multiple regression analysis the patient characteristics and treatments were correlated with seizure outcome using Engel's classification. Of the 73 patients with low grade tumors and median follow up of 3.8 years (range 1-20 years), 54 (74%) patients had a seizure ever and 46 (63%) had at least one seizure before tumor surgery. The only factor significantly associated with pre-surgical seizures was tumor histology. Of the 54 patients with seizures ever, 25 (46.3%) had a class I outcome at last follow up. There was no difference in seizure outcome between grade II gliomas (astrocytoma grade II, oligodendroglioma grade II, mixed oligo-astrocytoma grade II) and other pathologies (pilocytic astrocytoma, ependymomas, DNET, gangliocytoma and ganglioglioma). Once seizures were established seizure prognosis was similar between different pathologies. Chemotherapy (p=0.03) and radiation therapy (p=0.02) had a positive effect on seizure outcome. No other parameter including significant tumor growth during the follow up period predicted seizure outcome. Only three patients developed new-onset seizures after tumor surgery that were non-perioperative. Anticonvulsant medication was tapered in 14 patients with seizures and 10 had no further seizures. Five patients underwent additional epilepsy surgery with a class I outcome in four. Two patients received a vagal nerve stimulator with >50% seizure reduction. Seizures at presentation are the most important factor associated with continued seizures after tumor surgery. Pathology does not influence seizure outcome. Use of long term prophylactic anticonvulsants is unwarranted. Chemotherapy and
A method for nonlinear exponential regression analysis
Junkin, B. G.
1971-01-01
A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.
Multinomial logistic regression in workers' health
Grilo, Luís M.; Grilo, Helena L.; Gonçalves, Sónia P.; Junça, Ana
2017-11-01
In European countries, namely in Portugal, it is common to hear some people mentioning that they are exposed to excessive and continuous psychosocial stressors at work. This is increasing in diverse activity sectors, such as, the Services sector. A representative sample was collected from a Portuguese Services' organization, by applying a survey (internationally validated), which variables were measured in five ordered categories in Likert-type scale. A multinomial logistic regression model is used to estimate the probability of each category of the dependent variable general health perception where, among other independent variables, burnout appear as statistically significant.
Three Contributions to Robust Regression Diagnostics
Czech Academy of Sciences Publication Activity Database
Kalina, Jan
2015-01-01
Roč. 11, č. 2 (2015), s. 69-78 ISSN 1336-9180 Grant - others:GA ČR(CZ) GA13-01930S; Nadační fond na podporu vědy(CZ) Neuron Institutional support: RVO:67985807 Keywords : robust regression * robust econometrics * hypothesis test ing 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
SDE based regression for random PDEs
Bayer, Christian
2016-01-01
A simulation based method for the numerical solution of PDE with random coefficients is presented. By the Feynman-Kac formula, the solution can be represented as conditional expectation of a functional of a corresponding stochastic differential equation driven by independent noise. A time discretization of the SDE for a set of points in the domain and a subsequent Monte Carlo regression lead to an approximation of the global solution of the random PDE. We provide an initial error and complexity analysis of the proposed method along with numerical examples illustrating its behaviour.
Bayesian regression of piecewise homogeneous Poisson processes
Directory of Open Access Journals (Sweden)
Diego Sevilla
2015-12-01
Full Text Available In this paper, a Bayesian method for piecewise regression is adapted to handle counting processes data distributed as Poisson. A numerical code in Mathematica is developed and tested analyzing simulated data. The resulting method is valuable for detecting breaking points in the count rate of time series for Poisson processes. Received: 2 November 2015, Accepted: 27 November 2015; Edited by: R. Dickman; Reviewed by: M. Hutter, Australian National University, Canberra, Australia.; DOI: http://dx.doi.org/10.4279/PIP.070018 Cite as: D J R Sevilla, Papers in Physics 7, 070018 (2015
Selecting a Regression Saturated by Indicators
DEFF Research Database (Denmark)
Hendry, David F.; Johansen, Søren; Santos, Carlos
We consider selecting a regression model, using a variant of Gets, when there are more variables than observations, in the special case that the variables are impulse dummies (indicators) for every observation. We show that the setting is unproblematic if tackled appropriately, and obtain the fin...... the finite-sample distribution of estimators of the mean and variance in a simple location-scale model under the null that no impulses matter. A Monte Carlo simulation confirms the null distribution, and shows power against an alternative of interest....
Selecting a Regression Saturated by Indicators
DEFF Research Database (Denmark)
Hendry, David F.; Johansen, Søren; Santos, Carlos
We consider selecting a regression model, using a variant of Gets, when there are more variables than observations, in the special case that the variables are impulse dummies (indicators) for every observation. We show that the setting is unproblematic if tackled appropriately, and obtain the fin...... the finite-sample distribution of estimators of the mean and variance in a simple location-scale model under the null that no impulses matter. A Monte Carlo simulation confirms the null distribution, and shows power against an alternative of interest...
Mapping geogenic radon potential by regression kriging
Energy Technology Data Exchange (ETDEWEB)
Pásztor, László [Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Department of Environmental Informatics, Herman Ottó út 15, 1022 Budapest (Hungary); Szabó, Katalin Zsuzsanna, E-mail: sz_k_zs@yahoo.de [Department of Chemistry, Institute of Environmental Science, Szent István University, Páter Károly u. 1, Gödöllő 2100 (Hungary); Szatmári, Gábor; Laborczi, Annamária [Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Department of Environmental Informatics, Herman Ottó út 15, 1022 Budapest (Hungary); Horváth, Ákos [Department of Atomic Physics, Eötvös University, Pázmány Péter sétány 1/A, 1117 Budapest (Hungary)
2016-02-15
Radon ({sup 222}Rn) gas is produced in the radioactive decay chain of uranium ({sup 238}U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly. - Highlights: • A new method
Fixed kernel regression for voltammogram feature extraction
International Nuclear Information System (INIS)
Acevedo Rodriguez, F J; López-Sastre, R J; Gil-Jiménez, P; Maldonado Bascón, S; Ruiz-Reyes, N
2009-01-01
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
Regression analysis for the social sciences
Gordon, Rachel A
2010-01-01
The book provides graduate students in the social sciences with the basic skills that they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of both SAS and Stata "side-by-side" and use of chapter exercises in which students practice programming and interpretation on the same data set and course exercises in which students can choose their own research questions and data set.
SDE based regression for random PDEs
Bayer, Christian
2016-01-06
A simulation based method for the numerical solution of PDE with random coefficients is presented. By the Feynman-Kac formula, the solution can be represented as conditional expectation of a functional of a corresponding stochastic differential equation driven by independent noise. A time discretization of the SDE for a set of points in the domain and a subsequent Monte Carlo regression lead to an approximation of the global solution of the random PDE. We provide an initial error and complexity analysis of the proposed method along with numerical examples illustrating its behaviour.
Neutrosophic Correlation and Simple Linear Regression
Directory of Open Access Journals (Sweden)
A. A. Salama
2014-09-01
Full Text Available Since the world is full of indeterminacy, the neutrosophics found their place into contemporary research. The fundamental concepts of neutrosophic set, introduced by Smarandache. Recently, Salama et al., introduced the concept of correlation coefficient of neutrosophic data. In this paper, we introduce and study the concepts of correlation and correlation coefficient of neutrosophic data in probability spaces and study some of their properties. Also, we introduce and study the neutrosophic simple linear regression model. Possible applications to data processing are touched upon.
Spectral density regression for bivariate extremes
Castro Camilo, Daniela
2016-05-11
We introduce a density regression model for the spectral density of a bivariate extreme value distribution, that allows us to assess how extremal dependence can change over a covariate. Inference is performed through a double kernel estimator, which can be seen as an extension of the Nadaraya–Watson estimator where the usual scalar responses are replaced by mean constrained densities on the unit interval. Numerical experiments with the methods illustrate their resilience in a variety of contexts of practical interest. An extreme temperature dataset is used to illustrate our methods. © 2016 Springer-Verlag Berlin Heidelberg
SPE dose prediction using locally weighted regression
International Nuclear Information System (INIS)
Hines, J. W.; Townsend, L. W.; Nichols, T. F.
2005-01-01
When astronauts are outside earth's protective magnetosphere, they are subject to large radiation doses resulting from solar particle events (SPEs). The total dose received from a major SPE in deep space could cause severe radiation poisoning. The dose is usually received over a 20-40 h time interval but the event's effects may be mitigated with an early warning system. This paper presents a method to predict the total dose early in the event. It uses a locally weighted regression model, which is easier to train and provides predictions as accurate as neural network models previously used. (authors)
Mapping geogenic radon potential by regression kriging
International Nuclear Information System (INIS)
Pásztor, László; Szabó, Katalin Zsuzsanna; Szatmári, Gábor; Laborczi, Annamária; Horváth, Ákos
2016-01-01
Radon ( 222 Rn) gas is produced in the radioactive decay chain of uranium ( 238 U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly. - Highlights: • A new method, regression
SPE dose prediction using locally weighted regression
International Nuclear Information System (INIS)
Hines, J. W.; Townsend, L. W.; Nichols, T. F.
2005-01-01
When astronauts are outside Earth's protective magnetosphere, they are subject to large radiation doses resulting from solar particle events. The total dose received from a major solar particle event in deep space could cause severe radiation poisoning. The dose is usually received over a 20-40 h time interval but the event's effects may be reduced with an early warning system. This paper presents a method to predict the total dose early in the event. It uses a locally weighted regression model, which is easier to train, and provides predictions as accurate as the neural network models that were used previously. (authors)
AIRLINE ACTIVITY FORECASTING BY REGRESSION MODELS
Directory of Open Access Journals (Sweden)
Н. Білак
2012-04-01
Full Text Available Proposed linear and nonlinear regression models, which take into account the equation of trend and seasonality indices for the analysis and restore the volume of passenger traffic over the past period of time and its prediction for future years, as well as the algorithm of formation of these models based on statistical analysis over the years. The desired model is the first step for the synthesis of more complex models, which will enable forecasting of passenger (income level airline with the highest accuracy and time urgency.
Toy, Brian C; Krishnadev, Nupura; Indaram, Maanasa; Cunningham, Denise; Cukras, Catherine A; Chew, Emily Y; Wong, Wai T
2013-09-01
To investigate the association of spontaneous drusen regression in intermediate age-related macular degeneration (AMD) with changes on fundus photography and fundus autofluorescence (FAF) imaging. Prospective observational case series. Fundus images from 58 eyes (in 58 patients) with intermediate AMD and large drusen were assessed over 2 years for areas of drusen regression that exceeded the area of circle C1 (diameter 125 μm; Age-Related Eye Disease Study grading protocol). Manual segmentation and computer-based image analysis were used to detect and delineate areas of drusen regression. Delineated regions were graded as to their appearance on fundus photographs and FAF images, and changes in FAF signal were graded manually and quantitated using automated image analysis. Drusen regression was detected in approximately half of study eyes using manual (48%) and computer-assisted (50%) techniques. At year-2, the clinical appearance of areas of drusen regression on fundus photography was mostly unremarkable, with a majority of eyes (71%) demonstrating no detectable clinical abnormalities, and the remainder (29%) showing minor pigmentary changes. However, drusen regression areas were associated with local changes in FAF that were significantly more prominent than changes on fundus photography. A majority of eyes (64%-66%) demonstrated a predominant decrease in overall FAF signal, while 14%-21% of eyes demonstrated a predominant increase in overall FAF signal. FAF imaging demonstrated that drusen regression in intermediate AMD was often accompanied by changes in local autofluorescence signal. Drusen regression may be associated with concurrent structural and physiologic changes in the outer retina. Published by Elsevier Inc.
Logistic regression applied to natural hazards: rare event logistic regression with replications
Guns, M.; Vanacker, Veerle
2012-01-01
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 logisti...
Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon
2015-01-01
Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.
Bayesian nonlinear regression for large small problems
Chakraborty, Sounak; Ghosh, Malay; Mallick, Bani K.
2012-01-01
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.
Spontaneous regression of intracranial malignant lymphoma
International Nuclear Information System (INIS)
Kojo, Nobuto; Tokutomi, Takashi; Eguchi, Gihachirou; Takagi, Shigeyuki; Matsumoto, Tomie; Sasaguri, Yasuyuki; Shigemori, Minoru.
1988-01-01
In a 46-year-old female with a 1-month history of gait and speech disturbances, computed tomography (CT) demonstrated mass lesions of slightly high density in the left basal ganglia and left frontal lobe. The lesions were markedly enhanced by contrast medium. The patient received no specific treatment, but her clinical manifestations gradually abated and the lesions decreased in size. Five months after her initial examination, the lesions were absent on CT scans; only a small area of low density remained. Residual clinical symptoms included mild right hemiparesis and aphasia. After 14 months the patient again deteriorated, and a CT scan revealed mass lesions in the right frontal lobe and the pons. However, no enhancement was observed in the previously affected regions. A biopsy revealed malignant lymphoma. Despite treatment with steroids and radiation, the patient's clinical status progressively worsened and she died 27 months after initial presentation. Seven other cases of spontaneous regression of primary malignant lymphoma have been reported. In this case, the mechanism of the spontaneous regression was not clear, but changes in immunologic status may have been involved. (author)
Regression testing in the TOTEM DCS
International Nuclear Information System (INIS)
Rodríguez, F Lucas; Atanassov, I; Burkimsher, P; Frost, O; Taskinen, J; Tulimaki, V
2012-01-01
The Detector Control System of the TOTEM experiment at the LHC is built with the industrial product WinCC OA (PVSS). The TOTEM system is generated automatically through scripts using as input the detector Product Breakdown Structure (PBS) structure and its pinout connectivity, archiving and alarm metainformation, and some other heuristics based on the naming conventions. When those initial parameters and automation code are modified to include new features, the resulting PVSS system can also introduce side-effects. On a daily basis, a custom developed regression testing tool takes the most recent code from a Subversion (SVN) repository and builds a new control system from scratch. This system is exported in plain text format using the PVSS export tool, and compared with a system previously validated by a human. A report is sent to the developers with any differences highlighted, in readiness for validation and acceptance as a new stable version. This regression approach is not dependent on any development framework or methodology. This process has been satisfactory during several months, proving to be a very valuable tool before deploying new versions in the production systems.
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
Structural Break Tests Robust to Regression Misspecification
Directory of Open Access Journals (Sweden)
Alaa Abi Morshed
2018-05-01
Full Text Available Structural break tests for regression models are sensitive to model misspecification. We show—analytically and through simulations—that the sup Wald test for breaks in the conditional mean and variance of a time series process exhibits severe size distortions when the conditional mean dynamics are misspecified. We also show that the sup Wald test for breaks in the unconditional mean and variance does not have the same size distortions, yet benefits from similar power to its conditional counterpart in correctly specified models. Hence, we propose using it as an alternative and complementary test for breaks. We apply the unconditional and conditional mean and variance tests to three US series: unemployment, industrial production growth and interest rates. Both the unconditional and the conditional mean tests detect a break in the mean of interest rates. However, for the other two series, the unconditional mean test does not detect a break, while the conditional mean tests based on dynamic regression models occasionally detect a break, with the implied break-point estimator varying across different dynamic specifications. For all series, the unconditional variance does not detect a break while most tests for the conditional variance do detect a break which also varies across specifications.
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.
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.
Hyperspectral Unmixing with Robust Collaborative Sparse Regression
Directory of Open Access Journals (Sweden)
Chang Li
2016-07-01
Full Text Available Recently, sparse unmixing (SU of hyperspectral data has received particular attention for analyzing remote sensing images. However, most SU methods are based on the commonly admitted linear mixing model (LMM, which ignores the possible nonlinear effects (i.e., nonlinearity. In this paper, we propose a new method named robust collaborative sparse regression (RCSR based on the robust LMM (rLMM for hyperspectral unmixing. The rLMM takes the nonlinearity into consideration, and the nonlinearity is merely treated as outlier, which has the underlying sparse property. The RCSR simultaneously takes the collaborative sparse property of the abundance and sparsely distributed additive property of the outlier into consideration, which can be formed as a robust joint sparse regression problem. The inexact augmented Lagrangian method (IALM is used to optimize the proposed RCSR. The qualitative and quantitative experiments on synthetic datasets and real hyperspectral images demonstrate that the proposed RCSR is efficient for solving the hyperspectral SU problem compared with the other four state-of-the-art algorithms.
Supporting Regularized Logistic Regression Privately and Efficiently.
Directory of Open Access Journals (Sweden)
Wenfa Li
Full Text Available 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.
Interpreting parameters in the logistic regression model with random effects
DEFF Research Database (Denmark)
Larsen, Klaus; Petersen, Jørgen Holm; Budtz-Jørgensen, Esben
2000-01-01
interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects......interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects...
Functionally Graded Mo sintered steels
Directory of Open Access Journals (Sweden)
Manuel Cisneros-Belmonte
2016-12-01
Full Text Available Functionally graded materials (FGM, the multi-materials, strive to satisfy the numerous requirements demanded of parts in a given combination of compositions and microstructures. The required material compatibility lead the manufacturing process and the achieving of an interface, not always diffuse. Powder metallurgy is one of the techniques used in manufacturing functionally graded materials, in particular the compaction matrix of the possible techniques for forming these materials. In this paper, a process of forming a functionally graded steel based on the use of a high molybdenum steel with cooper and other steel with copper, without molybdenum, is proposed with the aim of concentrating this element to the surface of the workpiece, increasing the mechanical strength. The study is completed with the evaluation of physical properties (density and porosity distribution, mechanical properties (hardness, tensile strength and elongation and microstructural analysis by optical and scanning electron microscopy.
BANK FAILURE PREDICTION WITH LOGISTIC REGRESSION
Directory of Open Access Journals (Sweden)
Taha Zaghdoudi
2013-04-01
Full Text Available In recent years the economic and financial world is shaken by a wave of financial crisis and resulted in violent bank fairly huge losses. Several authors have focused on the study of the crises in order to develop an early warning model. It is in the same path that our work takes its inspiration. Indeed, we have tried to develop a predictive model of Tunisian bank failures with the contribution of the binary logistic regression method. The specificity of our prediction model is that it takes into account microeconomic indicators of bank failures. The results obtained using our provisional model show that a bank's ability to repay its debt, the coefficient of banking operations, bank profitability per employee and leverage financial ratio has a negative impact on the probability of failure.
Robust Mediation Analysis Based on Median Regression
Yuan, Ying; MacKinnon, David P.
2014-01-01
Mediation analysis has many applications in psychology and the social sciences. The most prevalent methods typically assume that the error distribution is normal and homoscedastic. However, this assumption may rarely be met in practice, which can affect the validity of the mediation analysis. To address this problem, we propose robust mediation analysis based on median regression. Our approach is robust to various departures from the assumption of homoscedasticity and normality, including heavy-tailed, skewed, contaminated, and heteroscedastic distributions. Simulation studies show that under these circumstances, the proposed method is more efficient and powerful than standard mediation analysis. We further extend the proposed robust method to multilevel mediation analysis, and demonstrate through simulation studies that the new approach outperforms the standard multilevel mediation analysis. We illustrate the proposed method using data from a program designed to increase reemployment and enhance mental health of job seekers. PMID:24079925
ANYOLS, Least Square Fit by Stepwise Regression
International Nuclear Information System (INIS)
Atwoods, C.L.; Mathews, S.
1986-01-01
Description of program or function: ANYOLS is a stepwise program which fits data using ordinary or weighted least squares. Variables are selected for the model in a stepwise way based on a user- specified input criterion or a user-written subroutine. The order in which variables are entered can be influenced by user-defined forcing priorities. Instead of stepwise selection, ANYOLS can try all possible combinations of any desired subset of the variables. Automatic output for the final model in a stepwise search includes plots of the residuals, 'studentized' residuals, and leverages; if the model is not too large, the output also includes partial regression and partial leverage plots. A data set may be re-used so that several selection criteria can be tried. Flexibility is increased by allowing the substitution of user-written subroutines for several default subroutines
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.
Conjoined legs: Sirenomelia or caudal regression syndrome?
Directory of Open Access Journals (Sweden)
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.
Conjoined legs: Sirenomelia or caudal regression syndrome?
Das, Sakti Prasad; Ojha, Niranjan; Ganesh, G Shankar; Mohanty, Ram Narayan
2013-07-01
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 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.
Logistic regression against a divergent Bayesian network
Directory of Open Access Journals (Sweden)
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.
Adaptive regression for modeling nonlinear relationships
Knafl, George J
2016-01-01
This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the s...
Crime Modeling using Spatial Regression Approach
Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.
2018-01-01
Act of criminality in Indonesia increased both variety and quantity every year. As murder, rape, assault, vandalism, theft, fraud, fencing, and other cases that make people feel unsafe. Risk of society exposed to crime is the number of reported cases in the police institution. The higher of the number of reporter to the police institution then the number of crime in the region is increasing. In this research, modeling criminality in South Sulawesi, Indonesia with the dependent variable used is the society exposed to the risk of crime. Modelling done by area approach is the using Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods. The independent variable used is the population density, the number of poor population, GDP per capita, unemployment and the human development index (HDI). Based on the analysis using spatial regression can be shown that there are no dependencies spatial both lag or errors in South Sulawesi.
Regression analysis for the social sciences
Gordon, Rachel A
2015-01-01
Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition.
Entrepreneurial intention modeling using hierarchical multiple regression
Directory of Open Access Journals (Sweden)
Marina Jeger
2014-12-01
Full Text Available The goal of this study is to identify the contribution of effectuation dimensions to the predictive power of the entrepreneurial intention model over and above that which can be accounted for by other predictors selected and confirmed in previous studies. As is often the case in social and behavioral studies, some variables are likely to be highly correlated with each other. Therefore, the relative amount of variance in the criterion variable explained by each of the predictors depends on several factors such as the order of variable entry and sample specifics. The results show the modest predictive power of two dimensions of effectuation prior to the introduction of the theory of planned behavior elements. The article highlights the main advantages of applying hierarchical regression in social sciences as well as in the specific context of entrepreneurial intention formation, and addresses some of the potential pitfalls that this type of analysis entails.
Gaussian process regression for geometry optimization
Denzel, Alexander; Kästner, Johannes
2018-03-01
We implemented a geometry optimizer based on Gaussian process regression (GPR) to find minimum structures on potential energy surfaces. We tested both a two times differentiable form of the Matérn kernel and the squared exponential kernel. The Matérn kernel performs much better. We give a detailed description of the optimization procedures. These include overshooting the step resulting from GPR in order to obtain a higher degree of interpolation vs. extrapolation. In a benchmark against the Limited-memory Broyden-Fletcher-Goldfarb-Shanno optimizer of the DL-FIND library on 26 test systems, we found the new optimizer to generally reduce the number of required optimization steps.
Least square regularized regression in sum space.
Xu, Yong-Li; Chen, Di-Rong; Li, Han-Xiong; Liu, Lu
2013-04-01
This paper proposes a least square regularized regression algorithm in sum space of reproducing kernel Hilbert spaces (RKHSs) for nonflat function approximation, and obtains the solution of the algorithm by solving a system of linear equations. This algorithm can approximate the low- and high-frequency component of the target function with large and small scale kernels, respectively. The convergence and learning rate are analyzed. We measure the complexity of the sum space by its covering number and demonstrate that the covering number can be bounded by the product of the covering numbers of basic RKHSs. For sum space of RKHSs with Gaussian kernels, by choosing appropriate parameters, we tradeoff the sample error and regularization error, and obtain a polynomial learning rate, which is better than that in any single RKHS. The utility of this method is illustrated with two simulated data sets and five real-life databases.
Statistical learning from a regression perspective
Berk, Richard A
2016-01-01
This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this can be seen as an extension of nonparametric regression. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. A continued emphasis on the implications for practice runs through the text. Among the statistical learning procedures examined are bagging, random forests, boosting, support vector machines and neural networks. Response variables may be quantitative or categorical. As in the first edition, a unifying theme is supervised learning that can be trea...
Model Selection in Kernel Ridge Regression
DEFF Research Database (Denmark)
Exterkate, Peter
Kernel ridge regression is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts. This paper investigates the influence of the choice of kernel and the setting of tuning parameters on forecast accuracy. We review several popular kernels......, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. We interpret the latter two kernels in terms of their smoothing properties, and we relate the tuning parameters associated to all these kernels to smoothness measures of the prediction function and to the signal-to-noise ratio. Based...... on these interpretations, we provide guidelines for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study confirms the practical usefulness of these rules of thumb. Finally, the flexible and smooth functional forms provided by the Gaussian and Sinc kernels makes them widely...
7 CFR 51.304 - Combination grades.
2010-01-01
... Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards, Inspections, Marketing Practices), DEPARTMENT OF AGRICULTURE REGULATIONS AND STANDARDS UNDER THE AGRICULTURAL MARKETING ACT OF 1946... Standards for Grades of Apples Grades § 51.304 Combination grades. (a) Combinations of the above grades may...
Seldow, Adam Lowell
2010-01-01
With the widespread growth of broadband Internet access, teachers, and in many cases, schools and school districts are transitioning from traditional paper-based grade books to student accessible online (Web-based) grade books. Online grade books offer students 24/7, on demand access to grades and various other student data, and have the potential…
Directory of Open Access Journals (Sweden)
Tao Xu
Full Text Available OBJECTIVES: To investigate the expression and prognostic value of bone sialoprotein (BSP in glioma patients. METHODS: We determined the expression of BSP using real-time RT-PCR and immunohistochemistry in tissue microarrays containing 15 normal brain and 270 glioma samples. Cumulative survival was calculated by the Kaplan-Meier method and analyzed by the log-rank test. Univariate and multivariate analyses were performed by the stepwise forward Cox regression model. RESULTS: Both BSP mRNA and protein levels were significantly elevated in high-grade glioma tissues compared with those of normal brain and low-grade glioma tissues, and BSP expression positively correlated with tumor grade (P<0.001. Univariate and multivariate analysis showed high BSP expression was an independent prognostic factor for a shorter progression-free survival (PFS and overall survival (OS in both grade III and grade IV glioma patients [hazard ratio (HR = 2.549 and 3.154 for grade III glioma, and HR = 1.637 and 1.574 for grade IV glioma, respectively]. Patients with low BSP expression had a significantly longer median OS and PFS than those with high BSP expression. Small extent of resection and lineage of astrocyte served as independent risk factors of both shorter PFS and OS in grade III glioma patients; GBM patients without O(6-methylguanine (O(6-meG DNA methyltransferase (MGMT methylation and Karnofsky performance score (KPS less than 70 points were related to poor prognosis. Lack of radiotherapy related to shorter OS but not affect PFS in both grade III and grade IV glioma patients. CONCLUSION: High BSP expression occurs in a significant subset of high-grade glioma patients and predicts a poorer outcome. The study identifies a potentially useful molecular marker for the categorization and targeted therapy of gliomas.
Learning Inverse Rig Mappings by Nonlinear Regression.
Holden, Daniel; Saito, Jun; Komura, Taku
2017-03-01
We present a framework to design inverse rig-functions-functions that map low level representations of a character's pose such as joint positions or surface geometry to the representation used by animators called the animation rig. Animators design scenes using an animation rig, a framework widely adopted in animation production which allows animators to design character poses and geometry via intuitive parameters and interfaces. Yet most state-of-the-art computer animation techniques control characters through raw, low level representations such as joint angles, joint positions, or vertex coordinates. This difference often stops the adoption of state-of-the-art techniques in animation production. Our framework solves this issue by learning a mapping between the low level representations of the pose and the animation rig. We use nonlinear regression techniques, learning from example animation sequences designed by the animators. When new motions are provided in the skeleton space, the learned mapping is used to estimate the rig controls that reproduce such a motion. We introduce two nonlinear functions for producing such a mapping: Gaussian process regression and feedforward neural networks. The appropriate solution depends on the nature of the rig and the amount of data available for training. We show our framework applied to various examples including articulated biped characters, quadruped characters, facial animation rigs, and deformable characters. With our system, animators have the freedom to apply any motion synthesis algorithm to arbitrary rigging and animation pipelines for immediate editing. This greatly improves the productivity of 3D animation, while retaining the flexibility and creativity of artistic input.
DRREP: deep ridge regressed epitope predictor.
Sher, Gene; Zhi, Degui; Zhang, Shaojie
2017-10-03
The ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been numerous advancements and improvements in epitope prediction, on average the best benchmark prediction accuracies are still only around 60%. New machine learning algorithms have arisen within the domain of deep learning, text mining, and convolutional networks. This paper presents a novel analytically trained and string kernel using deep neural network, which is tailored for continuous epitope prediction, called: Deep Ridge Regressed Epitope Predictor (DRREP). DRREP was tested on long protein sequences from the following datasets: SARS, Pellequer, HIV, AntiJen, and SEQ194. DRREP was compared to numerous state of the art epitope predictors, including the most recently published predictors called LBtope and DMNLBE. Using area under ROC curve (AUC), DRREP achieved a performance improvement over the best performing predictors on SARS (13.7%), HIV (8.9%), Pellequer (1.5%), and SEQ194 (3.1%), with its performance being matched only on the AntiJen dataset, by the LBtope predictor, where both DRREP and LBtope achieved an AUC of 0.702. DRREP is an analytically trained deep neural network, thus capable of learning in a single step through regression. By combining the features of deep learning, string kernels, and convolutional networks, the system is able to perform residue-by-residue prediction of continues epitopes with higher accuracy than the current state of the art predictors.
Collaborative regression-based anatomical landmark detection
International Nuclear Information System (INIS)
Gao, Yaozong; Shen, Dinggang
2015-01-01
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 and 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. (paper)
Functionally Graded Material: An overview
CSIR Research Space (South Africa)
Mahamood, RM
2012-07-01
Full Text Available -3146. [50] X. Jin, L. Wu, L. Guo, H. Yu, and Y. Sun, ?Experimental investigation of the mixed-mode crack propagation in ZrO2/NiCr functionally graded materials,? Engineering Fracture Mechanics, vol. 76(12), (2009), pp. 1800-1810. [51] Z. Cheng, D. Gao... by stable crack growth,? Engineering Fracture Mechanics, vol.72(15), (2005), pp. 2359-2372. [47] Z.-H. Jin, and R.H. Dodds Jr, ?Crack growth resistance behavior of a functionally graded material: computational studies,? Engineering Fracture Mechanics...
The effect of attending tutoring on course grades in Calculus I
Rickard, Brian; Mills, Melissa
2018-04-01
Tutoring centres are common in universities in the United States, but there are few published studies that statistically examine the effects of tutoring on student success. This study utilizes multiple regression analysis to model the effect of tutoring attendance on final course grades in Calculus I. Our model predicted that every three visits to the tutoring centre is correlated with an increase of a students' course grade by one per cent, after controlling for prior academic ability. We also found that for lower-achieving students, attending tutoring had a greater impact on final grades.
On the union of graded prime ideals
Directory of Open Access Journals (Sweden)
Uregen Rabia Nagehan
2016-01-01
Full Text Available In this paper we investigate graded compactly packed rings, which is defined as; if any graded ideal I of R is contained in the union of a family of graded prime ideals of R, then I is actually contained in one of the graded prime ideals of the family. We give some characterizations of graded compactly packed rings. Further, we examine this property on h – Spec(R. We also define a generalization of graded compactly packed rings, the graded coprimely packed rings. We show that R is a graded compactly packed ring if and only if R is a graded coprimely packed ring whenever R be a graded integral domain and h – dim R = 1.
WHAT INFLUENCES STUDENTS' EXPECTATIONS IN WHAT REGARDS GRADES?
Directory of Open Access Journals (Sweden)
Mare Codruta
2013-07-01
Full Text Available After a period of studying a certain subject, students form an opinion about it and begin having certain expectations. These expectations and the degree in which, in the end, they fulfil, contribute to the reputation of the university. Consequently, a continuous evaluation of the quality of the educational process is needed. The present research presents a part of a more complex study made on a sample of master students in Audit and Financial Management in Romania. The goal was to evidence the main factors that affect students' expectations in what regards the grades they will obtain at the end of the semester. For this, a questionnaire of 20 questions was applied to 250 such students. After factor reduction procedures were applied, six most significant variables were kept in the analysis: the proportion of knowledge acquired, the perceived level of utility of the discipline in the professional career of the student, the proportion in which the subject could contribute to getting employed in the field it belongs to, the evaluation method and two variables evaluating through grades the didactic performance during the course and the overall performance of the tenure professor. The influence of these variables upon the grade expected by the student was assessed with the help of the OLS regression, both in the simple and multiple forms. Out of the six hypotheses formulated, only one proved to be false based on the simple regression analysis. When individually assessed, the evaluation method announced by the teacher at the beginning of the semester turned out to have no statistically significant influence upon students' expectations. For the rest of the variables, results were according to the assumptions made, i.e. all determine in a significant positive manner the students' opinion about the grade they will get. We have also constructed the multiple regression models. When putting all variables together, the significance changes. The level of
Logistic regression applied to natural hazards: rare event logistic regression with replications
Directory of Open Access Journals (Sweden)
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.
Logistic regression applied to natural hazards: rare event logistic regression with replications
Guns, M.; Vanacker, V.
2012-06-01
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.
Smith, Paul F; Ganesh, Siva; Liu, Ping
2013-10-30
Regression is a common statistical tool for prediction in neuroscience. However, linear regression is by far the most common form of regression used, with regression trees receiving comparatively little attention. In this study, the results of conventional multiple linear regression (MLR) were compared with those of random forest regression (RFR), in the prediction of the concentrations of 9 neurochemicals in the vestibular nucleus complex and cerebellum that are part of the l-arginine biochemical pathway (agmatine, putrescine, spermidine, spermine, l-arginine, l-ornithine, l-citrulline, glutamate and γ-aminobutyric acid (GABA)). The R(2) values for the MLRs were higher than the proportion of variance explained values for the RFRs: 6/9 of them were ≥ 0.70 compared to 4/9 for RFRs. Even the variables that had the lowest R(2) values for the MLRs, e.g. ornithine (0.50) and glutamate (0.61), had much lower proportion of variance explained values for the RFRs (0.27 and 0.49, respectively). The RSE values for the MLRs were lower than those for the RFRs in all but two cases. In general, MLRs seemed to be superior to the RFRs in terms of predictive value and error. In the case of this data set, MLR appeared to be superior to RFR in terms of its explanatory value and error. This result suggests that MLR may have advantages over RFR for prediction in neuroscience with this kind of data set, but that RFR can still have good predictive value in some cases. Copyright © 2013 Elsevier B.V. All rights reserved.
Ridge regression estimator: combining unbiased and ordinary ridge regression methods of estimation
Directory of Open Access Journals (Sweden)
Sharad Damodar Gore
2009-10-01
Full Text Available Statistical literature has several methods for coping with multicollinearity. This paper introduces a new shrinkage estimator, called modified unbiased ridge (MUR. This estimator is obtained from unbiased ridge regression (URR in the same way that ordinary ridge regression (ORR is obtained from ordinary least squares (OLS. Properties of MUR are derived. Results on its matrix mean squared error (MMSE are obtained. MUR is compared with ORR and URR in terms of MMSE. These results are illustrated with an example based on data generated by Hoerl and Kennard (1975.
Directory of Open Access Journals (Sweden)
Hong-Juan Li
2013-04-01
Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.
Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients
Gorgees, HazimMansoor; Mahdi, FatimahAssim
2018-05-01
This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.
Patel, Uday B; Taylor, Fiona; Blomqvist, Lennart; George, Christopher; Evans, Hywel; Tekkis, Paris; Quirke, Philip; Sebag-Montefiore, David; Moran, Brendan; Heald, Richard; Guthrie, Ashley; Bees, Nicola; Swift, Ian; Pennert, Kjell; Brown, Gina
2011-10-01
To assess magnetic resonance imaging (MRI) and pathologic staging after neoadjuvant therapy for rectal cancer in a prospectively enrolled, multicenter study. In a prospective cohort study, 111 patients who had rectal cancer treated by neoadjuvant therapy were assessed for response by MRI and pathology staging by T, N and circumferential resection margin (CRM) status. Tumor regression grade (TRG) was also assessed by MRI. Overall survival (OS) was estimated by using the Kaplan-Meier product-limit method, and Cox proportional hazards models were used to determine associations between staging of good and poor responders on MRI or pathology and survival outcomes after controlling for patient characteristics. On multivariate analysis, the MRI-assessed TRG (mrTRG) hazard ratios (HRs) were independently significant for survival (HR, 4.40; 95% CI, 1.65 to 11.7) and disease-free survival (DFS; HR, 3.28; 95% CI, 1.22 to 8.80). Five-year survival for poor mrTRG was 27% versus 72% (P = .001), and DFS for poor mrTRG was 31% versus 64% (P = .007). Preoperative MRI-predicted CRM independently predicted local recurrence (LR; HR, 4.25; 95% CI, 1.45 to 12.51). Five-year survival for poor post-treatment pathologic T stage (ypT) was 39% versus 76% (P = .001); DFS for the same was 38% versus 84% (P = .001); and LR for the same was 27% versus 6% (P = .018). The 5-year survival for involved pCRM was 30% versus 59% (P = .001); DFS, 28 versus 62% (P = .02); and LR, 56% versus 10% (P = .001). Pathology node status did not predict outcomes. MRI assessment of TRG and CRM are imaging markers that predict survival outcomes for good and poor responders and provide an opportunity for the multidisciplinary team to offer additional treatment options before planning definitive surgery. Postoperative histopathology assessment of ypT and CRM but not post-treatment N status were important postsurgical predictors of outcome.
AN APPLICATION OF FUNCTIONAL MULTIVARIATE REGRESSION MODEL TO MULTICLASS CLASSIFICATION
Krzyśko, Mirosław; Smaga, Łukasz
2017-01-01
In this paper, the scale response functional multivariate regression model is considered. By using the basis functions representation of functional predictors and regression coefficients, this model is rewritten as a multivariate regression model. This representation of the functional multivariate regression model is used for multiclass classification for multivariate functional data. Computational experiments performed on real labelled data sets demonstrate the effectiveness of the proposed ...
Tam, Vivian W Y; Wang, K; Tam, C M
2008-04-01
Recycled demolished concrete (DC) as recycled aggregate (RA) and recycled aggregate concrete (RAC) is generally suitable for most construction applications. Low-grade applications, including sub-base and roadwork, have been implemented in many countries; however, higher-grade activities are rarely considered. This paper examines relationships among DC characteristics, properties of their RA and strength of their RAC using regression analysis. Ten samples collected from demolition sites are examined. The results show strong correlation among the DC samples, properties of RA and RAC. It should be highlighted that inferior quality of DC will lower the quality of RA and thus their RAC. Prediction of RAC strength is also formulated from the DC characteristics and the RA properties. From that, the RAC performance from DC and RA can be estimated. In addition, RAC design requirements can also be developed at the initial stage of concrete demolition. Recommendations are also given to improve the future concreting practice.
Introduction to the use of regression models in epidemiology.
Bender, Ralf
2009-01-01
Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.
Differentiating high-grade from low-grade chondrosarcoma with MR imaging
Energy Technology Data Exchange (ETDEWEB)
Yoo, Hye Jin; Hong, Sung Hwan; Choi, Ja-Young; Choi, Jung-Ah; Kang, Heung Sik [Seoul National University College of Medicine, Department of Radiology and Institute of Radiation Medicine, Seoul (Korea); Moon, Kyung Chul [Seoul National University College of Medicine, Department of Pathology, Seoul (Korea); Kim, Han-Soo [Seoul National University College of Medicine, Department of Orthopedic Surgery, Seoul (Korea)
2009-12-15
The purpose of the study was to evaluate the MR imaging features that differentiate between low-grade chondrosarcoma (LGCS) and high-grade chondrosarcoma (HGCS) and to determine the most reliable predictors for differentiation. MR images of 42 pathologically proven chondrosarcomas (28 LGCS and 14 HGCS) were retrospectively reviewed. There were 13 male and 29 female patients with an age range of 23-72 years (average age 51 years). On MR images, signal intensity, specific morphological characteristics including entrapped fat, internal lobular architecture, and outer lobular margin, soft tissue mass formation and contrast enhancement pattern were analysed. MR imaging features used to identify LGCS and HGCS were compared using univariate analysis and multivariate stepwise logistic regression analysis. On T1-weighted images, a central area of high signal intensity, which was not seen in LGCS, was frequently observed in HGCS (n = 5, 36%) (p < 0.01). Entrapped fat within the tumour was commonly seen in LGCS (n = 26, 93%), but not in HGCS (n = 1, 4%) (p < 0.01). LGCS more commonly (n = 24, 86%) preserved the characteristic internal lobular structures within the tumour than HGCSs (n = 4, 29%) (p < 0.01). Soft tissue formation was more frequently observed in HGCS (n = 11, 79%) than in LGCS (n = 1, 4%) (p < 0.01). On gadolinium-enhanced images, large central nonenhancing areas were exhibited in only two (7.1%) of LGCS, while HGCS frequently (n = 9, 64%) had a central nonenhancing portion (p < 0.01). Results of multivariate stepwise logistic regression analysis showed that soft tissue formation and entrapped fat within the tumour were the variables that could be used to independently differentiate LGCS from HGCS. There were several MR imaging features of chondrosarcoma that could be helpful in distinguishing HGCS from LGCS. Among them, soft tissue mass formation favoured the diagnosis of HGCS, and entrapped fat within the tumour was highly indicative of LGCS. (orig.)
Differentiating high-grade from low-grade chondrosarcoma with MR imaging
International Nuclear Information System (INIS)
Yoo, Hye Jin; Hong, Sung Hwan; Choi, Ja-Young; Choi, Jung-Ah; Kang, Heung Sik; Moon, Kyung Chul; Kim, Han-Soo
2009-01-01
The purpose of the study was to evaluate the MR imaging features that differentiate between low-grade chondrosarcoma (LGCS) and high-grade chondrosarcoma (HGCS) and to determine the most reliable predictors for differentiation. MR images of 42 pathologically proven chondrosarcomas (28 LGCS and 14 HGCS) were retrospectively reviewed. There were 13 male and 29 female patients with an age range of 23-72 years (average age 51 years). On MR images, signal intensity, specific morphological characteristics including entrapped fat, internal lobular architecture, and outer lobular margin, soft tissue mass formation and contrast enhancement pattern were analysed. MR imaging features used to identify LGCS and HGCS were compared using univariate analysis and multivariate stepwise logistic regression analysis. On T1-weighted images, a central area of high signal intensity, which was not seen in LGCS, was frequently observed in HGCS (n = 5, 36%) (p < 0.01). Entrapped fat within the tumour was commonly seen in LGCS (n = 26, 93%), but not in HGCS (n = 1, 4%) (p < 0.01). LGCS more commonly (n = 24, 86%) preserved the characteristic internal lobular structures within the tumour than HGCSs (n = 4, 29%) (p < 0.01). Soft tissue formation was more frequently observed in HGCS (n = 11, 79%) than in LGCS (n = 1, 4%) (p < 0.01). On gadolinium-enhanced images, large central nonenhancing areas were exhibited in only two (7.1%) of LGCS, while HGCS frequently (n = 9, 64%) had a central nonenhancing portion (p < 0.01). Results of multivariate stepwise logistic regression analysis showed that soft tissue formation and entrapped fat within the tumour were the variables that could be used to independently differentiate LGCS from HGCS. There were several MR imaging features of chondrosarcoma that could be helpful in distinguishing HGCS from LGCS. Among them, soft tissue mass formation favoured the diagnosis of HGCS, and entrapped fat within the tumour was highly indicative of LGCS. (orig.)
Automation of Flight Software Regression Testing
Tashakkor, Scott B.
2016-01-01
NASA is developing the Space Launch System (SLS) to be a heavy lift launch vehicle supporting human and scientific exploration beyond earth orbit. SLS will have a common core stage, an upper stage, and different permutations of boosters and fairings to perform various crewed or cargo missions. Marshall Space Flight Center (MSFC) is writing the Flight Software (FSW) that will operate the SLS launch vehicle. The FSW is developed in an incremental manner based on "Agile" software techniques. As the FSW is incrementally developed, testing the functionality of the code needs to be performed continually to ensure that the integrity of the software is maintained. Manually testing the functionality on an ever-growing set of requirements and features is not an efficient solution and therefore needs to be done automatically to ensure testing is comprehensive. To support test automation, a framework for a regression test harness has been developed and used on SLS FSW. The test harness provides a modular design approach that can compile or read in the required information specified by the developer of the test. The modularity provides independence between groups of tests and the ability to add and remove tests without disturbing others. This provides the SLS FSW team a time saving feature that is essential to meeting SLS Program technical and programmatic requirements. During development of SLS FSW, this technique has proved to be a useful tool to ensure all requirements have been tested, and that desired functionality is maintained, as changes occur. It also provides a mechanism for developers to check functionality of the code that they have developed. With this system, automation of regression testing is accomplished through a scheduling tool and/or commit hooks. Key advantages of this test harness capability includes execution support for multiple independent test cases, the ability for developers to specify precisely what they are testing and how, the ability to add
Laplacian embedded regression for scalable manifold regularization.
Chen, Lin; Tsang, Ivor W; Xu, Dong
2012-06-01
Semi-supervised learning (SSL), as a powerful tool to learn from a limited number of labeled data and a large number of unlabeled data, has been attracting increasing attention in the machine learning community. In particular, the manifold regularization framework has laid solid theoretical foundations for a large family of SSL algorithms, such as Laplacian support vector machine (LapSVM) and Laplacian regularized least squares (LapRLS). However, most of these algorithms are limited to small scale problems due to the high computational cost of the matrix inversion operation involved in the optimization problem. In this paper, we propose a novel framework called Laplacian embedded regression by introducing an intermediate decision variable into the manifold regularization framework. By using ∈-insensitive loss, we obtain the Laplacian embedded support vector regression (LapESVR) algorithm, which inherits the sparse solution from SVR. Also, we derive Laplacian embedded RLS (LapERLS) corresponding to RLS under the proposed framework. Both LapESVR and LapERLS possess a simpler form of a transformed kernel, which is the summation of the original kernel and a graph kernel that captures the manifold structure. The benefits of the transformed kernel are two-fold: (1) we can deal with the original kernel matrix and the graph Laplacian matrix in the graph kernel separately and (2) if the graph Laplacian matrix is sparse, we only need to perform the inverse operation for a sparse matrix, which is much more efficient when compared with that for a dense one. Inspired by kernel principal component analysis, we further propose to project the introduced decision variable into a subspace spanned by a few eigenvectors of the graph Laplacian matrix in order to better reflect the data manifold, as well as accelerate the calculation of the graph kernel, allowing our methods to efficiently and effectively cope with large scale SSL problems. Extensive experiments on both toy and real
7 CFR 810.404 - Grades and grade requirements for corn.
2010-01-01
... 7 Agriculture 7 2010-01-01 2010-01-01 false Grades and grade requirements for corn. 810.404... OFFICIAL UNITED STATES STANDARDS FOR GRAIN United States Standards for Corn Principles Governing the Application of Standards § 810.404 Grades and grade requirements for corn. Grade Minimum test weight per...
7 CFR 810.1804 - Grades and grade requirements for sunflower seed.
2010-01-01
... 7 Agriculture 7 2010-01-01 2010-01-01 false Grades and grade requirements for sunflower seed. 810... AGRICULTURE OFFICIAL UNITED STATES STANDARDS FOR GRAIN United States Standards for Sunflower Seed Principles Governing the Application of Standards § 810.1804 Grades and grade requirements for sunflower seed. Grade...
Attendance Policies and Student Grades
Risen, D. Michael
2007-01-01
The details described in this case study examine the issues related to attendance policies and how such policies might be legally used to affect student grades. Concepts discussed should cause graduate students in educational administration to reflect on the issues presented from various points of view when the students complete an analysis of the…
Grading Rubrics: Hoopla or Help?
Howell, Rebecca J.
2014-01-01
The purpose of the study was to offer some quantitative, multivariate evidence concerning the impact of grading rubric use on academic outcome among American higher education students. Using a pre-post, quasi-experimental research design, cross-sectional data were derived from undergraduates enrolled in an elective during spring and fall 2009 at…
Transportation: Grade 8. Cluster IV.
Calhoun, Olivia H.
A curriculum guide for grade 8, the document is devoted to the occupational cluster "Transportation." It is divided into five units: surface transportation, interstate transportation, air transportation, water transportation, and subterranean transportation (the Metro). Each unit is introduced by a statement of the topic, the unit's…
Noordman, Bo Jan; Spaander, Manon C W; Valkema, Roelf; Wijnhoven, Bas P L; van Berge Henegouwen, Mark I; Shapiro, Joël; Biermann, Katharina; van der Gaast, Ate; van Hillegersberg, Richard; Hulshof, Maarten C C M; Krishnadath, Kausilia K; Lagarde, Sjoerd M; Nieuwenhuijzen, Grard A P; Oostenbrug, Liekele E; Siersema, Peter D; Schoon, Erik J; Sosef, Meindert N; Steyerberg, Ewout W; van Lanschot, J Jan B
2018-05-31
clinical response evaluations and the final pathological response in resection specimens, as shown by the proportion of tumour regression grade (TRG) 3 or 4 (>10% residual carcinoma in the resection specimen) residual tumours that was missed during clinical response evaluations. This study was registered with the Netherlands Trial Register (NTR4834), and has been completed. Between July 22, 2013, and Dec 28, 2016, 219 patients were included, 207 of whom were included in the analyses. Eight of 26 TRG3 or TRG4 tumours (31% [95% CI 17-50]) were missed by endoscopy with regular biopsies and fine-needle aspiration. Four of 41 TRG3 or TRG4 tumours (10% [95% CI 4-23]) were missed with bite-on-bite biopsies and fine-needle aspiration. Endoscopic ultrasonography with maximum tumour thickness measurement missed TRG3 or TRG4 residual tumours in 11 of 39 patients (28% [95% CI 17-44]). PET-CT missed six of 41 TRG3 or TRG4 tumours (15% [95% CI 7-28]). PET-CT detected interval distant histologically proven metastases in 18 (9%) of 190 patients (one squamous cell carcinoma, 17 adenocarcinomas). After neoadjuvant chemoradiotherapy for oesophageal cancer, clinical response evaluation with endoscopic ultrasonography, bite-on-bite biopsies, and fine-needle aspiration of suspicious lymph nodes was adequate for detection of locoregional residual disease, with PET-CT for detection of interval metastases. Active surveillance with this combination of diagnostic modalities is now being assessed in a phase 3 randomised controlled trial (SANO trial; Netherlands Trial Register NTR6803). Dutch Cancer Society. Copyright © 2018 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Qiutong Jin
2016-06-01
Full Text Available Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK and geographically weighted regression Kriging (GWRK methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM, normalized difference vegetation index (NDVI, solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.
Hecht, Jeffrey B.
The analysis of regression residuals and detection of outliers are discussed, with emphasis on determining how deviant an individual data point must be to be considered an outlier and the impact that multiple suspected outlier data points have on the process of outlier determination and treatment. Only bivariate (one dependent and one independent)…
A rotor optimization using regression analysis
Giansante, N.
1984-01-01
The design and development of helicopter rotors is subject to the many design variables and their interactions that effect rotor operation. Until recently, selection of rotor design variables to achieve specified rotor operational qualities has been a costly, time consuming, repetitive task. For the past several years, Kaman Aerospace Corporation has successfully applied multiple linear regression analysis, coupled with optimization and sensitivity procedures, in the analytical design of rotor systems. It is concluded that approximating equations can be developed rapidly for a multiplicity of objective and constraint functions and optimizations can be performed in a rapid and cost effective manner; the number and/or range of design variables can be increased by expanding the data base and developing approximating functions to reflect the expanded design space; the order of the approximating equations can be expanded easily to improve correlation between analyzer results and the approximating equations; gradients of the approximating equations can be calculated easily and these gradients are smooth functions reducing the risk of numerical problems in the optimization; the use of approximating functions allows the problem to be started easily and rapidly from various initial designs to enhance the probability of finding a global optimum; and the approximating equations are independent of the analysis or optimization codes used.
Regression analysis of sparse asynchronous longitudinal data.
Cao, Hongyuan; Zeng, Donglin; Fine, Jason P
2015-09-01
We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data. For models with either time invariant or time-dependent coefficients, the estimators are consistent and asymptotically normal but converge at slower rates than those achieved with synchronous data. Simulation studies evidence that the methods perform well with realistic sample sizes and may be superior to a naive application of methods for synchronous data based on an ad hoc last value carried forward approach. The practical utility of the methods is illustrated on data from a study on human immunodeficiency virus.
Free Software Development. 1. Fitting Statistical Regressions
Directory of Open Access Journals (Sweden)
Lorentz JÄNTSCHI
2002-12-01
Full Text Available The present paper is focused on modeling of statistical data processing with applications in field of material science and engineering. A new method of data processing is presented and applied on a set of 10 Ni–Mn–Ga ferromagnetic ordered shape memory alloys that are known to exhibit phonon softening and soft mode condensation into a premartensitic phase prior to the martensitic transformation itself. The method allows to identify the correlations between data sets and to exploit them later in statistical study of alloys. An algorithm for computing data was implemented in preprocessed hypertext language (PHP, a hypertext markup language interface for them was also realized and put onto comp.east.utcluj.ro educational web server, and it is accessible via http protocol at the address http://vl.academicdirect.ro/applied_statistics/linear_regression/multiple/v1.5/. The program running for the set of alloys allow to identify groups of alloys properties and give qualitative measure of correlations between properties. Surfaces of property dependencies are also fitted.
Kepler AutoRegressive Planet Search (KARPS)
Caceres, Gabriel
2018-01-01
One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The Kepler AutoRegressive Planet Search (KARPS) project implements statistical methodology associated with autoregressive processes (in particular, ARIMA and ARFIMA) to model stellar lightcurves in order to improve exoplanet transit detection. We also develop a novel Transit Comb Filter (TCF) applied to the AR residuals which provides a periodogram analogous to the standard Box-fitting Least Squares (BLS) periodogram. We train a random forest classifier on known Kepler Objects of Interest (KOIs) using select features from different stages of this analysis, and then use ROC curves to define and calibrate the criteria to recover the KOI planet candidates with high fidelity. These statistical methods are detailed in a contributed poster (Feigelson et al., this meeting).These procedures are applied to the full DR25 dataset of NASA’s Kepler mission. Using the classification criteria, a vast majority of known KOIs are recovered and dozens of new KARPS Candidate Planets (KCPs) discovered, including ultra-short period exoplanets. The KCPs will be briefly presented and discussed.
DNBR Prediction Using a Support Vector Regression
International Nuclear Information System (INIS)
Yang, Heon Young; Na, Man Gyun
2008-01-01
PWRs (Pressurized Water Reactors) generally operate in the nucleate boiling state. However, the conversion of nucleate boiling into film boiling with conspicuously reduced heat transfer induces a boiling crisis that may cause the fuel clad melting in the long run. This type of boiling crisis is called Departure from Nucleate Boiling (DNB) phenomena. Because the prediction of minimum DNBR in a reactor core is very important to prevent the boiling crisis such as clad melting, a lot of research has been conducted to predict DNBR values. The object of this research is to predict minimum DNBR applying support vector regression (SVR) by using the measured signals of a reactor coolant system (RCS). The SVR has extensively and successfully been applied to nonlinear function approximation like the proposed problem for estimating DNBR values that will be a function of various input variables such as reactor power, reactor pressure, core mass flowrate, control rod positions and so on. The minimum DNBR in a reactor core is predicted using these various operating condition data as the inputs to the SVR. The minimum DBNR values predicted by the SVR confirm its correctness compared with COLSS values
Smartphone-based grading of apple quality
Li, Xianglin; Li, Ting
2018-02-01
Apple quality grading is a critical issue in apple industry which is one economical pillar of many countries. Artificial grading is inefficient and of poor accuracy. Here we proposed to develop a portable, convenient, real-time, and low cost method aimed at grading apple. Color images of the apples were collected with a smartphone and the grade of sampled apple was assessed by a customized smartphone app, which offered the functions translating RGB color values of the apple to color grade and translating the edge of apple image to weight grade. The algorithms are based on modeling with a large number of apple image at different grades. The apple grade data evaluated by the smartphone are in accordance with the actual data. This study demonstrated the potential of smart phone in apple quality grading/online monitoring at gathering and transportation stage for apple industry.
Sirenomelia and severe caudal regression syndrome.
Seidahmed, Mohammed Z; Abdelbasit, Omer B; Alhussein, Khalid A; Miqdad, Abeer M; Khalil, Mohammed I; Salih, Mustafa A
2014-12-01
To describe cases of sirenomelia and severe caudal regression syndrome (CRS), to report the prevalence of sirenomelia, and compare our findings with the literature. 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. The cases were identified as part of a study of neural tube defects during that period. A literature search was conducted using MEDLINE. During the 22-year study period, the total number of deliveries was 124,933 out of whom, 4 patients with sirenomelia, and 2 patients with severe forms of CRS were identified. All the patients with sirenomelia had single umbilical artery, and none were the infant of a diabetic mother. One patient was a twin, and another was one of triplets. The 2 patients with CRS were sisters, their mother suffered from type II diabetes mellitus and morbid obesity on insulin, and neither of them had a single umbilical artery. Other associated anomalies with sirenomelia included an absent radius, thumb, and index finger in one patient, Potter's syndrome, abnormal ribs, microphthalmia, congenital heart disease, hypoplastic lungs, and diaphragmatic hernia. The prevalence of sirenomelia (3.2 per 100,000) is high compared with the international prevalence of one per 100,000. Both cases of CRS were infants of type II diabetic mother with poor control, supporting the strong correlation of CRS and maternal diabetes.
Gaussian process regression for tool wear prediction
Kong, Dongdong; Chen, Yongjie; Li, Ning
2018-05-01
To realize and accelerate the pace of intelligent manufacturing, this paper presents a novel tool wear assessment technique based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and Gaussian process regression (GPR) for real-timely and accurately monitoring the in-process tool wear parameters (flank wear width). The KPCA_IRBF is a kind of new nonlinear dimension-increment technique and firstly proposed for feature fusion. The tool wear predictive value and the corresponding confidence interval are both provided by utilizing the GPR model. Besides, GPR performs better than artificial neural networks (ANN) and support vector machines (SVM) in prediction accuracy since the Gaussian noises can be modeled quantitatively in the GPR model. However, the existence of noises will affect the stability of the confidence interval seriously. In this work, the proposed KPCA_IRBF technique helps to remove the noises and weaken its negative effects so as to make the confidence interval compressed greatly and more smoothed, which is conducive for monitoring the tool wear accurately. Moreover, the selection of kernel parameter in KPCA_IRBF can be easily carried out in a much larger selectable region in comparison with the conventional KPCA_RBF technique, which helps to improve the efficiency of model construction. Ten sets of cutting tests are conducted to validate the effectiveness of the presented tool wear assessment technique. The experimental results show that the in-process flank wear width of tool inserts can be monitored accurately by utilizing the presented tool wear assessment technique which is robust under a variety of cutting conditions. This study lays the foundation for tool wear monitoring in real industrial settings.
Barbaro, Brunella; Vitale, Renata; Valentini, Vincenzo; Illuminati, Sonia; Vecchio, Fabio M; Rizzo, Gianluca; Gambacorta, Maria Antonietta; Coco, Claudio; Crucitti, Antonio; Persiani, Roberto; Sofo, Luigi; Bonomo, Lorenzo
2012-06-01
To prospectively monitor the response in patients with locally advanced nonmucinous rectal cancer after chemoradiotherapy (CRT) using diffusion-weighted magnetic resonance imaging. The histopathologic finding was the reference standard. The institutional review board approved the present study. A total of 62 patients (43 men and 19 women; mean age, 64 years; range, 28-83) provided informed consent. T(2)- and diffusion-weighted magnetic resonance imaging scans (b value, 0 and 1,000 mm(2)/s) were acquired before, during (mean 12 days), and 6-8 weeks after CRT. We compared the median apparent diffusion coefficients (ADCs) between responders and nonresponders and examined the associations with the Mandard tumor regression grade (TRG). The postoperative nodal status (ypN) was evaluated. The Mann-Whitney/Wilcoxon two-sample test was used to evaluate the relationships among the pretherapy ADCs, extramural vascular invasion, early percentage of increases in ADCs, and preoperative ADCs. Low pretreatment ADCs (23% ADC increase had a 96.3% negative predictive value for TRG 4. In 9 of 16 complete responders, CRT-related tumor downsizing prevented ADC evaluations. The preoperative ADCs were significantly different (p = .0012) between the patients with and without downstaging (preoperative ADC ≥1.4 × 10(-3)mm(2)/s showed a positive and negative predictive value of 78.9% and 61.8%, respectively, for response assessment). The TRG 1 and TRG 2-4 groups were not significantly different. Diffusion-weighted magnetic resonance imaging seems to be a promising tool for monitoring the response to CRT. Copyright © 2012 Elsevier Inc. All rights reserved.
Bensignor, T; Brouquet, A; Dariane, C; Thirot-Bidault, A; Lazure, T; Julié, C; Nordlinger, B; Penna, C; Benoist, S
2015-06-01
Pathological response to chemotherapy without pelvic irradiation is not well defined in rectal cancer. This study aimed to evaluate the objective pathological response to preoperative chemotherapy without pelvic irradiation in middle or low locally advanced rectal cancer (LARC). Between 2008 and 2013, 22 patients with middle or low LARC (T3/4 and/or N+ and circumferential resection margin rectal resection after preoperative chemotherapy. The pathological response of rectal tumour was analysed according to the Rödel tumour regression grading (TRG) system. Predictive factors of objective pathological response (TRG 2-4) were analysed. All patients underwent rectal surgery after a median of six cycles of preoperative chemotherapy. Of these, 20 (91%) had sphincter saving surgery and an R0 resection. Twelve (55%) patients had an objective pathological response (TRG 2-4), including one complete response. Poor response (TRG 0-1) to chemotherapy was noted in 10 (45%) patients. In univariate analyses, none of the factors examined was found to be predictive of an objective pathological response to chemotherapy. At a median follow-up of 37.2 months, none of the 22 patients experienced local recurrence. Of the 19 patients with Stage IV rectal cancer, 15 (79%) had liver surgery with curative intent. Preoperative chemotherapy without pelvic irradiation is associated with objective pathological response and adequate local control in selected patients with LARC. Further prospective controlled studies will address the question of whether it can be used as a valuable alternative to radiochemotherapy in LARC. Colorectal Disease © 2014 The Association of Coloproctology of Great Britain and Ireland.
DEFF Research Database (Denmark)
Appelt, A. L.; Ploen, J.; Vogelius, I. R.
2013-01-01
estimated radiation dose-response curves for various grades of tumor regression after preoperative CRT. Methods and Materials: A total of 222 patients, treated with consistent chemotherapy and radiation therapy techniques, were considered for the analysis. Radiation therapy consisted of a combination...... of external-beam radiation therapy and brachytherapy. Response at the time of operation was evaluated from the histopathologic specimen and graded on a 5-point scale (TRG1-5). The probability of achieving complete, major, and partial response was analyzed by ordinal logistic regression, and the effect...... of including clinical parameters in the model was examined. The radiation dose-response relationship for a specific grade of histopathologic tumor regression was parameterized in terms of the dose required for 50% response, D-50,D-i, and the normalized dose-response gradient, gamma(50,i). Results: A highly...
Prognostic implications of 2005 Gleason grade modification
DEFF Research Database (Denmark)
Thomsen, Frederik Birkebæk; Folkvaljon, Yasin; Brasso, Klaus
2016-01-01
,890 men assessed with the modified Gleason classification, diagnosed between 2003 and 2007, underwent primary RP. Histopathology was reported according to the Gleason Grading Groups (GGG): GGG1 = Gleason score (GS) 6, GGG2 = GS 7(3 + 4), GGG3 = GS 7(4 + 3), GGG4 = GS 8 and GGG5 = GS 9-10. Cumulative...... incidence and multivariable Cox proportional hazards regression models were used to assess difference in BCR. RESULTS: The cumulative incidence of BCR was lower using the modified compared to the original classification: GGG2 (16% vs. 23%), GGG3 (21% vs. 35%) and GGG4 (18% vs. 34%), respectively. Risk......OBJECTIVE: To assess the impact of the 2005 modification of the Gleason classification on risk of biochemical recurrence (BCR) after radical prostatectomy (RP). PATIENTS AND METHODS: In the Prostate Cancer data Base Sweden (PCBaSe), 2,574 men assessed with the original Gleason classification and 1...
Keith, Timothy Z
2014-01-01
Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. Covers both MR and SEM, while explaining their relevance to one another Also includes path analysis, confirmatory factor analysis, and latent growth modeling Figures and tables throughout provide examples and illustrate key concepts and techniques For additional resources, please visit: http://tzkeith.com/.
Anaplasia and grading in medulloblastomas.
Eberhart, Charles G; Burger, Peter C
2003-07-01
The variable clinical outcomes of medulloblastoma patients have prompted a search for markers with which to tailor therapies to individuals. In this review, we discuss clinical, histological and molecular features that can be used in such treatment customization, focusing on how histopathological grading can impact both patient care and research on the molecular basis of CNS embryonal tumors. Medulloblastomas span a histological spectrum ending in overtly malignant large cell/anaplastic lesions characterized by increased nuclear size, marked cytological anaplasia, and increased mitotic and apoptotic rates. These "high-grade" lesions make up approximately one quarter of medulloblastomas, and recur and metastasize more frequently than tumors lacking anaplasia. We believe anaplastic change represents a type of malignant progression common to many medulloblastoma subtypes and to other CNS embryonal lesions as well. Correlation of these histological changes with the accumulation of genetic events suggests a model for the histological and molecular progression of medulloblastoma.
Gradings on simple Lie algebras
Elduque, Alberto
2013-01-01
Gradings are ubiquitous in the theory of Lie algebras, from the root space decomposition of a complex semisimple Lie algebra relative to a Cartan subalgebra to the beautiful Dempwolff decomposition of E_8 as a direct sum of thirty-one Cartan subalgebras. This monograph is a self-contained exposition of the classification of gradings by arbitrary groups on classical simple Lie algebras over algebraically closed fields of characteristic not equal to 2 as well as on some nonclassical simple Lie algebras in positive characteristic. Other important algebras also enter the stage: matrix algebras, the octonions, and the Albert algebra. Most of the presented results are recent and have not yet appeared in book form. This work can be used as a textbook for graduate students or as a reference for researchers in Lie theory and neighboring areas.
Grading of quality assurance requirements
International Nuclear Information System (INIS)
1991-01-01
The present Manual provides guidance and illustrative examples for applying a method by which graded quality assurance requirements may be determined and adapted to the items and services of a nuclear power plant in conformance with the requirements of the IAEA Nuclear Safety Standards (NUSS) Code and Safety Guides on quality assurance. The Manual replaces the previous publication IAEA-TECDOC-303 on the same subject. Various methods of grading quality assurance are available in a number of Member States. During the development of the present Manual it was not considered practical to attempt to resolve the differences between those methods and it was preferred to identify and benefit from the good practices available in all the methods. The method presented in this Manual deals with the aspects of management, documentation, control, verification and administration which affect quality. 1 fig., 4 tabs
Kepler AutoRegressive Planet Search
Caceres, Gabriel Antonio; Feigelson, Eric
2016-01-01
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. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; AR-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. The analysis procedures of the project are applied to a portion of the publicly available Kepler light curve data for the full 4-year mission duration. Tests of the methods have been made on a subset of Kepler Objects of Interest (KOI) systems, classified both as planetary `candidates' and `false positives' by the Kepler Team, as well as a random sample of unclassified systems. We find that the ARMA-type modeling successfully reduces the stellar variability, by a factor of 10 or more in active stars and by smaller factors in more quiescent stars. A typical quiescent Kepler star has an interquartile range (IQR) of ~10 e-/sec, which may improve slightly after modeling, while those with IQR ranging from 20 to 50 e-/sec, have improvements from 20% up to 70%. High activity stars (IQR exceeding 100) markedly improve. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. Our findings to date on real
Detection of epistatic effects with logic regression and a classical linear regression model.
Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata
2014-02-01
To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.
Ultrasound elastography in patients with rectal cancer treated with chemoradiation
DEFF Research Database (Denmark)
Rafaelsen, S R; Vagn-Hansen, C; Sørensen, T
2013-01-01
OBJECTIVE: The current literature has described several predictive markers in rectal cancer patients treated with chemoradiation, but so far none of them have been validated for clinical use. The purpose of the present study was to compare quantitative elastography based on ultrasound measurements...... in the course of chemoradiation with tumor response based on T stage classification and the Mandard tumor regression grading (TRG). MATERIALS AND METHODS: We prospectively examined 31 patients with rectal cancer planned for high dose radiochemotherapy. The tumor and the mesorectal fat elasticity were measured...
Loss Aversion in the Classroom: A Nudge towards a Better Grade?
Grijalva, Therese; Koford, Brandon C.; Parkhurst, Gregory
2018-01-01
Using data from 499 students over 12 sections, 2 courses, and 3 instructors, we estimate the effect of loss aversion on the probability of turning in extra credit assignments and the effect on the overall grade. Regression results indicate no effect of loss aversion on the probability of turning in extra credit assignments and no effect on a…
The Effect of Attending Tutoring on Course Grades in Calculus I
Rickard, Brian; Mills, Melissa
2018-01-01
Tutoring centres are common in universities in the United States, but there are few published studies that statistically examine the effects of tutoring on student success. This study utilizes multiple regression analysis to model the effect of tutoring attendance on final course grades in Calculus I. Our model predicted that every three visits to…
Predictors of Grades for Black Americans in a Non-Calculus, Preprofessional Physics Sequence.
Vincent, Harold A.; And Others
Variables to predict grades in a noncalculus, preprofessional college physics course at Xavier University of Louisiana, a historically-black institution, were identified using linear regression. The two-semester, noncalculus physics course emphasizes the application of physics in the health professions. The study population consisted of 123…
Lee, Jennifer
2012-01-01
The intent of this study was to examine the relationship between media multitasking orientation and grade point average. The study utilized a mixed-methods approach to investigate the research questions. In the quantitative section of the study, the primary method of statistical analyses was multiple regression. The independent variables for the…
Impact of School Violence on Youth Alcohol Abuse: Differences Based on Gender and Grade Level
Vidourek, Rebecca A.; King, Keith A.; Merianos, Ashley L.
2016-01-01
The purpose of this study was to examine the impact of school violence on recent alcohol use and episodic heavy drinking among seventh- through 12th-grade students. A total of 54,631 students completed a survey assessing substance use and other risky behaviors. Logistic regression analyses were conducted to examine the research questions. Results…
Predictors of Quality Verbal Engagement in Third-Grade Literature Discussions
Young, Chase
2014-01-01
This study investigates how reading ability and personality traits predict the quality of verbal discussions in peer-led literature circles. Third grade literature discussions were recorded, transcribed, and coded. The coded statements and questions were quantified into a quality of engagement score. Through multiple linear regression, the…
A Study of the Stability of School Effectiveness Measures across Grades and Subject Areas.
Mandeville, Garrett K.; Anderson, Lorin W.
School effectiveness indices (SEIs), based on regressing test performance onto earlier test performance and a socioeconomic status measure, were obtained for eight subject-grade combinations from 485 South Carolina elementary schools. The analysis involved school means based on longitudinally matched student data. Reading and mathematics…
Saltonstall, Margot
2013-01-01
This study seeks to advance and expand research on college student success. Using multinomial logistic regression analysis, the study investigates the contribution of psychosocial variables above and beyond traditional achievement and demographic measures to predicting first-semester college grade point average (GPA). It also investigates if…
Do Nondomestic Undergraduates Choose a Major Field in Order to Maximize Grade Point Averages?
Bergman, Matthew E.; Fass-Holmes, Barry
2016-01-01
The authors investigated whether undergraduates attending an American West Coast public university who were not U.S. citizens (nondomestic) maximized their grade point averages (GPA) through their choice of major field. Multiple regression hierarchical linear modeling analyses showed that major field's effect size was small for these…
Grade Expectations: Rationality and Overconfidence
Magnus, Jan R.; Peresetsky, Anatoly A.
2018-01-01
Confidence and overconfidence are essential aspects of human nature, but measuring (over)confidence is not easy. Our approach is to consider students' forecasts of their exam grades. Part of a student's grade expectation is based on the student's previous academic achievements; what remains can be interpreted as (over)confidence. Our results are based on a sample of about 500 second-year undergraduate students enrolled in a statistics course in Moscow. The course contains three exams and each student produces a forecast for each of the three exams. Our models allow us to estimate overconfidence quantitatively. Using these models we find that students' expectations are not rational and that most students are overconfident, in agreement with the general literature. Less obvious is that overconfidence helps: given the same academic achievement students with larger confidence obtain higher exam grades. Female students are less overconfident than male students, their forecasts are more rational, and they are also faster learners in the sense that they adjust their expectations more rapidly. PMID:29375449
Grade Expectations: Rationality and Overconfidence
Directory of Open Access Journals (Sweden)
Jan R. Magnus
2018-01-01
Full Text Available Confidence and overconfidence are essential aspects of human nature, but measuring (overconfidence is not easy. Our approach is to consider students' forecasts of their exam grades. Part of a student's grade expectation is based on the student's previous academic achievements; what remains can be interpreted as (overconfidence. Our results are based on a sample of about 500 second-year undergraduate students enrolled in a statistics course in Moscow. The course contains three exams and each student produces a forecast for each of the three exams. Our models allow us to estimate overconfidence quantitatively. Using these models we find that students' expectations are not rational and that most students are overconfident, in agreement with the general literature. Less obvious is that overconfidence helps: given the same academic achievement students with larger confidence obtain higher exam grades. Female students are less overconfident than male students, their forecasts are more rational, and they are also faster learners in the sense that they adjust their expectations more rapidly.
Diagnostic Algorithm to Reflect Regressive Changes of Human Papilloma Virus in Tissue Biopsies
Lhee, Min Jin; Cha, Youn Jin; Bae, Jong Man; Kim, Young Tae
2014-01-01
Purpose Landmark indicators have not yet to be developed to detect the regression of cervical intraepithelial neoplasia (CIN). We propose that quantitative viral load and indicative histological criteria can be used to differentiate between atypical squamous cells of undetermined significance (ASCUS) and a CIN of grade 1. Materials and Methods We collected 115 tissue biopsies from women who tested positive for the human papilloma virus (HPV). Nine morphological parameters including nuclear size, perinuclear halo, hyperchromasia, typical koilocyte (TK), abortive koilocyte (AK), bi-/multi-nucleation, keratohyaline granules, inflammation, and dyskeratosis were examined for each case. Correlation analyses, cumulative logistic regression, and binary logistic regression were used to determine optimal cut-off values of HPV copy numbers. The parameters TK, perinuclear halo, multi-nucleation, and nuclear size were significantly correlated quantitatively to HPV copy number. Results An HPV loading number of 58.9 and AK number of 20 were optimal to discriminate between negative and subtle findings in biopsies. An HPV loading number of 271.49 and AK of 20 were optimal for discriminating between equivocal changes and obvious koilocytosis. Conclusion We propose that a squamous epithelial lesion with AK of >20 and quantitative HPV copy number between 58.9-271.49 represents a new spectrum of subtle pathological findings, characterized by AK in ASCUS. This can be described as a distinct entity and called "regressing koilocytosis". PMID:24532500
Determining factors influencing survival of breast cancer by fuzzy logistic regression model.
Nikbakht, Roya; Bahrampour, Abbas
2017-01-01
Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morphology, grade, age, and treatments (surgery, radiotherapy, and chemotherapy) were applied in the fuzzy logistic regression model. Performance of model was determined in terms of mean degree of membership (MDM). The study results showed that almost 41% of patients were in neoplasm and malignant group and more than two-third of them were still alive after 5-year follow-up. Based on the fuzzy logistic model, the most important factors influencing survival were chemotherapy, morphology, and radiotherapy, respectively. Furthermore, the MDM criteria show that the fuzzy logistic regression have a good fit on the data (MDM = 0.86). Fuzzy logistic regression model showed that chemotherapy is more important than radiotherapy in survival of patients with breast cancer. In addition, another ability of this model is calculating possibilistic odds of survival in cancer patients. The results of this study can be applied in clinical research. Furthermore, there are few studies which applied the fuzzy logistic models. Furthermore, we recommend using this model in various research areas.
International Nuclear Information System (INIS)
Alvarez R, J.T.; Morales P, R.
1992-06-01
The absorbed dose for equivalent soft tissue is determined,it is imparted by ophthalmologic applicators, ( 90 Sr/ 90 Y, 1850 MBq) using an extrapolation chamber of variable electrodes; when estimating the slope of the extrapolation curve using a simple lineal regression model is observed that the dose values are underestimated from 17.7 percent up to a 20.4 percent in relation to the estimate of this dose by means of a regression model polynomial two grade, at the same time are observed an improvement in the standard error for the quadratic model until in 50%. Finally the global uncertainty of the dose is presented, taking into account the reproducibility of the experimental arrangement. As conclusion it can infers that in experimental arrangements where the source is to contact with the extrapolation chamber, it was recommended to substitute the lineal regression model by the quadratic regression model, in the determination of the slope of the extrapolation curve, for more exact and accurate measurements of the absorbed dose. (Author)
Measure of uncertainty in regional grade variability
Tutmez, B.; Kaymak, U.; Melin, P.; Castillo, O.; Gomez Ramirez, E.; Kacprzyk, J.; Pedrycz, W.
2007-01-01
Because the geological events are neither homogeneous nor isotropic, the geological investigations are characterized by particularly high uncertainties. This paper presents a hybrid methodology for measuring of uncertainty in regional grade variability. In order to evaluate the fuzziness in grade
Sparse Regression by Projection and Sparse Discriminant Analysis
Qi, Xin; Luo, Ruiyan; Carroll, Raymond J.; Zhao, Hongyu
2015-01-01
predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths
Poisson Mixture Regression Models for Heart Disease Prediction.
Mufudza, Chipo; Erol, Hamza
2016-01-01
Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.
Dimension Reduction and Discretization in Stochastic Problems by Regression Method
DEFF Research Database (Denmark)
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, ...
Linear regression crash prediction models : issues and proposed solutions.
2010-05-01
The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...
An Additive-Multiplicative Cox-Aalen Regression Model
DEFF Research Database (Denmark)
Scheike, Thomas H.; Zhang, Mei-Jie
2002-01-01
Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects...
Logistic Regression Modeling of Diminishing Manufacturing Sources for Integrated Circuits
National Research Council Canada - National Science Library
Gravier, Michael
1999-01-01
.... The research identified logistic regression as a powerful tool for analysis of DMSMS and further developed twenty models attempting to identify the "best" way to model and predict DMSMS using logistic regression...
Model-based Quantile Regression for Discrete Data
Padellini, Tullia; Rue, Haavard
2018-01-01
Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite
The grading management of the quality assurance
International Nuclear Information System (INIS)
Ma Xiaozheng; Han Shufang; Yu Bei; Tian Xuehang
2009-01-01
This paper introduces the quality assurance grading management of the items, services and technology process on nuclear power plants (nuclear island, conventional island, BOP), such as the requirements and aim in the related code, guide, technical document, the requirements for the related units, the grading principle and grading, the considering method for the differences of QA requirements of the each QA grand, as well as the status and propositions in the QA grading management in China. (authors)
The MIDAS Touch: Mixed Data Sampling Regression Models
Ghysels, Eric; Santa-Clara, Pedro; Valkanov, Rossen
2004-01-01
We introduce Mixed Data Sampling (henceforth MIDAS) regression models. The regressions involve time series data sampled at different frequencies. Technically speaking MIDAS models specify conditional expectations as a distributed lag of regressors recorded at some higher sampling frequencies. We examine the asymptotic properties of MIDAS regression estimation and compare it with traditional distributed lag models. MIDAS regressions have wide applicability in macroeconomics and ï¿½nance.
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...
van Rhijn, Bas W G; van Leenders, Geert J L H; Ooms, Bert C M; Kirkels, Wim J; Zlotta, Alexandre R; Boevé, Egbert R; Jöbsis, Adriaan C; van der Kwast, Theo H
2010-06-01
A new grading system for bladder cancer (BCa) was adopted in 2004 to reduce observer variability and provide better prognostic information. We compared the World Health Organization (WHO) 1973 and 2004 systems for observer variability and prognosis. Slides of 173 primary non-muscle-invasive BCa were reviewed two times by four pathologists. Intra- and interobserver variability were assessed using κ statistics. We determined the mean grade (eg, G1/low malignant potential is 1 grade point, G2/low grade is 2 grade points) of the pathologists per grading cycle. Kaplan-Meier analyses were applied for prediction of recurrence and progression. For WHO 2004 and 1973 grading, the agreement between the pathologists was 39-74% (κ: 0.14-0.58) and 39-64% (κ: 0.15-0.41), respectively. The intraobserver agreement varied from 71% to 88% (κ: 0.55-0.81). The mean grade of a pathologist was constant (difference below 0.1 grade point) irrespective of the grading system. Conversely, mean-grade differences among the pathologists were high, up to 0.7 grade point. The mean grades for the WHO 2004 system were 0.3-0.5 grade point higher than those of WHO 1973. Mean grade distinguished low and high graders among the pathologists and was strongly linked with risk of progression in each grade category. The variation in mean grade among individual pathologists exceeded the grade shift caused by WHO 2004 grading. Knowledge of the pathologist's mean grade allows a better assessment of the prognostic value of grading. Mean grade has the potential to become a tool for quality assurance in pathology. Copyright © 2009 European Association of Urology. Published by Elsevier B.V. All rights reserved.
Examining Text Complexity in the Early Grades
Fitzgerald, Jill; Elmore, Jeff; Hiebert, Elfrieda H.; Koons, Heather H.; Bowen, Kimberly; Sanford-Moore, Eleanor E.; Stenner, A. Jackson
2016-01-01
The Common Core raises the stature of texts to new heights, creating a hubbub. The fuss is especially messy at the early grades, where children are expected to read more complex texts than in the past. But early-grades teachers have been given little actionable guidance about text complexity. The authors recently examined early-grades texts to…
Registration Patterns Under Two Different Grading Systems.
Remley, Audrey W.
In the early 1960's, Westminster College adopted a new grading system, with the traditional grade levels of A, B, C, D, and F converted to DN (Distinction), HP (High Pass), P (Pass), and NC (No Credit). NC replaced both D and F of the old system, and grade point averages were abolished, in an effort to encourage students to register in more…
Forecasting and recruitment in graded manpower systems
van Nunen, J.A.E.E.; Wessels, J.
1977-01-01
In this paper a generalized Markov model is introduced to describe the dynamic behaviour of an individual employee in a graded Manpower system. Characteristics like the employee's grade, his educational level, his age and the time spent in his actual grade, can be incorporated in the Markov model.
Demystify Learning Expectations to Address Grade Inflation
Hodges, Linda C.
2014-01-01
This article describes the subject of "grade inflation," a reference to educators giving higher grades to student work than their expectations for student achievement warrant. Of the many reasons why this practice happens, Hodges specifically discusses inflating grades as "a natural consequence" when the faculty really…
Grade Inflation: An Issue for Higher Education?
Caruth, Donald L.; Caruth, Gail D.
2013-01-01
Grade inflation impacts university credibility, student courses of study, choices of institution, and other areas. There has been an upward shift in grades without a corresponding upward shift in knowledge gained. Some of the most frequently mentioned causes of grade inflation are: (1) student evaluations of professors; (2) student teacher…
Does Education Corrupt? Theories of Grade Inflation
Oleinik, Anton
2009-01-01
Several theories of grade inflation are discussed in this review article. It is argued that grade inflation results from the substitution of criteria specific to the search for truth by criteria of quality control generated outside of academia. Particular mechanisms of the grade inflation that occurs when a university is transformed into a…
Using Dominance Analysis to Determine Predictor Importance in Logistic Regression
Azen, Razia; Traxel, Nicole
2009-01-01
This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…
Meta-Modeling by Symbolic Regression and Pareto Simulated Annealing
Stinstra, E.; Rennen, G.; Teeuwen, G.J.A.
2006-01-01
The subject of this paper is a new approach to Symbolic Regression.Other publications on Symbolic Regression use Genetic Programming.This paper describes an alternative method based on Pareto Simulated Annealing.Our method is based on linear regression for the estimation of constants.Interval
Wanvarie, Samkaew; Sathapatayavongs, Boonmee
2007-09-01
The aim of this paper was to assess factors that predict students' performance in the Medical Licensing Examination of Thailand (MLET) Step1 examination. The hypothesis was that demographic factors and academic records would predict the students' performance in the Step1 Licensing Examination. A logistic regression analysis of demographic factors (age, sex and residence) and academic records [high school grade point average (GPA), National University Entrance Examination Score and GPAs of the pre-clinical years] with the MLET Step1 outcome was accomplished using the data of 117 third-year Ramathibodi medical students. Twenty-three (19.7%) students failed the MLET Step1 examination. Stepwise logistic regression analysis showed that the significant predictors of MLET Step1 success/failure were residence background and GPAs of the second and third preclinical years. For students whose sophomore and third-year GPAs increased by an average of 1 point, the odds of passing the MLET Step1 examination increased by a factor of 16.3 and 12.8 respectively. The minimum GPAs for students from urban and rural backgrounds to pass the examination were estimated from the equation (2.35 vs 2.65 from 4.00 scale). Students from rural backgrounds and/or low-grade point averages in their second and third preclinical years of medical school are at risk of failing the MLET Step1 examination. They should be given intensive tutorials during the second and third pre-clinical years.
Li, Jiangtong; Luo, Yongdao; Dai, Honglin
2018-01-01
Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR's analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR's result better than PLSR.
Morales, Esteban; de Leon, John Mark S; Abdollahi, Niloufar; Yu, Fei; Nouri-Mahdavi, Kouros; Caprioli, Joseph
2016-03-01
The study was conducted to evaluate threshold smoothing algorithms to enhance prediction of the rates of visual field (VF) worsening in glaucoma. We studied 798 patients with primary open-angle glaucoma and 6 or more years of follow-up who underwent 8 or more VF examinations. Thresholds at each VF location for the first 4 years or first half of the follow-up time (whichever was greater) were smoothed with clusters defined by the nearest neighbor (NN), Garway-Heath, Glaucoma Hemifield Test (GHT), and weighting by the correlation of rates at all other VF locations. Thresholds were regressed with a pointwise exponential regression (PER) model and a pointwise linear regression (PLR) model. Smaller root mean square error (RMSE) values of the differences between the observed and the predicted thresholds at last two follow-ups indicated better model predictions. The mean (SD) follow-up times for the smoothing and prediction phase were 5.3 (1.5) and 10.5 (3.9) years. The mean RMSE values for the PER and PLR models were unsmoothed data, 6.09 and 6.55; NN, 3.40 and 3.42; Garway-Heath, 3.47 and 3.48; GHT, 3.57 and 3.74; and correlation of rates, 3.59 and 3.64. Smoothed VF data predicted better than unsmoothed data. Nearest neighbor provided the best predictions; PER also predicted consistently more accurately than PLR. Smoothing algorithms should be used when forecasting VF results with PER or PLR. The application of smoothing algorithms on VF data can improve forecasting in VF points to assist in treatment decisions.
Regression: The Apple Does Not Fall Far From the Tree.
Vetter, Thomas R; Schober, Patrick
2018-05-15
Researchers and clinicians are frequently interested in either: (1) assessing whether there is a relationship or association between 2 or more variables and quantifying this association; or (2) determining whether 1 or more variables can predict another variable. The strength of such an association is mainly described by the correlation. However, regression analysis and regression models can be used not only to identify whether there is a significant relationship or association between variables but also to generate estimations of such a predictive relationship between variables. This basic statistical tutorial discusses the fundamental concepts and techniques related to the most common types of regression analysis and modeling, including simple linear regression, multiple regression, logistic regression, ordinal regression, and Poisson regression, as well as the common yet often underrecognized phenomenon of regression toward the mean. The various types of regression analysis are powerful statistical techniques, which when appropriately applied, can allow for the valid interpretation of complex, multifactorial data. Regression analysis and models can assess whether there is a relationship or association between 2 or more observed variables and estimate the strength of this association, as well as determine whether 1 or more variables can predict another variable. Regression is thus being applied more commonly in anesthesia, perioperative, critical care, and pain research. However, it is crucial to note that regression can identify plausible risk factors; it does not prove causation (a definitive cause and effect relationship). The results of a regression analysis instead identify independent (predictor) variable(s) associated with the dependent (outcome) variable. As with other statistical methods, applying regression requires that certain assumptions be met, which can be tested with specific diagnostics.
Molenaar, Dylan; Dolan, Conor V.; de Boeck, Paul
2012-01-01
The Graded Response Model (GRM; Samejima, "Estimation of ability using a response pattern of graded scores," Psychometric Monograph No. 17, Richmond, VA: The Psychometric Society, 1969) can be derived by assuming a linear regression of a continuous variable, Z, on the trait, [theta], to underlie the ordinal item scores (Takane & de Leeuw in…
Few crystal balls are crystal clear : eyeballing regression
International Nuclear Information System (INIS)
Wittebrood, R.T.
1998-01-01
The theory of regression and statistical analysis as it applies to reservoir analysis was discussed. It was argued that regression lines are not always the final truth. It was suggested that regression lines and eyeballed lines are often equally accurate. The many conditions that must be fulfilled to calculate a proper regression were discussed. Mentioned among these conditions were the distribution of the data, hidden variables, knowledge of how the data was obtained, the need for causal correlation of the variables, and knowledge of the manner in which the regression results are going to be used. 1 tab., 13 figs
The Relationship of Grade Span in 9th Grade to Math Achievement in High School
West, John; Miller, Mary Lou; Myers, Jim; Norton, Timothy
2015-01-01
Purpose, Scope, and Method of Study: The purpose of this study was to determine if a correlation exists between grade span for ninth grade and gains in math achievement test scores in 10th grade and 12th grade. A quantitative, longitudinal, correlational research design was employed to investigate the research questions. The population was high…
International Nuclear Information System (INIS)
Burnet, Neil G.; Lynch, Andrew G.; Jefferies, Sarah J.; Price, Stephen J.; Jones, Phil H.; Antoun, Nagui M.; Xuereb, John H.; Pohl, Ute
2007-01-01
Introduction: There is ambiguity in pathological grading of high grade gliomas within the WHO 2000 classification, especially those with predominant oligodendroglial differentiation. Patients and methods: All adult high grade gliomas treated radically, 1996-2005, were assessed. Cases in which pathology was grade III but radiology suggested glioblastoma (GBM) were classified as 'grade III/IV'; their pathology was reviewed. Results: Data from 245 patients (52 grade III, 18 grade III/IV, 175 GBM) were analysed using a Cox Proportional Hazards model. On pathology review, features suggestive of more aggressive behaviour were found in all 18 grade III/IV tumours. Oligodendroglial components with both necrosis and microvascular proliferation were present in 7. MIB-1 counts for the last 8 were all above 14%, mean 27%. Median survivals were: grade III 34 months, grade III/IV 10 months, GBM 11 months. Survival was not significantly different between grade III/IV and GBM. Patients with grade III/IV tumours had significantly worse outcome than grade III, with a hazard of death 3.7 times higher. Conclusions: The results highlight the current inconsistency in pathological grading of high grade tumours, especially those with oligodendroglial elements. Patients with histological grade III tumours but radiological appearances suggestive of GBM should be managed as glioblastoma
Association of Grade Configuration with School Climate for 7th and 8th Grade Students
Malone, Marisa; Cornell, Dewey; Shukla, Kathan
2017-01-01
Educational authorities have questioned whether middle schools provide the best school climate for 7th and 8th grade students, and proposed that other grade configurations such as K-8th grade schools may provide a better learning environment. The purpose of this study was to compare 7th and 8th grade students' perceptions of 4 key features of…
Comparing Dropout Predictors for Two State-Level Panels Using Grade 6 and Grade 8 Data
Franklin, Bobby J.; Trouard, Stephen B.
2016-01-01
The purpose of this study was to examine the effectiveness of dropout predictors across time. Two state-level high school graduation panels were selected to begin with the seventh and ninth grades but end at the same time. The first panel (seventh grade) contained 29,554 students and used sixth grade predictors. The second panel (ninth grade)…
Grade Inflation Marches On: Grade Increases from the 1990s to 2000s
Kostal, Jack W.; Kuncel, Nathan R.; Sackett, Paul R.
2016-01-01
Grade inflation threatens the integrity of college grades as indicators of academic achievement. In this study, we contribute to the literature on grade inflation by providing the first estimate of the size of grade increases at the student level between the mid-1990s and mid-2000s. By controlling for student characteristics and course-taking…
The effect of various grading scales on student grade point averages.
Barnes, Kelli D; Buring, Shauna M
2012-04-10
To investigate changes in and the impact of grading scales from 2005 to 2010 and explore pharmacy faculty and student perceptions of whole-letter and plus/minus grading scales on cumulative grade point averages (GPAs) in required courses. Grading scales used in 2010 at the University of Cincinnati College of Pharmacy were retrospectively identified and compared to those used in 2005. Mean GPA was calculated using a whole-letter grading scale and a plus/minus grading scale to determine the impact of scales on GPA. Faculty members and students were surveyed regarding their perceptions of plus/minus grading. Nine unique grading scales were used throughout the curriculum, including plus/minus (64%) and whole-letter (21%) grading scales. From 2005 to 2010 there was transition from use of predominantly whole-letter scales to plus/minus grading scales. The type of grading scale used did not affect the mean cumulative GPA. Students preferred use of a plus-only grading scale while faculty members preferred use of a plus/minus grading scale. The transition from whole-letter grading to plus/minus grading in courses from 2005 to 2010 reflects pharmacy faculty members' perception that plus/minus grading allows for better differentiation between students' performances.
Risk of Recurrence in Operated Parasagittal Meningiomas: A Logistic Binary Regression Model.
Escribano Mesa, José Alberto; Alonso Morillejo, Enrique; Parrón Carreño, Tesifón; Huete Allut, Antonio; Narro Donate, José María; Méndez Román, Paddy; Contreras Jiménez, Ascensión; Pedrero García, Francisco; Masegosa González, José
2018-02-01
Parasagittal meningiomas arise from the arachnoid cells of the angle formed between the superior sagittal sinus (SSS) and the brain convexity. In this retrospective study, we focused on factors that predict early recurrence and recurrence times. We reviewed 125 patients with parasagittal meningiomas operated from 1985 to 2014. We studied the following variables: age, sex, location, laterality, histology, surgeons, invasion of the SSS, Simpson removal grade, follow-up time, angiography, embolization, radiotherapy, recurrence and recurrence time, reoperation, neurologic deficit, degree of dependency, and patient status at the end of follow-up. Patients ranged in age from 26 to 81 years (mean 57.86 years; median 60 years). There were 44 men (35.2%) and 81 women (64.8%). There were 57 patients with neurologic deficits (45.2%). The most common presenting symptom was motor deficit. World Health Organization grade I tumors were identified in 104 patients (84.6%), and the majority were the meningothelial type. Recurrence was detected in 34 cases. Time of recurrence was 9 to 336 months (mean: 84.4 months; median: 79.5 months). Male sex was identified as an independent risk for recurrence with relative risk 2.7 (95% confidence interval 1.21-6.15), P = 0.014. Kaplan-Meier curves for recurrence had statistically significant differences depending on sex, age, histologic type, and World Health Organization histologic grade. A binary logistic regression was made with the Hosmer-Lemeshow test with P > 0.05; sex, tumor size, and histologic type were used in this model. Male sex is an independent risk factor for recurrence that, associated with other factors such tumor size and histologic type, explains 74.5% of all cases in a binary regression model. Copyright © 2017 Elsevier Inc. All rights reserved.
International Nuclear Information System (INIS)
Ogura, Ichiro; Kurabayashi, Tohru; Amagasa, Teruo; Iwaki, Hiroshi; Sasaki, Takehito
2001-01-01
The purpose of this study was to evaluate the predictive value of preoperative neck computed tomography (CT) in combination with histologic grading as a prognostic factor for patients with tongue carcinoma. Fifty-five patients with squamous cell carcinoma of the tongue were examined by CT prior to radical neck dissection. The locoregional failure and survival rates of these patients were analyzed in relation to their clinical characteristics, histologic grading (World Health Organization, WHO) based on tongue biopsy, and imaging diagnoses prior to surgery. Logistic multivariate regression analysis showed that both histologic grading and number of metastatic lymph nodes on CT were significant and independent prognostic factors in locoregional failure (p=0.009 and p=0.009, respectively). When the numebr of metastatic lymph nodes detected on preoperative neck CT were combined with the histologic grading for the evaluation, the five-year overall survival rates of A group (0 node with any Grade, or 1 node with Grade I-II) and B group (1 node with Grade III, or 2 or more nodes with any Grade) were 74.5% and 37.5%, respectively (p=0.001). The difference was more significant than histologic grading alone or the number of metastatic lymph nodes seen on CT alone. The combination of preoperative neck CT with histologic grading of the primary tumor is useful as a prognostic indicator for patients with tongue carcinoma. (author)
Hassinger-Das, Brenna; Jordan, Nancy C.; Glutting, Joseph; Irwin, Casey; Dyson, Nancy
2013-01-01
Domain general skills that mediate the relation between kindergarten number sense and first-grade mathematics skills were investigated. Participants were 107 children who displayed low number sense in the fall of kindergarten. Controlling for background variables, multiple regression analyses showed that attention problems and executive functioning both were unique predictors of mathematics outcomes. Attention problems were more important for predicting first-grade calculation performance while executive functioning was more important for predicting first-grade performance on applied problems. Moreover, both executive functioning and attention problems were unique partial mediators of the relationship between kindergarten and first-grade mathematics skills. The results provide empirical support for developing interventions that target executive functioning and attention problems in addition to instruction in number skills for kindergartners with initial low number sense. PMID:24237789
Sparse reduced-rank regression with covariance estimation
Chen, Lisha
2014-12-08
Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.
Sparse reduced-rank regression with covariance estimation
Chen, Lisha; Huang, Jianhua Z.
2014-01-01
Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.
Meaney, Christopher; Moineddin, Rahim
2014-01-24
In biomedical research, response variables are often encountered which have bounded support on the open unit interval--(0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their respective probability models. The randomly simulated draws from the various probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided. If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar results were observed if the
Jaber, Mohammed; Wölfer, Johannes; Ewelt, Christian; Holling, Markus; Hasselblatt, Martin; Niederstadt, Thomas; Zoubi, Tarek; Weckesser, Matthias; Stummer, Walter
2016-03-01
Approximately 20% of grade II and most grade III gliomas fluoresce after 5-aminolevulinic acid (5-ALA) application. Conversely, approximately 30% of nonenhancing gliomas are actually high grade. The aim of this study was to identify preoperative factors (ie, age, enhancement, 18F-fluoroethyl tyrosine positron emission tomography [F-FET PET] uptake ratios) for predicting fluorescence in gliomas without typical glioblastomas imaging features and to determine whether fluorescence will allow prediction of tumor grade or molecular characteristics. Patients harboring gliomas without typical glioblastoma imaging features were given 5-ALA. Fluorescence was recorded intraoperatively, and biopsy specimens collected from fluorescing tissue. World Health Organization (WHO) grade, Ki-67/MIB-1 index, IDH1 (R132H) mutation status, O-methylguanine DNA methyltransferase (MGMT) promoter methylation status, and 1p/19q co-deletion status were assessed. Predictive factors for fluorescence were derived from preoperative magnetic resonance imaging and F-FET PET. Classification and regression tree analysis and receiver-operating-characteristic curves were generated for defining predictors. Of 166 tumors, 82 were diagnosed as WHO grade II, 76 as grade III, and 8 as glioblastomas grade IV. Contrast enhancement, tumor volume, and F-FET PET uptake ratio >1.85 predicted fluorescence. Fluorescence correlated with WHO grade (P fluorescing grade III gliomas was higher than in nonfluorescing tumors, whereas in fluorescing and nonfluorescing grade II tumors, no differences were noted. Age, tumor volume, and F-FET PET uptake are factors predicting 5-ALA-induced fluorescence in gliomas without typical glioblastoma imaging features. Fluorescence was associated with an increased Ki-67/MIB-1 index and high-grade pathology. Whether fluorescence in grade II gliomas identifies a subtype with worse prognosis remains to be determined.
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. Copyright
Lo, Benjamin W Y; Fukuda, Hitoshi; Angle, Mark; Teitelbaum, Jeanne; Macdonald, R Loch; Farrokhyar, Forough; Thabane, Lehana; Levine, Mitchell A H
2016-01-01
Classification and regression tree analysis involves the creation of a decision tree by recursive partitioning of a dataset into more homogeneous subgroups. Thus far, there is scarce literature on using this technique to create clinical prediction tools for aneurysmal subarachnoid hemorrhage (SAH). The classification and regression tree analysis technique was applied to the multicenter Tirilazad database (3551 patients) in order to create the decision-making algorithm. In order to elucidate prognostic subgroups in aneurysmal SAH, neurologic, systemic, and demographic factors were taken into account. The dependent variable used for analysis was the dichotomized Glasgow Outcome Score at 3 months. Classification and regression tree analysis revealed seven prognostic subgroups. Neurological grade, occurrence of post-admission stroke, occurrence of post-admission fever, and age represented the explanatory nodes of this decision tree. Split sample validation revealed classification accuracy of 79% for the training dataset and 77% for the testing dataset. In addition, the occurrence of fever at 1-week post-aneurysmal SAH is associated with increased odds of post-admission stroke (odds ratio: 1.83, 95% confidence interval: 1.56-2.45, P tree was generated, which serves as a prediction tool to guide bedside prognostication and clinical treatment decision making. This prognostic decision-making algorithm also shed light on the complex interactions between a number of risk factors in determining outcome after aneurysmal SAH.
Is the Sky Falling? Grade Inflation and the Signaling Power of Grades.
Pattison, Evangeleen; Grodsky, Eric; Muller, Chandra
2013-06-01
Grades are the fundamental currency of our educational system; they signal academic achievement and non-cognitive skills to parents, employers, postsecondary gatekeepers, and students themselves. Grade inflation compromises the signaling value of grades, undermining their capacity to achieve the functions for which they are intended. We challenge the 'increases in grade point average' definition of grade inflation and argue that grade inflation must be understood in terms of the signaling power of grades. Analyzing data from four nationally representative samples, we find that in the decades following 1972: (a) grades have risen at high schools and dropped at four-year colleges, in general, and selective four-year institutions, in particular; and (b) the signaling power of grades has attenuated little, if at all.
Robust Regression and its Application in Financial Data Analysis
Mansoor Momeni; Mahmoud Dehghan Nayeri; Ali Faal Ghayoumi; Hoda Ghorbani
2010-01-01
This research is aimed to describe the application of robust regression and its advantages over the least square regression method in analyzing financial data. To do this, relationship between earning per share, book value of equity per share and share price as price model and earning per share, annual change of earning per share and return of stock as return model is discussed using both robust and least square regressions, and finally the outcomes are compared. Comparing the results from th...
Local bilinear multiple-output quantile/depth regression
Czech Academy of Sciences Publication Activity Database
Hallin, M.; Lu, Z.; Paindaveine, D.; Šiman, Miroslav
2015-01-01
Roč. 21, č. 3 (2015), s. 1435-1466 ISSN 1350-7265 R&D Projects: GA MŠk(CZ) 1M06047 Institutional support: RVO:67985556 Keywords : conditional depth * growth chart * halfspace depth * local bilinear regression * multivariate quantile * quantile regression * regression depth Subject RIV: BA - General Mathematics Impact factor: 1.372, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/siman-0446857.pdf
Mixture of Regression Models with Single-Index
Xiang, Sijia; Yao, Weixin
2016-01-01
In this article, we propose a class of semiparametric mixture regression models with single-index. We argue that many recently proposed semiparametric/nonparametric mixture regression models can be considered special cases of the proposed model. However, unlike existing semiparametric mixture regression models, the new pro- posed model can easily incorporate multivariate predictors into the nonparametric components. Backfitting estimates and the corresponding algorithms have been proposed for...
Alternative regression models to assess increase in childhood BMI
Beyerlein, Andreas; Fahrmeir, Ludwig; Mansmann, Ulrich; Toschke, André M
2008-01-01
Abstract Background Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 childre...
Preface to Berk's "Regression Analysis: A Constructive Critique"
de Leeuw, Jan
2003-01-01
It is pleasure to write a preface for the book ”Regression Analysis” of my fellow series editor Dick Berk. And it is a pleasure in particular because the book is about regression analysis, the most popular and the most fundamental technique in applied statistics. And because it is critical of the way regression analysis is used in the sciences, in particular in the social and behavioral sciences. Although the book can be read as an introduction to regression analysis, it can also be read as a...
A logistic regression estimating function for spatial Gibbs point processes
DEFF Research Database (Denmark)
Baddeley, Adrian; Coeurjolly, Jean-François; Rubak, Ege
We propose a computationally efficient logistic regression estimating function for spatial Gibbs point processes. The sample points for the logistic regression consist of the observed point pattern together with a random pattern of dummy points. The estimating function is closely related to the p......We propose a computationally efficient logistic regression estimating function for spatial Gibbs point processes. The sample points for the logistic regression consist of the observed point pattern together with a random pattern of dummy points. The estimating function is closely related...
Spontaneous regression of metastases from malignant melanoma: a case report
DEFF Research Database (Denmark)
Kalialis, Louise V; Drzewiecki, Krzysztof T; Mohammadi, Mahin
2008-01-01
A case of a 61-year-old male with widespread metastatic melanoma is presented 5 years after complete spontaneous cure. Spontaneous regression occurred in cutaneous, pulmonary, hepatic and cerebral metastases. A review of the literature reveals seven cases of regression of cerebral metastases......; this report is the first to document complete spontaneous regression of cerebral metastases from malignant melanoma by means of computed tomography scans. Spontaneous regression is defined as the partial or complete disappearance of a malignant tumour in the absence of all treatment or in the presence...
Acupuncture and Spontaneous Regression of a Radiculopathic Cervical Herniated Disc
Directory of Open Access Journals (Sweden)
Kim Sung-Ha
2012-06-01
Full Text Available The spontaneous regression of herniated cervical discs is not a well-established phenomenon. However, we encountered a case of a spontaneous regression of a severe radiculopathic herniated cervical disc that was treated with acupuncture, pharmacopuncture, and herb medicine. The symptoms were improved within 12 months of treatment. Magnetic resonance imaging (MRI conducted at that time revealed marked regression of the herniated disc. This case provides an additional example of spontaneous regression of a herniated cervical disc documented by MRI following non-surgical treatment.
Production of Food Grade Yeasts
Directory of Open Access Journals (Sweden)
Argyro Bekatorou
2006-01-01
Full Text Available Yeasts have been known to humans for thousands of years as they have been used in traditional fermentation processes like wine, beer and bread making. Today, yeasts are also used as alternative sources of high nutritional value proteins, enzymes and vitamins, and have numerous applications in the health food industry as food additives, conditioners and flavouring agents, for the production of microbiology media and extracts, as well as livestock feeds. Modern scientific advances allow the isolation, construction and industrial production of new yeast strains to satisfy the specific demands of the food industry. Types of commercial food grade yeasts, industrial production processes and raw materials are highlighted. Aspects of yeast metabolism, with respect to carbohydrate utilization, nutritional aspects and recent research advances are also discussed.
International Nuclear Information System (INIS)
Su Zhongzhen; Shan Hong; He Bingjun; Lv Wentian; Meng Xiaochun; Wang Jin; Zhu Kangshun; Yang Yang; Chen Guihua
2009-01-01
Purpose: To select the most powerful predictors for the evaluation of hepatic steatosis grade. Methods and materials: Forty-five healthy New Zealand rabbits were randomly divided into one normal control group and three experimental groups. Hepatic steatosis models were established by feeding a high-fat, high-sugar diet and drinking water containing 5% ethanol. Twenty-two variable indexes were measured using general observation, biochemical examination, ultrasonography, computed tomography (CT), and proton magnetic resonance spectroscopy (MRS). Univariate analysis, correlation analysis, and stepwise regression analysis were used to make the selection of the most powerful predictors. ROC analysis was used to compare the diagnostic efficacy of single index with combined index (Y) expressed by a regression equation. Results: Based on statistical analysis, there were 12 variable indexes with significant differences among groups, which correlated with hepatic steatosis grade: liver weight, hepatic index, liver CT value, liver-to-muscle attenuation ratio, 1 H MRS fat peak value, fat peak area, fat-to-water peak area ratio, fat percentage, ultrasound attenuation coefficient, serum aspartate aminotransferase, total cholesterol (TC) and triglycerides. Among them hepatic index, liver CT value and serum TC were selected as the most powerful predictors for hepatic steatosis grade with correlation coefficients of 0.709, -0.764, and 0.886, respectively. The regression equation was: Y = 1.975 + 3.906 x 10 -2 X 1 + 0.369X 2 - 2.84 x 10 -2 X 3 , where Y = hepatic steatosis grade, X 1 = TC, X 2 = hepatic index, and X 3 = liver CT value. ROC analysis displayed PPV, NPV, curve area of combined index (Y) were superior to simple index (hepatic index, liver CT value and serum TC) in evaluating hepatic steatosis grade, and they were nearly 1.0000, 1.0000 and 1.000, respectively. Conclusions: Combined application of several diagnostic methods is superior to simple diagnostic method, and
Processing of low-grade uranium ores
International Nuclear Information System (INIS)
Michel, P.
1975-01-01
Four types of low grade ores are studied. Low grade ores which must be extracted because they are enclosed in a normal grade deposit. Heap leaching is the processing method which is largely used. It allows to obtain solutions or preconcentrates which may be delivered at the nearest plant. Normal grade ores contained in a low amplitude deposit which can be processed using leaching as far as the operation does not need any large expensive equipment. Medium grade ores in medium amplitude deposits to which a simplified conventional process can be applied using fast heap leaching. Low grade ores in large deposits. The processing possibilities leading to use in place leaching are explained. The operating conditions of the method are studied (leaching agent, preparation of the ore deposit to obtain a good tightness with regard to the hydrological system and to have a good contact between ore and reagent) [fr
Processing of low grade uranium ores
International Nuclear Information System (INIS)
Michel, P.
1978-10-01
Four types of low-grade ores are studied: (1) Low-grade ores that must be extracted because they are enclosed in a normal-grade deposit. Heap leaching is the processing method which is largely used. (2) Normal-grade ores contained in low-amplitude deposits. They can be processed using in-place leaching as far as the operation does not need any large and expensive equipment. (3) Medium-grade ores in medium-amplitude deposits. A simplified conventional process can be applied using fast heap leaching. (4) Low-grade ores in large deposits. The report explains processing possibilities leading in most cases to the use of in-place leaching. The operating conditions of this method are laid out, especially the selection of the leaching agents and the preparation of the ore deposit
McDuff, Susan G R; McDuff, DeForest; Farace, Jennifer A; Kelly, Carolyn J; Savoia, Maria C; Mandel, Jess
2014-06-30
To assess the impact of a change in preclerkship grading system from Honors/Pass/Fail (H/P/F) to Pass/Fail (P/F) on University of California, San Diego (UCSD) medical students' academic performance. Academic performance of students in the classes of 2011 and 2012 (constant-grading classes) were collected and compared with performance of students in the class of 2013 (grading-change class) because the grading policy at UCSD SOM was changed for the class of 2013, from H/P/F during the first year (MS1) to P/F during the second year (MS2). For all students, data consisted of test scores from required preclinical courses from MS1 and MS2 years, and USMLE Step 1 scores. Linear regression analysis controlled for other factors that could be predictive of student performance (i.e., MCAT scores, undergraduate GPA, age, gender, etc.) in order to isolate the effect of the changed grading policy on academic performance. The change in grading policy in the MS2 year only, without any corresponding changes to the medical curriculum, presents a unique natural experiment with which to cleanly evaluate the effect of P/F grading on performance outcomes. After controlling for other factors, the grading policy change to P/F grading in the MS2 year had a negative impact on second-year grades relative to first-year grades (the constant-grading classes performed 1.65% points lower during their MS2 year compared to the MS1 year versus 3.25% points lower for the grading-change class, p < 0.0001), but had no observable impact on USMLE Step 1 scores. A change in grading from H/P/F grading to P/F grading was associated with decreased performance on preclinical examinations but no decrease in performance on the USMLE Step 1 examination. These results are discussed in the broader context of the multitude of factors that should be considered in assessing the merits of various grading systems, and ultimately the authors recommend the continuation of pass-fail grading at UCSD School of Medicine.
A New Grading System for the Management of Antenatal Hydronephrosis.
Dos Santos, Joana; Parekh, Rulan S; Piscione, Tino D; Hassouna, Tarek; Figueroa, Victor; Gonima, Paula; Vargas, Isis; Farhat, Walid; Rosenblum, Norman D
2015-10-07
Standard clinical assessments do not predict surgical intervention in patients with a moderate degree of upper tract hydronephrosis. This study investigated whether combined measures of renal calyceal dilation and anteroposterior diameter (APD) of the renal pelvis at the first postnatal ultrasound better predict surgical intervention beyond standard assessments of the APD or Society of Fetal Urology (SFU) grading system. A retrospective cohort of 348 children with antenatal hydronephrosis followed from 2003 to 2013 were studied. Using Cox regression, the risk for surgery by APD, SFU, and combined grading on the basis of the first postnatal ultrasound was calculated. The predictive capability of each grading system for surgery was determined by calculating the positive likelihood ratio (LR+). The combination of APD≥6-9 mm and diffuse caliectasis had a hazard ratio (HR) of 19.5 (95% confidence interval [95% CI], 3.94 to 96.9) versus 0.59 (95% CI, 0.05 to 6.53) for APD≥6-9 mm alone and a similar risk of 8.9 for SFU grade 3 (95% CI, 3.84 to 20.9). The combination of APD≥9-15 mm and diffuse caliectasis had an HR of 18.7 (95% CI, 4.36 to 80.4) versus 1.75 (95% CI, 0.29 to 10.5) for APD≥9-15 mm alone. The LR+ for surgery for diffuse caliectasis and APD≥6-9 mm was higher than for APD≥6-9 mm alone (HR=2.62; 95% CI, 0.87 to 7.94 versus HR=0.04; 95% CI, 0.01 to 0.32) and was higher for APD≥9-15 mm and diffuse caliectasis than APD≥9-15 mm alone (HR=2.0; 95% CI, 1.15 to 3.45 versus HR=0.14; 95% CI, 0.04 to 0.43). Both combined groups of moderate hydronephrosis (APD≥6-9 mm or ≥9-15 mm with diffuse caliectasis) had only slightly higher LR+ than SFU grade 3 (HR=1.89; 95% CI, 1.17 to 3.05). These results suggest a grading system combining APD and diffuse caliectasis distinguishes those children with moderate degrees of upper tract hydronephrosis that are at higher risk of surgery. Copyright © 2015 by the American Society of Nephrology.
Significance testing in ridge regression for genetic data
Directory of Open Access Journals (Sweden)
De Iorio Maria
2011-09-01
Full Text Available Abstract Background Technological developments have increased the feasibility of large scale genetic association studies. Densely typed genetic markers are obtained using SNP arrays, next-generation sequencing technologies and imputation. However, SNPs typed using these methods can be highly correlated due to linkage disequilibrium among them, and standard multiple regression techniques fail with these data sets due to their high dimensionality and correlation structure. There has been increasing interest in using penalised regression in the analysis of high dimensional data. Ridge regression is one such penalised regression technique which does not perform variable selection, instead estimating a regression coefficient for each predictor variable. It is therefore desirable to obtain an estimate of the significance of each ridge regression coefficient. Results We develop and evaluate a test of significance for ridge regression coefficients. Using simulation studies, we demonstrate that the performance of the test is comparable to that of a permutation test, with the advantage of a much-reduced computational cost. We introduce the p-value trace, a plot of the negative logarithm of the p-values of ridge regression coefficients with increasing shrinkage parameter, which enables the visualisation of the change in p-value of the regression coefficients with increasing penalisation. We apply the proposed method to a lung cancer case-control data set from EPIC, the European Prospective Investigation into Cancer and Nutrition. Conclusions The proposed test is a useful alternative to a permutation test for the estimation of the significance of ridge regression coefficients, at a much-reduced computational cost. The p-value trace is an informative graphical tool for evaluating the results of a test of significance of ridge regression coefficients as the shrinkage parameter increases, and the proposed test makes its production computationally feasible.
Regression calibration with more surrogates than mismeasured variables
Kipnis, Victor
2012-06-29
In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.
Regression calibration with more surrogates than mismeasured variables
Kipnis, Victor; Midthune, Douglas; Freedman, Laurence S.; Carroll, Raymond J.
2012-01-01
In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.