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binary regression: Total gain in positive and negative predictive values

BIOMETRICAL JOURNAL 2012年第6期54卷 808-823页

作者： Klotsche, Jens Ferger, Dietmar Leistner, David Pieper, Lars Zeiher, Andreas M. Wittchen, Hans-Ulrich Rehm, Juergen Tech Univ Dresden Inst Clin Psychol & Psychotherapy D-01187 Dresden Germany Tech Univ Dresden Ctr Clin Epidemiol & Longitudinal Studies CELOS D-01187 Dresden Germany Tech Univ Dresden Dept Math D-01069 Dresden Germany Goethe Univ Frankfurt Dept Med 3 D-60596 Frankfurt Germany Ctr Addict & Mental Hlth Publ Hlth & Regulatory Policy Sect Toronto ON M5S 2S1 Canada

Models that predict disease incidence or disease recurrence are attractive for clinicians as well as for patients. The usefulness of a risk prediction model is linked to the two questions whether the observed outcome is confirmed by the prediction and whether the risk prediction is accurate in predicting the future outcome, respectively. The first phrasing of the question is linked to considering sensitivity and specificity and the latter to the positive and negative predictive values. We present the measures of standardized total gain in positive and negative predictive values dealing with the performance or accuracy of the prediction model for a binary outcome. Both measures provide a useful tool for assessing the performance or accuracy of a set of predictor variables for the prediction of a binary outcome. This concept is a tool for evaluating the optimal prediction model in future research.

关键词： binary regression Negative predictive value Positive predictive value Total gain

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binary regression Investigate on Composition Tempering Temperature and Hardness (HRC) of Common Steel

Binary Regression Investigate on Composition Tempering Tempe...

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International Conference on Industry, Information System and Material Engineering

作者： Zou, Qing-hua Wuhan Text Univ Sch Mech & Elect Engn Wuhan 430073 Peoples R China

ISBN: (纸本)9783037850275

Establish the uniform regression relation expression for these three parameters including composition of over ten kinds of common steels, composition tempering temperature and hardness (HRC) of steel, which can accurately calculate the relative values of these three parameters of over ten kinds of common steels within the discussed scope. binary regression analysis can be expanded to any Tempering temperature and each kind of steel grade, and the work can be fulfilled with computer. Furthermore express other thermal treatment process parameters and its corresponding mechanical property of each kind of steel with the uniform mathematic relation, so that related uniform regression relation expression can be used into the actual production process, and this is what the author will complete further.

关键词： binary regression composition tempering hardness (HRC) carbon equivalent

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TRANSFORMATIONS TO LINEARITY IN binary regression

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SIAM JOURNAL ON APPLIED MATHEMATICS 1983年第4期43卷 703-710页

作者： WHITTEMORE, AS Dep. Family Community Preventive Med. Stanford Univ. Stanford CA 94305 USA

A one-parameter family of transformations for binary response probabilities is introduced. The family is used to explore relationships between response and explanatory variables by assuming that the transformed probabilities depend linearly on the known variables and on unknown parameters reflecting the variables’ effect on response. The family includes the logistic and one-hit transformations, which form the bases for two popular notions of “no interaction” between explanatory variables. Maximum likelihood methods are used to determine those transformations in the family that provide acceptable fits to data, and the methods are illustrated with data from an animal cancer bioassay. The paper concludes with a comparison of probabilistic and statistical properties of logistic and one-hit transformations.

关键词： binary regression interaction logistic transformation maximum likelihood one-hit transformation

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Integrating random walk and binary regression to identify novel miRNA-disease association

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BMC BIOINFORMATICS 2019年第1期20卷 1-13页

作者： Niu, Ya-Wei Wang, Guang-Hui Yan, Gui-Ying Chen, Xing Shandong Univ Sch Math Jinan 250100 Shandong Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China China Univ Min & Technol Sch Informat & Control Engn 1 Daxue Rd Xuzhou 221116 Jiangsu Peoples R China

BackgroundIn the last few decades, cumulative experimental researches have witnessed and verified the important roles of microRNAs (miRNAs) in the development of human complex diseases. Benefitting from the rapid growth both in the availability of miRNA-related data and the development of various analysis methodologies, up until recently, some computational models have been developed to predict human disease related miRNAs, efficiently and *** this work, we proposed a computational model of Random Walk and binary regression-based MiRNA-Disease Association prediction (RWBRMDA). RWBRMDA extracted features for each miRNA from random walk with restart on the integrated miRNA similarity network for binary logistic regression to predict potential miRNA-disease associations. RWBRMDA obtained AUC of 0.8076 in the leave-one-out cross validation. Additionally, we carried out three different patterns of case studies on four human complex diseases. Specifically, Esophageal cancer and Prostate cancer were conducted as one kind of case study based on known miRNA-disease associations in HMDD v2.0 database. Out of the top 50 predicted miRNAs, 94 and 90% were respectively confirmed by recent experimental reports. To simulate new disease without known related miRNAs, the information of known Breast cancer related miRNAs was removed. As a result, 98% of the top 50 predicted miRNAs for Breast cancer were confirmed. Lymphoma, the verified ratio of which was 88%, was used to assess the prediction robustness of RWBRMDA based on the association records in HMDD v1.0 *** anticipated that RWBRMDA could benefit the future experimental investigations about the relation between human disease and miRNAs by generating promising and testable top-ranked miRNAs, and significantly reducing the effort and cost of identification works.

关键词： microRNA Disease miRNA-disease association Random walk binary regression

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Assessing the predictive ability of a multilevel binary regression model

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2012年第6期56卷 1966-1980页

作者： Van Oirbeek, R. Lesaffre, E. Katholieke Univ Leuven Interuniv Inst Biostat & Stat Bioinformat B-3000 Louvain Belgium Univ Hasselt Hasselt Belgium Erasmus MC Dept Biostat Rotterdam Netherlands

An adaptation of the Brier score and the concordance probability is proposed for the two-level and the three-level random intercept binary regression model. This results in 2 different Brier scores and 3 different C-indices for the two-level binary regression model and 4 different Brier scores and 7 different C-indices for the three-level binary regression model. The ensemble of these measures offers a better view on how the different elements of the random effects model, i.e. the covariates and the random effects, affect the predictive ability of the model separately, evaluated on a within-cluster, between-cluster and global level. For all measures, an estimation procedure using Bayesian and likelihood estimation methods was developed, including a percentile and a BCa non-parametric bootstrap step to construct credible/confidence intervals. In a simulation study, the likelihood estimation procedure showed difficulties in estimating unbiasedly the predictive ability of the random effects, while the Bayesian estimation procedure resulted in good estimation properties for all of the developed measures. The BCa non-parametric bootstrap method resulted in confidence/credible intervals with better coverage properties than the percentile non-parametric bootstrap method. The proposals are applied to a real-life binary data set with a three-level structure using the Bayesian estimation procedure. (c) 2011 Elsevier B.V. All rights reserved.

关键词： Brier score C-index Concordance probability Multilevel binary regression

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Local influence in binary regression models, and its correspondence with global influence

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2000年第2期29卷 349-368页

作者： Farrell, PJ Cadigan, NG Acadia Univ Dept Math & Stat Wolfville NS B0P 1X0 Canada Fisheries & Ocean St John NF A1C 5X1 Canada

We develop local influence diagnostics for a general binary regression model, and apply these methods to case-weight perturbations in four examples. In addition, we illustrate the correspondence between case-deletion diagnostics and local case-weight perturbation slopes and curvatures. We demonstrate that local influence diagnostics can provide a more computationally efficient means for obtaining analogous information to that yielded by case-deletion diagnostics, which can be thought of as global influence perturbations. We also assess the global consistency of patterns of local influence using these data examples.

关键词： influence diagnostics binary regression likelihood displacement deviance displacement

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BIOMETRIKA 1990年第3期77卷 642-648页

作者： ZHAO, LP PRENTICE, RL Division of Public Health Sciences Fred Hutchinson Cancer Research Center SeattleWashington 98104 U.S.A.

A model of quadratic exponential form is parameterized in terms of marginal means and pairwise correlations for the regression analysis of correlated binary data. Pseudo-maximum likelihood methods using a special case termed the multiplicative model are proposed, but are noted to be computationally unattractive if the ''blocks'' of correlated responses are at all large. On the other hand score estimating functions for mean and correlation parameters are shown to be of a particularly simple form under the quadratic exponential family. Special cases of such estimating functions having attractive computational properties are identified and illustrated.

关键词： binary regression Correlated binary data Estimating function Multiplicative model Pseudo-maximum likelihood Quadratic exponential model

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Optimal adaptive inference in random design binary regression

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BERNOULLI 2018年第1期24卷 699-739页

作者： Mukherjee, Rajarshi Sen, Subhabrata Stanford Univ Dept Stat Sequoia Hall390 Serra Mall Stanford CA 94305 USA

We construct confidence sets for the regression function in nonparametric binary regression with an unknown design density a nuisance parameter in the problem. These confidence sets are adaptive in L-2 loss over a continuous class of Sobolev type spaces. Adaptation holds in the smoothness of the regression function, over the maximal parameter spaces where adaptation is possible, provided the design density is smooth enough. We identify two key regimes one where adaptation is possible, and one where some critical regions must be removed. We address related questions about goodness of fit testing and adaptive estimation of relevant infinite dimensional parameters.

关键词： adaptive confidence sets binary regression U-statistics

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Regularized robust estimation in binary regression models

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JOURNAL OF APPLIED STATISTICS 2022年第3期49卷 574-598页

作者： Tang, Qingguo Karunamuni, Rohana J. Liu, Boxiao Nanjing Univ Sci & Technol Sch Econ & Management Nanjing Peoples R China Univ Alberta Dept Math & Stat Sci Edmonton AB T6G 2G1 Canada Bank Montreal Toronto ON Canada

In this paper, we investigate robust parameter estimation and variable selection for binary regression models withgrouped data. We investigate estimation procedures based on the minimum-distance approach. In particular, we employ minimum Hellinger and minimum symmetric chi-squared distances criteria and propose regularized minimum-distance estimators. These estimators appear to possess a certain degree of automatic robustness against model misspecification and/or for potential outliers. We show that the proposed non-penalized and penalized minimum-distance estimators are efficient under the model and simultaneously have excellent robustness properties. We study their asymptotic properties such as consistency, asymptotic normality and oracle properties. Using Monte Carlo studies, we examine the small-sample and robustness properties of the proposed estimators and compare them with traditional likelihood estimators. We also study two real-data applications to illustrate our methods. The numerical studies indicate the satisfactory finite-sample performance of our procedures.

关键词： binary regression maximum likelihood minimum-distance methods variable selection efficiency robustness

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Least squares moment identification of binary regression mixture models

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METRIKA 2021年第4期84卷 561-593页

作者： Auder, Benjamin Gassiat, Elisabeth Loum, Mor Absa Univ Paris Saclay Lab Math Orsay CNRS F-91405 Orsay France

We consider finite mixtures of generalized linear models with binary output. We prove that cross moments (between the output and the regression variables) up to order three are sufficient to identify all parameters of the model. We propose a least-squares estimation method based on those moments and we prove the consistency and the Gaussian asymptotic behavior of the estimator. We provide simulation results and comparisons with likelihood methods. Numerical experiments were conducted using the R-package morpheus that we developed for our least-squares moment method and with the R-package flexmix for likelihood methods. We then give some possible extensions to finite mixtures of regressions with binary output including both continuous and categorical covariates, and possibly longitudinal data.

关键词： Generalized linear model Mixture model Moment method Spectral method binary regression

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