We present an efficient algorithm for simultaneously training elastic-net-regularized generalized linear models across many related problems, which may arise from bootstrapping, cross-validation and nonparametric permutation testing. Our approach leverages the redundancies across problems to obtain ≈ 10x computational improvements relative to solving the problems sequentially by the standard gl...

This note generalizes Chao's estimator of population size for closed capture-recapture studies if covariates are available. Chao's estimator was developed under unobserved heterogeneity in which case it represents a lower bound of the population size. If observed heterogeneity is available in form of covariates we show how this information can be used to reduce the bias of Chao's estimator. The...

the methods applied to regularization of the ill-posed problems can be classified under “direct” and “indirect” methods. practice has shown that the effects of different regularization techniques on an ill-posed problem are not the same, and as such each ill-posed problem requires its own investigation in order to identify its most suitable regularization method. in the geoid computations witho...

Capture-recapture analysis has been used to evaluate infectious disease surveillance. Violation of the underlying assumptions can jeopardize the validity of the capture-recapture estimates and a tool is needed for cross-validation. We re-examined 19 datasets of log-linear model capture-recapture studies on infectious disease incidence using three truncated models for incomplete count data as al...

Two category Support Vector Machines (SVM) have been very popular in the machine learning community for the classification problem. Solving multicategory problems by a series of binary classifiers is quite common in the SVM paradigm. However, this approach may fail under a variety of circumstances. We have proposed the Multicategory Support Vector Machine (MSVM), which extends the binary SVM to...

Generalized linear mixed-effect models are widely used for the analysis of correlated nonGaussian data such as those found in longitudinal studies. In this article, we consider extensions with nonparametric fixed effects and parametric random effects. The estimation is through the penalized likelihood method, and our focus is on the efficient computation and the effective smoothing parameter se...

This paper tackles the problem of selecting among several linear estimators in nonparametric regression; this includes model selection for linear regression, the choice of a regularization parameter in kernel ridge regression or spline smoothing, and the choice of a kernel in multiple kernel learning. We propose a new algorithm which first estimates consistently the variance of the noise, based...