This note develops an R estimator of the regression parameters in errors variables linear model, when distributions vectors covariates and measurement are known. The paper also contains proof asymptotic uniform linearity a sequence simple rank statistics based on residuals class nonlinear parametric models where possibly dependent. result turn facilitates normality above mentioned model. Pitman...

Three (3) different methods (logistic regression, covariate shift and k-NN) were applied to five (5) internal datasets and one (1) external, publically available dataset where covariate shift existed. In all cases, k-NN's performance was inferior to either logistic regression or covariate shift. Surprisingly, there was no obvious advantage for using covariate shift to reweight the training data...

In a regression analysis, suppose we suspect that there are several heterogeneous groups in the population that a sample represents. Mixture regression models have been applied to address such problems. By modeling the conditional distribution of the response given the covariate as a mixture, the sample can be clustered into groups and the individual regression models for the groups can be esti...

We propose a class of kernel stick-breaking processes for uncountable collections of dependent random probability measures. The process is constructed by first introducing an infinite sequence of random locations. Independent random probability measures and beta-distributed random weights are assigned to each location. Predictor-dependent random probability measures are then constructed by mixi...

Clustering, like covariate selection for classification, is an important step to compress and interpret the data. However, clustering of covariates often performed independently classification step, which can lead undesirable results that harm interpretability compression rate. Therefore, we propose a method cluster while taking into account class label information samples. We formulate problem...

Estimation of covariate-dependent conditional covariance matrix in a high-dimensional space poses challenge to contemporary statistical research. The existing kernel estimators may not be locally adaptive due using single bandwidth explore the smoothness all entries target function. Moreover, corresponding theory holds only for i.i.d. samples although most applications, are dependent. In this p...

In an empirical Bayesian setting, we provide a new multiple testing method, useful when an additional covariate is available, that influences the probability of each null hypothesis being true. We measure the posterior significance of each test conditionally on the covariate and the data, leading to greater power. Using covariate-based prior information in an unsupervised fashion, we produce a ...