In many settings, the variable of interest is a proportion with high concentration of data at the boundaries. This paper proposes a regression model for a fractional variable with nontrivial probability masses at the extremes. In particular, the dependent variable is assumed to be a mixed random variable, obtained as the mixture of a Bernoulli and a beta random variables. The extreme values of ...

A random-effects regression model is proposed for analysis of clustered data. Unlike ordinary regression analysis of clustered data, random-effects regression models do not assume that each observation is independent but do assume that data within clusters are dependent to some degree. The degree of this dependency is estimated along with estimates of the usual model parameters, thus adjusting ...

We investigate a method for regression that makes use of a randomly generated subspace GP ⊂ F (of finite dimension P) of a given large (possibly infinite) dimensional function space F , for example, L2([0,1] d ;R). GP is defined as the span of P random features that are linear combinations of a basis functions of F weighted by random Gaussian i.i.d. coefficients. We show practical motivation fo...

This article deals with random projections applied as a data reduction technique for Bayesian regression analysis. We show sufficient conditions under which the entire d-dimensional distribution is approximately preserved under random projections by reducing the number of data points from n to k ∈ O(poly(d/ε)) in the case n d. Under mild assumptions, we prove that evaluating a Gaussian likeliho...

In this work we present a novel approach to ensemble learning for regression models, by combining the ensemble generation technique of random subspace method with the ensemble integration methods of Stacked Regression and Dynamic Selection. We show that for simple regression methods such as global linear regression and nearest neighbours, this is a more effective method than the popular ensembl...

Several statistical models have been proposed for the genetic evaluation of production traits in dairy cattle based on test-day records. Three main approaches have been put forward in the literature: random regression, orthogonal polynomials, and, more recently, character process models. The aim of this paper is to show how these different approaches are related, to compare their performance fo...

Statistical methodology is presented for the analysis of multiple events with random eeects and measurement error. We model multiple events in a general space using a random measure, and deene point process regression models with residual random eeects. Our approach to parameter estimation and signiicance testing is to start with a simple naive model of Poisson process regression, and then to a...

In The Bell Curve, Herrnstein and Murray (1994) claim, based on evidence from crosssectional regressions, that differences in wages in the U.S. labor market are predominantly explained by general intelligence. Cawley, Heckman, and Vytlacil (1999), using evidence from random effects panel regressions, reject this claim, in part because returns to general intelligence vary by racial and gender su...