analysis of variance (ANOVA): one-way nested, hierarchical two-way linear model fixed, random, and mixed models factorial arrangement of treatments variance components sum of squares, mean square, expected mean square sampling fraction, fixed effects, random effects simple effects, main effects, interaction effects, additive effects analysis of covariance, covariate, concomitant variable data t...

This paper conducts a Monte Carlo simulation study to evaluate the performance of multivariate matching methods that select a subset of treatment and control observations. The matching methods studied are the widely used nearest neighbor matching with propensity score calipers and the more recently proposed methods, optimal matching of an optimally chosen subset and optimal cardinality matching...

We propose a class of Bayesian cure rate models by incorporating a baseline density function as well as multiplicative and additive covariate structures. Our model naturally accommodates zero and non-zero cure rates, which provides an objective way to examine the existence of a survival fraction in the failure time data. An inherent parameter constraint needs to be incorporated into the model f...

We evaluate an approach to detect single-nucleotide polymorphisms (SNPs) that account for a linkage signal with covariate-based affected relative pair linkage analysis in a conditional-logistic model framework using all 200 replicates of the Genetic Analysis Workshop 17 family data set. We begin by combining the multiple known covariate values into a single variable, a propensity score. We also...

Recurrent events are frequently encountered in biomedical studies. Evaluating the covariates effects on the marginal recurrent event rate is of practical interest. There are mainly two types of rate models for the recurrent event data: the multiplicative rates model and the additive rates model. We consider a more flexible additive-multiplicative rates model for analysis of recurrent event data...

Generalized additive models (GAMs) have been widely used. While the procedure for fitting a generalized additive model to independent data has been well established, not as much work has been done when the data are correlated. The currently available methods are not completely satisfactory in practice. A new approach is proposed to fit generalized additive models with spatio-temporal data via t...

Additive models are widely used to provide very flexible and effective descriptions of regression data where assumptions of linear relationships are too restrictive. However, these models usually assume independent errors and there are many applications where the data are correlated. This is often true in environmental settings and an application to water quality modelling in the River Clyde is...

We consider the efficient estimation of a regression parameter in a partially linear additive nonparametric regression model from repeated measures data when the covariates are multivariate. To date, while there is some literature in the scalar covariate case, the problem has not been addressed in the multivariate additive model case. Ours represents a first contribution in this direction. As p...

SUMMARY In many problems one wants to model the relationship between a response Y and a covariate X. Sometimes it is diicult, expensive, or even impossible to observe X directly, but one can instead observe a substitute variable W which is easier to obtain. By far the most common model for the relationship between the actual covariate of interest X and the substitute W is W = X + U, where the v...

Conditional copulas are flexible statistical tools that couple joint conditional and marginal conditional distributions. In a linear regression setting with more than one covariate and two dependent outcomes, we consider additive models for studying the dependence between covariates and the copula parameter. We examine the computation and model selection tools needed for Bayesian inference. The...