Covariate adjustment methods are frequently used when baseline covariate information is available for randomized controlled trials. Using a simulation study, we compared the analysis of covariance (ANCOVA) with three nonparametric covariate adjustment methods with respect to point and interval estimation for the difference between means. The three alternative methods were based on important mem...

The random design setting for linear regression concerns estimators based on a random sample of covariate/response pairs. This work gives explicit bounds on the prediction error for the ordinary least squares estimator and the ridge regression estimator under mild assumptions on the covariate/response distributions. In particular, this work provides sharp results on the “out-of-sample” predicti...

For nonparametric regression models with fixed and random design, two classes of estimators for the error variance have been introduced: second sample moments based on residuals from a nonparametric fit, and difference-based estimators. The former are asymptotically optimal but require estimating the regression function; the latter are simple but have larger asymptotic variance. For nonparametr...

Some products like green vegetables, volatile liquids and others deteriorate continuously due to evaporation, spoilage etc. In this study, an inventory model is developed for deteriorating items with linearly time dependent demand rate under inflation and time discounting over a finite planning horizon. Shortages are allowed and linearly time dependent. Mathematical model is presented for the p...

over the past three decades vertical price transmission analysis has been the subject of considerable attention in applied agricultural conomics. it has been argued that the existence of asymmetric price transmission generates rents for marketing and processing agents. retail prices allegedly move faster upwards than downwards in response to farm level pricemovements. this is an important issue...

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...

We describe a Bayesian semiparametric (failure time) transformation model for which an unknown monotone transformation of failure times is assumed linearly dependent on observed covariates with an unspecified error distribution. The two unknowns: the monotone transformation and error distribution are assigned prior distributions with large supports. Our class of regression model includes the pr...

In this paper we consider the estimation of the error distribution in a heteroscedastic nonparametric regression model with multivariate covariates. As estimator we consider the empirical distribution function of residuals, which are obtained from multivariate local polynomial fits of the regression and variance functions, respectively. Weak convergence of the empirical residual process to a Ga...

A health outcome can be observed at a spatial location and we wish to relate this to a set of environmental measurements made on a sampling grid. The environmental measurements are covariates in the model but due to the interpolation associated with the grid there is an error inherent in the covariate value used at the outcome location. Since there may be multiple measurements made on different...

We introduce a new approach to inference for subgroups in clinical trials. We use Bayesian model selection, and a threshold on posterior model probabilities to identify subgroup effects for reporting. For each covariate of interest, we define a separate class of models, and use the posterior probability associated with each model and the threshold to determine the existence of a subgroup effect...