Coverage probability

About: Coverage probability is a research topic. Over the lifetime, 2479 publications have been published within this topic receiving 53259 citations.

Confidence intervals in ridge regression by bootstrapping the dependent variable: a simulation study

Abstract: As it is well known, Ridge Regression can be a useful technique for estimating the coefficients of a Multiple Regression Model in the presence of multicollinearity. It has, however, the drawback that the distribution of the estimator is unknown, so that only asymptotic confidence intervals may be obtained. The aim of this paper is to report on the use of a technique that combines the Bootstrap and the Edgeworth Expansion to obtain an approximation to the distribution of some Ridge Regression estimators. Some simulation experiments were carried out to compare the asymptotic confidence intervals with those obtained with this technique

A simple procedure for computing improved prediction intervals for autoregressive models

Abstract: . This article concerns the construction of prediction intervals for time series models. The estimative or plug-in solution is usually not entirely adequate, since the (conditional) coverage probability may differ substantially from the nominal value. Prediction intervals with improved (conditional) coverage probability can be defined by adjusting the estimative ones, using rather complicated asymptotic procedures or suitable simulation techniques. This article extends to Markov process models a recent result by Vidoni, which defines a relatively simple predictive distribution function, giving improved prediction limits as quantiles. This new solution is fruitfully considered in the challenging context of prediction for time-series models, with particular regard to AR and ARCH processes.

Modeling multivariate discrete failure time data.

Abstract: A bivariate discrete survival distribution that allows flexible modeling of the marginal distributions and yields a constant odds ratio at any grid point is proposed The distribution can be extended to a multivariate distribution and is readily generalized to accommodate covariates in the marginal distributions and pairwise odds ratios In addition, a pseudo-likelihood estimation procedure for estimating the regression coefficients in the marginal models and the association parameters in the pairwise odds ratios is presented We evaluate the performance of the proposed estimation procedure through simulations For bivariate data, pseudo-likelihood estimation of the association parameter has high efficiency Loss of efficiency in the marginal regression coefficient estimates is small when the association is not strong For both the marginal regression coefficients and the association parameter, coverage probabilities are close to the 95% nominal level For multivariate data, the simulation results show that the parameter estimates are consistent Coverage probability for the regression coefficient in the marginal model is close to the 95% nominal level but is slightly less than the nominal level for the association parameter We illustrate the proposed methods using a subset of the Framingham Heart Study data where a significant positive association was found between the failure times of siblings

Bayesian Inference and prediction or order statistics for a Type II censored Weibull distribution

Abstract: This paper describes the Bayesian inference and prediction of the two-parameter Weibull distribution when the data are Type-II censored data. The aim of this paper is two fold. First we consider the Bayesian inference of the unknown parameters under different loss functions. The Bayes estimates cannot be obtained in closed form. We use Gibbs sampling procedure to draw Markov Chain Monte Carlo (MCMC) samples and it has been used to compute the Bayes estimates and also to construct symmetric credible intervals. Further we consider the Bayes prediction of the future order statistics based on the observed sample. We consider the posterior predictive density of the future observations and also construct a predictive interval with a given coverage probability. Monte Carlo simulations are performed to compare different methods and one data analysis is performed for illustration purposes.