Coverage probability

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

Small sample validity of latent variable models for correlated binary data

Abstract: Recently developed models for correlated binary responses assume that the binary outcomes are manifestations of latent normal variables. In these models, the covariance structure is based on the tetrachoric correlations and is more flexible than similar models which use Pearson correlations. The parameters are estimated by the approach of generalized estimating equations(GEE). This simulation study investigation the impact of sample size and incorrectly assuming an independence model on parameter estimates in terms of bias, efficiency and coverage probability is close to the nomial level unless the sample is small (≤20), or an incorrect independence model is assumed for correlated unbalanced data. Methods for reducing bias are proposed using a weighed jackknife technique

Optimal Bandwidth Choice for Interval Estimation in GMM Regression

Abstract: In time series regression with nonparametrically autocorrelated errors, it is now standard empirical practice to construct confidence intervals for regression coefficients on the basis of nonparametrically studentized t-statistics The standard error used in the studentization is typically estimated by a kernel method that involves some smoothing process over the sample autocovariances The underlying parameter (M) that controls this tuning process is a bandwidth or truncation lag and it plays a key role in the finite sample properties of tests and the actual coverage properties of the associated confidence intervals The present paper develops a bandwidth choice rule for M that optimizes the coverage accuracy of interval estimators in the context of linear GMM regression The optimal bandwidth balances the asymptotic variance with the asymptoticbias of the robust standard error estimator This approach contrasts with the conventional bandwidth choice rule for nonparametric estimation where the focus is the nonparametric quantity itself and the choice rule balances asymptotic variance with squared asymptotic bias It turns out that the optimal bandwidth for interval estimation has a different expansion rate and is typically substantially larger than the optimal bandwidth for point estimation of the standard errors The new approach to bandwidth choice calls for refined asymptotic measurement of the coverage probabilities, which are provided by means of an Edgeworth expansion of the finite sample distribution of the nonparametrically studentized t-statistic This asymptotic expansion extends earlier work and is of independent interest A simple plug-in procedure for implementing this optimal bandwidth is suggested and simulations confirm that the new plug-in procedure works well in finite samples Issues of interval length and false coverage probability are also considered, leading to a secondary approach to bandwidth selection with similar properties

A wavelet-based spectral method for steady-state simulation analysis

Abstract: We develop an automated wavelet-based spectral method for constructing an approximate confidence interval on the steady-state mean of a simulation output process. This procedure, called WASSP, determines a batch size and a warm-up period beyond which the computed batch means form an approximately stationary Gaussian process. Based on the log-smoothed-periodogram of the batch means, WASSP uses wavelets to estimate the batch means log-spectrum and ultimately the steady-state variance constant (SSVC) of the original (unbatched) process. WASSP combines the SSVC estimator with the grand average of the batch means in a sequential procedure for constructing a confidence-interval estimator of the steady-state mean that satisfies user-specified requirements on absolute or relative precision as well as coverage probability. An extensive performance evaluation provides evidence of WASSP's robustness in comparison with some other output analysis methods.