Search

Abstract: Estimation in the response model under callbacks has been relatively underdeveloped though the response model has been frequently applied to nonresponse weighting adjustment or imputation. We propose an estimator using response probability and an auxiliary variable under callbacks. We also study the estimation of the response probability and a replication variance estimator of the estimator. A simulation study illustrates our technique. [Copyright &y& Elsevier]

In this paper, we consider the problem of testing for a change of the marginal density of a strong mixing process. The test statistic is constructed based on the sequential kernel estimate. In order to derive the asymptotic distribution of the test statistic, we first show that a functional central limit theorem holds for the sequential density estimator under some regularity conditions. Based on the result, we show that the limiting distribution of the test statistic is a function of independent Brownian bridges. [Copyright &y& Elsevier]

In this paper we consider the goodness of fit test of the errors of autoregressive models using the kernel estimate of the marginal density function based on residuals. The test statistic is based on the integrated squared error of the nonparametric density estimate and a smoothed version of the parametric fit of the density. It is shown that the test statistic behaves asymptotically the same as the one based on true errors unless the autoregressive process is unstable. [Copyright &y& Elsevier]