摘要:

We consider a parametric nonlinear regression model with independent and Gaussian errors. We assume that the number of observations is fixed and that the variance of errors tends to zero; we then derive the properties of confidence intervals for the parameters. These confidence intervals are calculated using both the quantiles of the estimator's asymptotic law and the quantiles of the estimator's bootstrap distribution. We show that if the pseudo-errors are simulated using the Gaussian distribution, then bootstrap can be applied successfully. The usual reduction in coverage error of confidence intervals is not, however, verified. A simulation study for a biadditive model shows the superiority of bootstrap when calculating confidence intervals for the interaction parameters.

ISSN:0233-1888    

DOI:10.1080/02331889908802664

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

For different classes of deterministic and random sampling (tk), we establish the asymptotic expressions for the bias and the variance of the estimatern(x) based on sampled datafor the regression functionr(x) =E(YtXt=x) of unbounded continuous-time processes(not necessarily stationary). Under mild mixing conditions, we show thatrn(x) has exactly the same asymptotic quadratic error as in the i.i.d. case. In order to prove this result, we use some large deviations inequalities for mixing processes.

ISSN:0233-1888    

DOI:10.1080/02331889908802665

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

ISSN:0233-1888    

DOI:10.1080/02331889908802666

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

This paper deals with the problem of predicting the median of future sample from the one parameter exponential distribution. The density function of the median is obtained when the sample size is even. Parametric prediction bounds for the median of future sample for arbitrary size are obtained using the Bayesian approach. A numerical example is given to illustrate the procedures.

ISSN:0233-1888    

DOI:10.1080/02331889908802667

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

In this paper, three competing survival function estimators are compared under the assumptions of the so-called Koziol– Green model, which is a simple model of informative random censoring. It is shown that the model specific estimators of Ebrahimi and Abdushukurov, Cheng, and Lin are asymptotically equivalent. Further, exact expressions for the (noncentral) moments of these estimators are given, and their biases are analytically compared with the bias of the familiar Kaplan–Meier estimator. Finally, MSE comparisons of the three estimators are given for some selected rates of censoring.

ISSN:0233-1888    

DOI:10.1080/02331889908802668

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

Steven K. Thompson: Sampling. Wiley, New York 1992, xv+ 343 pp.

ISSN:0233-1888    

DOI:10.1080/02331889908802669

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

ISSN:0233-1888    

DOI:10.1080/02331889908802663

出版商:Gordon & Breach Sceince Publishers    

年代:1999

数据来源: Taylor