Introducing model uncertainty by moving blocks bootstrap

It is common in parametric bootstrap to select the model from the data, and then treat as if it were the true model. Chatfield (1993, 1996) has shown that ignoring the model uncertainty may seriously undermine the coverage accuracy of prediction intervals. In this paper, we propose a method based on moving block bootstrap for introducing the model selection step in the resampling algorithm. We present a Monte Carlo study comparing the finite sample properties of the proposel method with those of alternative methods in the case of prediction intervas.     

论当前的国际形势与我国的外交政策

江泽民同志在党的十六大报告中对国际形势进行了分析 ,指出和平与发展仍是当今时代的主题。不管国际风云如何变幻 ,我们始终不渝地奉行独立自主的和平外交政策。本文根据十六大报告的论述 ,就如何正确认识当前的国际形势 ,奉行独立自主的和平外交政策进行了较为全面的阐述。     

优良樟子松种源引种试验初报

通过７ 年的造林试验观测，筛选出适宜科尔沁沙地北部地区生长的优良樟子松种源呼玛、青山、高峰、塔河等６ 个种源     

Variance Estimation Based on Invariance Principles

Consistent variance estimators for certain stochastic processes are suggested using the fact that (weak or strong) invariance principles may be available. Convergence rates are also derived, the latter being essentially determined by the approximation rates in the corresponding invariance principles. As an application, a change point test in a simple AMOC renewal model is briefly discussed, where variance estimators possessing good enough convergence rates are required.     

Eine bemerkung zur stetigkeit zufälliger felder

In the paper a sequence of bounded regions containing n independent identically and uniformly on D_n distributed points is considered. It is assumed that the d–dimensional volume v(D_n) is asymptotically proportional to n. Under these conditions it is shown that the number of pairs of points within a distance r>0 of each other is asymptotically normally distributed. For proving this among other things a lemma of BOLTHAUSEN is used, whereas even strong estimates for U–statistics are insufficient. The obtained result is applied for testing the hypothesis of randomness     

Bootstrap tests for variance components in generalized linear mixed models

In many applications of generalized linear mixed models to clustered correlated or longitudinal data, often we are interested in testing whether a random effects variance component is zero. The usual asymptotic mixture of chi‐square distributions of the score statistic for testing constrained variance components does not necessarily hold. In this article, the author proposes and explores a parametric bootstrap test that appears to be valid based on its estimated level of significance under the null hypothesis. Results from a simulation study indicate that the bootstrap test has a level much closer to the nominal one while the asymptotic test is conservative, and is more powerful than the usual asymptotic score test based on a mixture of chi‐squares. The proposed bootstrap test is illustrated using two sets of real‐life data obtained from clinical trials. The Canadian Journal of Statistics © 2009 Statistical Society of Canada     

The Distribution of Stock Returns When the Market Is Up

Abstract

For some investments, the relation between stock returns and the market proxy is conventionally described by a linear regression model with the normality assumption. This paper derives the distribution of stock returns for a security in an upgrade (or downgrade) market with the assumption that the log stock returns of the market proxy follow a mixture of normal distributions. We discuss MLE and the method of moment estimation for parameters involved in the model. An analysis of stock data in Johannesburg Stock Exchange is included to illustrate the model. This note explains the phenomenon in financial analysis regarding the shape of the distribution of long-run stock returns limited on an upgrade or downgrade market index.     

Effects of correlated disturbances of some regression problems

In a linear regression model the disturbances are assumed to be independently distributed. If the correlation among the disturbances exists, then the usual F statistics have not the F distribution and the distributions depend on the regressor variables. This paper gives bounds on the F statistics, whose distributions do not depends on the regressor variables. The bounds are applied to a test on a general linear hypothesis of the regression coefficients and to evaluate the confidence level of a prediction set.     

The exact power function of an exact test of a regression model against multiple separate alternatives

A common procedure for testing a regression model against separate alternatives is to check the statistical significance of predictions from the latter appended as artificial regressors in the model under test. This paper derives the previously-unknown exact small-sample power function of such a procedure. It is then demonstrated that the procedure is biased in small samples.