Bootstrap-based inferential improvements in beta autoregressive moving average model

We consider the issue of performing accurate small sample inference in beta autoregressive moving average model, which is useful for modeling and forecasting continuous variables that assume values in the interval (0,?1). The inferences based on conditional maximum likelihood estimation have good asymptotic properties, but their performances in small samples may be poor. This way, we propose bootstrap bias corrections of the point estimators and different bootstrap strategies for confidence interval improvements. Our Monte Carlo simulations show that finite sample inference based on bootstrap corrections is much more reliable than the usual inferences. We also presented an empirical application.     

Goodness-of-fit tests for centralized Wishart processes

Abstract

In this paper we present several goodness-of-fit tests for the centralized Wishart process, a popular matrix-variate time series model used to capture the stochastic properties of realized covariance matrices. The new test procedures are based on the extended Bartlett decomposition derived from the properties of the Wishart distribution and allows to obtain sets of independently and standard normally distributed random variables under the null hypothesis. Several tests for normality and independence are then applied to these variables in order to support or to reject the underlying assumption of a centralized Wishart process. In order to investigate the influence of estimated parameters on the suggested testing procedures in the finite-sample case, a simulation study is conducted. Finally, the new test methods are applied to real data consisting of realized covariance matrices computed for the returns on six assets traded on the New York Stock Exchange.     

On the corrections to information matrix tests

This paper addresses the issue of designing finite-sample corrections to information matrix tests. We review a Cornish-Fisher correction that has been propowed elsewhere and propose an alternative, Bartlett-type correction. Simulation results for skewness, excess kurtosis, normality and heteroskedasticity tests are given.     

The Size and Power of Bootstrap and Bartlett-Corrected Tests of Hypotheses on the Cointegrating Vectors

In this paper we compare Bartlett-corrected, bootstrap, and fast double bootstrap tests on maximum likelihood estimates of cointegration parameters. The key result is that both the bootstrap and the Bartlett-corrected tests must be based on the unrestricted estimates of the cointegrating vectors: procedures based on the restricted estimates have almost no power. The small sample size bias of the asymptotic test appears so severe as to advise strongly against its use with the sample sizes commonly available; the fast double bootstrap test minimizes size bias, while the Bartlett-corrected test is somehow more powerful.     

《汉语大词典》释义商兑与引书订讹举例

《汉语大词典》已成为文史哲专业人士的良师益友和不可缺少的工具。人们希望其中释义和引书方面的错讹越少越好。但《汉语大词典》在这两方面均存在一些可商兑的条目,它们在释义或引文方面的错误应当分别加以改正。     

用固定角距法测量度盘对径改正的探讨

本文对用固定角距法测量度盘对径改正的原理作了较深入的探讨，并推导了测量对径改正的计算公式。     

Symbolic computation: a unified approach to studying likelihood

We describe a set of procedures that automate many algebraic calculations common in statistical asymptotic theory. The procedures are very general and serve to unify the study of likelihood and likelihood type functions. The procedures emulate techniques one would normally carry out by hand; this strategy is emphasised throughout the paper. The purpose of the software is to provide a practical alternative to difficult manual algebraic computations. The result is a method that is quick and free of clerical error.     

On confidence intervals associated with the usual and adjusted likelihoods

In the presence of nuisance parameters, we drive an explicit higher order asymptotic formula to compare the expected lengths of confidence intervals given by likelihood ratio statistics arising from the usual profile likelihood and various adjustments thereof. Highest posterior density regions, with approximate frequentist validity, are also included in the study.     

Experimental designs with dependent observations: New results and comments on previous results

We present a new sufficient condition on the covariance matrix of the normality distributed observations of an ANOVA model (with orthogonal decomposition of the total sum of squares) under which the F-statictics are distributed proportionally to Fisher F-random variables variables. A new proof of a previous result, a necessary and sufficient condition for applicability of Barlett’s test to the observations of a one-way ANOVA models, and comments on recent results are also given.