Statistical hypothesis testing

About: Statistical hypothesis testing is a research topic. Over the lifetime, 19580 publications have been published within this topic receiving 1037815 citations. The topic is also known as: statistical hypothesis testing & confirmatory data analysis.

On tests of the overall treatment effect in meta-analysis with normally distributed responses.

Abstract: For the meta-analysis of controlled clinical trials or epidemiological studies, in which the responses are at least approximately normally distributed, a refined test for the hypothesis of no overall treatment effect is proposed. The test statistic is based on a direct estimation function for the variance of the overall treatment effect estimator. As outcome measures, the absolute and the standardized difference between means are considered. In simulation studies it is shown that the proposed test keeps the prescribed significance level very well in contrast to the commonly used tests in the fixed effects and random effects model, respectively, which can become very liberal. Furthermore, just for using the proposed test it is not necessary to choose between the fixed effects and the random effects approach in advance.

Choice of structural model via parsimony: a rationale based on precision

Abstract: It is shown that, in large samples, the more parsimonious of two competing nested models yields an estimator of the common parameters that has smaller sampling variance. The use of parsimony as a criterion for choice between two otherwise acceptable models can thus be rationalized on the basis of precision of estimation.

Cross-Section Regression with Common Shocks

Abstract: This paper considers regression models for cross-section data that exhibit cross-section dependence due to common shocks, such as macroeconomic shocks. The paper analyzes the properties of least squares (LS) estimators in this context. The results of the paper allow for any form of cross-section dependence and heterogeneity across population units. The probability limits of the LS estimators are determined, and necessary and sufficient conditions are given for consistency. The asymptotic distributions of the estimators are found to be mixed normal after recentering and scaling. The t, Wald, and F statistics are found to have asymptotic standard normal, X 2 , and scaled X 2 distributions, respectively, under the null hypothesis when the conditions required for consistency of the parameter under test hold. However, the absolute values of t, Wald, and F statistics are found to diverge to infinity under the null hypothesis when these conditions fail. Confidence intervals exhibit similarly dichotomous behavior. Hence, common shocks are found to be innocuous in some circumstances, but quite problematic in others. Models with factor structures for errors and regressors are considered. Using the general results, conditions are determined under which consistency of the LS estimators holds and fails in models with factor structures. The results are extended to cover heterogeneous and functional factor structures in which common factors have different impacts on different population units.

Sample size determination : a review

Abstract: This paper is concerned with methods of sample size determination. The approach is to cover a small number of simple problems, such as estimating the mean of a normal distribution or the slope in a regression equation, and to present some key techniques. The methods covered are in two groups: frequentist and Bayesian. Frequentist methods specify a null and alternative hypothesis for the parameter of interest and then find the sample size by controlling both size and power. These methods often need to use prior information but cannot allow for the uncertainty that is associated with it. By contrast, the Bayesian approach offers a wide variety of techniques, all of which offer the ability to deal with uncertainty associated with prior information.