Topic
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.
Papers published on a yearly basis
Papers
More filters
01 Nov 2010
TL;DR: The model checking problem for stochastic systems with respect to such logics is typically solved by a numerical approach [31,8,35,22,21,5] that iteratively computes (or approximates) the exact measure of paths satisfying relevant subformulas as discussed by the authors.
Abstract: Quantitative properties of stochastic systems are usually specified in logics that allow one to compare the measure of executions satisfying certain temporal properties with thresholds The model checking problem for stochastic systems with respect to such logics is typically solved by a numerical approach [31,8,35,22,21,5] that iteratively computes (or approximates) the exact measure of paths satisfying relevant subformulas; the algorithms themselves depend on the class of systems being analyzed as well as the logic used for specifying the properties Another approach to solve the model checking problem is to simulate the system for finitely many executions, and use hypothesis testing to infer whether the samples provide a statistical evidence for the satisfaction or violation of the specification In this tutorial, we survey the statistical approach, and outline its main advantages in terms of efficiency, uniformity, and simplicity
451 citations
Book•
16 Sep 1999
TL;DR: What is Bootstrapping?
Abstract: What is Bootstrapping? Estimation Confidence Sets and Hypothesis Testing Regression Analysis Forecasting and Time Series Analysis Which Resampling Method Should You Use? Efficient and Effective Simulation Special Topics When Does Bootstrapping Fail? Bibliography Indexes.
450 citations
TL;DR: It is argued that a "Bayesian ecology" would make better use of pre-existing data; allow stronger conclusions to be drawn from large-scale experiments with few replicates; and be more relevant to environmental decision-making.
Abstract: In our statistical practice, we ecologists work comfortably within the hypothetico-deductive epistemology of Popper and the frequentist statistical methodology of Fisher. Consequently, our null hypotheses do not often take into account pre-existing data and do not require parameterization, our experiments demand large sample sizes, and we rarely use results from one experiment to predict the outcomes of future experiments. Comparative statistical statements such as we reject the null hypothesis at the 0.05 level, which reflect the likelihood of our data given our hypothesis, are of little use in communicating our results to nonspecialists or in describing the degree of certitude we have in our conclusions. In contrast, Bayesian statistical inference requires the explicit assignment of prior probabilities, based on existing information, to the outcomes of experiments. Such an assignment forces the parameterization of null and alternative hypotheses. The results of these experiments, regardless of sample size, then can be used to compute posterior probabilities of our hypotheses given the available data. Inferential conclusions in a Bayesian mode also are more meaningful in environmental policy discussions: e.g., our experiments indicate that there is a 95% probability that acid deposition will affect northeastern conifer forests. Based on comparisons with current statistical practice in ecology, I argue that a Bayesian ecology would (a) make better use of pre-existing data; (b) allow stronger conclusions to be drawn from large-scale experiments with few replicates; and (c) be more relevant to environmental decision-making.
449 citations
Book•
07 Jun 1994
TL;DR: The Ordinary Principal Component Model Factor Analysis Factor Analysis of Correlated Observations Ordinal and Nominal Random Data Other Models for Discrete Data Factor Analysis and Least Squares Regression Exercises References.
Abstract: Preliminaries Matrixes, Vector Spaces The Ordinary Principal Components Model Statistical Testing of the Ordinary Principal Components Model Extensions of the Ordinary Principal Components Model Factor Analysis Factor Analysis of Correlated Observations Ordinal and Nominal Random Data Other Models for Discrete Data Factor Analysis and Least Squares Regression Exercises References Index.
448 citations
01 Jan 1998
TL;DR: Model building and data analysis in the biological sciences somewhat presupposes that the person has some advanced education in the quantitative sciences, and statistics in particular, and this requirement also implies that a person has substantial knowledge of statistical hypothesis-testing approaches.
Abstract: Model building and data analysis in the biological sciences somewhat presupposes that the person has some advanced education in the quantitative sciences, and statistics in particular This requirement also implies that a person has substantial knowledge of statistical hypothesis-testing approaches Such people, including ourselves over the past several years, often find it difficult to understand the information-theoretic approach, only because it is conceptually so very different from the testing approach that is so familiar Relatively speaking, the concepts and practical use of the information-theoretic approach are much simpler than those of statistical hypothesis testing, and very much simpler than some of the various Bayesian approaches to data analysis (eg, Laud and Ibrahim 1995 and Carlin and Chib 1995)
446 citations