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Parametric statistics
About: Parametric statistics is a research topic. Over the lifetime, 39200 publications have been published within this topic receiving 765761 citations.
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TL;DR: A number of considerations related to choosing methods for the meta-analysis of ecological data, including the choice of parametric vs. resampling methods, reasons for conducting weighted analyses where possible, and comparisons fixed vs. mixed models in categorical and regression-type analyses are outlined.
Abstract: Meta-analysis is the use of statistical methods to summarize research findings across studies. Special statistical methods are usually needed for meta-analysis, both because effect-size indexes are typically highly heteroscedastic and because it is desirable to be able to distinguish between-study variance from within-study sampling-error variance. We outline a number of considerations related to choosing methods for the meta-analysis of ecological data, including the choice of parametric vs. resampling methods, reasons for conducting weighted analyses where possible, and comparisons fixed vs. mixed models in categorical and regression-type analyses.
954 citations
TL;DR: In this paper, two very closely related definitions of robustness of a sequence of estimators are given which take into account the types of deviations from parametric models that occur in practice.
Abstract: Two very closely related definitions of robustness of a sequence of estimators are given which take into account the types of deviations from parametric models that occur in practice. These definitions utilize the properties of the Prokhorov distance between probability distributions. It is proved that weak $^\ast$-continuous functionals on the space of probability distributions define robust sequences of estimators (in either sense). The concept of the "breakdown point" of a sequence of estimators is defined, and some examples are given.
949 citations
TL;DR: The strengths and limitations of correlation-based signal processing methods, with emphasis on the bispectrum and trispectrum, and the applications of higher-order spectra in signal processing are discussed.
Abstract: The strengths and limitations of correlation-based signal processing methods are discussed. The definitions, properties, and computation of higher-order statistics and spectra, with emphasis on the bispectrum and trispectrum are presented. Parametric and nonparametric expressions for polyspectra of linear and nonlinear processes are described. The applications of higher-order spectra in signal processing are discussed. >
931 citations
TL;DR: In this paper, the authors use experimental or observational data to estimate decision regimes that result in a maximal mean response, and make smooth parametric assumptions only on quantities that are directly relevant to the goal of estimating the optimal rules.
Abstract: Summary. A dynamic treatment regime is a list of decision rules, one per time interval, for how the level of treatment will be tailored through time to an individual’s changing status. The goal of this paper is to use experimental or observational data to estimate decision regimes that result in a maximal mean response. To explicate our objective and to state the assumptions, we use the potential outcomes model. The method proposed makes smooth parametric assumptions only on quantities that are directly relevant to the goal of estimating the optimal rules. We illustrate the methodology proposed via a small simulation.
922 citations
TL;DR: In this paper, the authors proposed new methods for testing and correcting for sample selection bias in panel data models, which allow the unobserved effects in both the regression and selection equations to be correlated with the observed variables; the error distribution in the regression equation is unspecified; arbitrary serial dependence in the idiosyncratic errors of both equations is allowed; all idiosyncratic error can be heterogeneously distributed.
917 citations