Parametric statistics

About: Parametric statistics is a research topic. Over the lifetime, 39200 publications have been published within this topic receiving 765761 citations.

Techniques of event history modeling : new approaches to causal analysis

Abstract: Contents: Preface. Introduction. Event History Data Structures. Nonparametric Descriptive Methods. Exponential Transition Rate Models. Piecewise Constant Exponential Models. Exponential Models With Time-Dependent Covariates. Parametric Models of Time-Dependence. Methods to Check Parametric Assumptions. Semi-Parametric Transition Rate Models. Problems of Model Specification. Appendix: Basic Information About TDA.

System and method for measuring wireless device and network usage and performance metrics

Abstract: Systems and methods for measuring wireless device and wireless network usage and performance metrics are set forth. The system includes at least one wireless device, and data gathering software installed on the wireless device for collecting device parametric data, network parametric data, event data. A control center may receive, store and process said device parametric data, network parametric data, and event data.

Modern Robust Statistical Methods An Easy Way to Maximize the Accuracy and Power of Your Research

Abstract: Summary Most researchers analyze data using outdated methods.Classic parametric tests, effect sizes, and conÞdence inter-vals around effect size statistics are not robust to violationsof their assumptions, and violations seem to occur fre-quently when real data are analyzed. Researchers relyingon statistical tests (e.g. LeveneOs test) to identify assump-tion violations may frequently fail to detect deviations fromnormality and homoscedasticity that are large enough toseriously affect the Type I error rate and power of classicparametric tests. We recommend that researchers bypassclassic parametric statistics in favor of modern robustmethods. Modern methods perform well in a much largerrange of situations than do classic techniques. The use ofmodern methods will result in researchers Þnding morestatistically signiÞcant results when real effects exist in thepopulation. Using modern methods will also reduce thenumber of Type I errors made by researchers and result inmore accurate conÞdence intervals around robust effectsize statistics. A range of accessible texts about modernmethods is available (e.g., Wilcox, 2001, 2003), as well asa wide range of software to perform modern analyses.Given the wealth of resources available, researchers have atremendous opportunity to engage in modern robust statis-tical methods.

Smoothing Parameter and Model Selection for General Smooth Models

Abstract: This article discusses a general framework for smoothing parameter estimation for models with regular likelihoods constructed in terms of unknown smooth functions of covariates. Gaussian random effects and parametric terms may also be present. By construction the method is numerically stable and convergent, and enables smoothing parameter uncertainty to be quantified. The latter enables us to fix a well known problem with AIC for such models, thereby improving the range of model selection tools available. The smooth functions are represented by reduced rank spline like smoothers, with associated quadratic penalties measuring function smoothness. Model estimation is by penalized likelihood maximization, where the smoothing parameters controlling the extent of penalization are estimated by Laplace approximate marginal likelihood. The methods cover, for example, generalized additive models for nonexponential family responses (e.g., beta, ordered categorical, scaled t distribution, negative binomial a...

Parametric shape analysis via 3-valued logic

Abstract: Shape analysis concerns the problem of determining "shape invariants" for programs that perform destructive updating on dynamically allocated storage. This article presents a parametric framework for shape analysis that can be instantiated in different ways to create different shape-analysis algorithms that provide varying degrees of efficiency and precision. A key innovation of the work is that the stores that can possibly arise during execution are represented (conservatively) using 3-valued logical structures. The framework is instantiated in different ways by varying the predicates used in the 3-valued logic. The class of programs to which a given instantiation of the framework can be applied is not limited a priori (i.e., as in some work on shape analysis, to programs that manipulate only lists, trees, DAGS, etc.); each instantiation of the framework can be applied to any program, but may produce imprecise results (albeit conservative ones) due to the set of predicates employed.