Parametric statistics

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

Grey Wolf Optimizer Algorithm-Based Tuning of Fuzzy Control Systems With Reduced Parametric Sensitivity

Abstract: This paper proposes an innovative tuning approach for fuzzy control systems (CSs) with a reduced parametric sensitivity using the Grey Wolf Optimizer (GWO) algorithm. The CSs consist of servo system processes controlled by Takagi–Sugeno–Kang proportional-integral fuzzy controllers (TSK PI-FCs). The process models have second-order dynamics with an integral component, variable parameters, a saturation, and dead-zone static nonlinearity. The sensitivity analysis employs output sensitivity functions of the sensitivity models defined with respect to the parametric variations of the processes. The GWO algorithm is used in solving the optimization problems, where the objective functions include the output sensitivity functions. GWO's motivation is based on its low-computational cost. The tuning approach is validated in an experimental case study of a position control for a laboratory nonlinear servo system, and TSK PI-FCs with a reduced process small time constant sensitivity are offered.

On parameter estimation and goodness-of-fit testing for spatial point patterns

Abstract: The paper discusses the objectives of spatial point pattern analsis with particular reference to the distinction between mapped and sampled data. For the former case available nlodels are reviewed briefly the role of preliminary testing is discussed and a procedure for ftting a parametric sslodel is outlined. A simulation studA of several tests of spatial randonlness is intended to provide some insight into the suitability for model-fitting of various summasy descriptions of a mapped pattern. Two examples illustrate the use of the statistical techniques. Soss1e problem areas which-merit further investigation are identified.

Estimation of counterfactual distributions using quantile regression

Abstract: This paper proposes estimators of unconditional distribution functions in the presence of covariates. The methods are based on the estimation of the conditional distribution by (parametric or nonparametric) quantile regression. The conditional distribution is then integrated over the range of the covariates, allowing for the estimation of counterfactual distributions. In the parametric settings, we propose an extension of the Oaxaca (1973) / Blinder (1973) decomposition of means to the full distribution. In the nonparametric setting, we develop an efficient local-linear regression estimator for quantile treatment effects. We show root n consistency and asymptotic normality of the estimators and present analytical estimators of their variance. Monte-Carlo simulations show that the procedures perform well in finite samples. An application to the black-white wage gap illustrates the usefulness of the estimators.

Inference in Nonlinear Econometric Models with Structural Change

Abstract: This paper extends the classical test for structural change in linear regression models (see Chow (1960)) to a wide variety of nonlinear models, estimated by a variety of different procedures. Wald, Lagrange multiplier-like, and likelihood ratio-like test statistics are introduced. The results allow for heterogeneity and temporal dependence of the observations. In the process of developing the above tests, the paper also provides a compact presentation of general unifying results for estimation and testing in nonlinear parametric econometric models.