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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.


Papers
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Journal ArticleDOI
TL;DR: In this article, the authors consider a regression model in which the mean function may have a discontinuity at an unknown point and propose an estimate of the location of the discontinuity based on one-side nonparametric regression estimates of the mean functions.
Abstract: We consider a regression model in which the mean function may have a discontinuity at an unknown point. We propose an estimate of the location of the discontinuity based on one-side nonparametric regression estimates of the mean function. The change point estimate is shown to converge in probability at rate 0(n-1) and to have the same asymptotic distribution as maximum likelihood estimates considered by other authors under parametric regression models. Confidence regions for the location and size of the change are also discussed.

176 citations

Book ChapterDOI
25 Sep 2006
TL;DR: It is demonstrated for the first time how information about an algorithm's parameter settings can be incorporated into a model, and how such models can be used to automatically adjust the algorithm's parameters on a per-instance basis in order to optimize its performance.
Abstract: Machine learning can be used to build models that predict the run-time of search algorithms for hard combinatorial problems. Such empirical hardness models have previously been studied for complete, deterministic search algorithms. In this work, we demonstrate that such models can also make surprisingly accurate predictions of the run-time distributions of incomplete and randomized search methods, such as stochastic local search algorithms. We also show for the first time how information about an algorithm's parameter settings can be incorporated into a model, and how such models can be used to automatically adjust the algorithm's parameters on a per-instance basis in order to optimize its performance. Empirical results for Novelty+ and SAPS on structured and unstructured SAT instances show very good predictive performance and significant speedups of our automatically determined parameter settings when compared to the default and best fixed distribution-specific parameter settings.

176 citations

Proceedings ArticleDOI
01 Dec 1998
TL;DR: The article discusses developments in this area, including alternative metamodel types and experimental designs, which are based on parametric polynomial response surface approximations.
Abstract: The simulation community has used metamodels to study the behavior of computer simulations for over twenty-five years. The most popular techniques have been based on parametric polynomial response surface approximations. The article discusses developments in this area, including alternative metamodel types and experimental designs.

176 citations

Journal ArticleDOI
TL;DR: New algorithms for robust stability analysis and gain-scheduled controller synthesis for linear systems affected by time-varying parametric uncertainties are presented and can also be applied to parameter-dependent nonlinear systems with real-rational nonlinearities.
Abstract: We present new algorithms for robust stability analysis and gain-scheduled controller synthesis for linear systems affected by time-varying parametric uncertainties. These new techniques can also be applied to parameter-dependent nonlinear systems with real-rational nonlinearities. Sufficient conditions for robust stability, as well as conditions for the existence of a robustly stabilizing gain-scheduled controller, are given in terms of a finite number of linear matrix inequalities (LMIs); explicit formulas for constructing robustly stabilizing gain-scheduled controllers are given in terms of the feasible set of these LMIs. The improvement offered by our approach over existing methods for stability analysis and gain-scheduled controller synthesis for parameter-dependent linear systems are analyzed in theory. Numerical examples demonstrate that our approach can offer significant improvement in practice.

176 citations

Journal ArticleDOI
TL;DR: In this paper, a two-stage estimation procedure is proposed to estimate the link function for the single index and the parameters in the single indices, as well as the linear component of the model, and asymptotic normality is established for both parametric components.
Abstract: In this paper, we study the estimation for a partial-linear single-index model. A two-stage estimation procedure is proposed to estimate the link function for the single index and the parameters in the single index, as well as the parameters in the linear component of the model. Asymptotic normality is established for both parametric components. For the index, a constrained estimating equation leads to an asymptotically more efficient estimator than existing estimators in the sense that it is of a smaller limiting variance. The estimator of the nonparametric link function achieves optimal convergence rates, and the structural error variance is obtained. In addition, the results facilitate the construction of confidence regions and hypothesis testing for the unknown parameters. A simulation study is performed and an application to a real dataset is illustrated. The extension to multiple indices is briefly sketched.

176 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20252
20242
20233,966
20227,822
20211,968
20202,033