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Asymptotic Properties Of A Class Of Robust M Estimators For Nonlinear Regression Models With Momentless Distributed Errors And Regressors
Download Asymptotic Properties Of A Class Of Robust M Estimators For Nonlinear Regression Models With Momentless Distributed Errors And Regressors full books in PDF, epub, and Kindle. Read online Asymptotic Properties Of A Class Of Robust M Estimators For Nonlinear Regression Models With Momentless Distributed Errors And Regressors ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Asymptotic Properties of a Class of Robust M-estimators for Nonlinear Regression Models with Momentless Distributed Errors and Regressors by : Hermanus Josephus Bierens
Download or read book Asymptotic Properties of a Class of Robust M-estimators for Nonlinear Regression Models with Momentless Distributed Errors and Regressors written by Hermanus Josephus Bierens and published by . This book was released on 1977 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Properties of Maximum Likelihood Estimators in the Nonlinear Regression Model when the Errors are Neither Independent Nor Identically Distributed by : R. D. H. Heijmans
Download or read book Asymptotic Properties of Maximum Likelihood Estimators in the Nonlinear Regression Model when the Errors are Neither Independent Nor Identically Distributed written by R. D. H. Heijmans and published by . This book was released on 1982 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Properties of Maximum Likelihood Estimators in the Nonlinear Regression Model when the Errors are Neither Independent for Identically Distributed by : R. D. H. Heijmans
Download or read book Asymptotic Properties of Maximum Likelihood Estimators in the Nonlinear Regression Model when the Errors are Neither Independent for Identically Distributed written by R. D. H. Heijmans and published by . This book was released on 1982 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Economics Working Papers by : John Fletcher
Download or read book Economics Working Papers written by John Fletcher and published by . This book was released on 1978 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Distributions of Some Scale Estimators in Nonlinear Models With Long Memory Errors Having Infinite Variance by :
Download or read book Asymptotic Distributions of Some Scale Estimators in Nonlinear Models With Long Memory Errors Having Infinite Variance written by and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Properties of S-estimators for Nonlinear Regression Models with Dependent, Heterogeneous Processes by : Shinichi Sakata
Download or read book Asymptotic Properties of S-estimators for Nonlinear Regression Models with Dependent, Heterogeneous Processes written by Shinichi Sakata and published by . This book was released on 1994 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Properties of S Estimators for Nonlinear Regression Models with de by : Shinichi Sakata
Download or read book Asymptotic Properties of S Estimators for Nonlinear Regression Models with de written by Shinichi Sakata and published by . This book was released on 1994 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis On the Non-Asymptotic Properties of Regularized M-Estimators by : Demian Pouzo
Download or read book On the Non-Asymptotic Properties of Regularized M-Estimators written by Demian Pouzo and published by . This book was released on 2016 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a general framework for regularization in M-estimation problems under time dependent (absolutely regular-mixing) data which encompasses many of the existing estimators. We derive non-asymptotic concentration bounds for the regularized M-estimator. Our results exhibit a variance-bias trade-off, with the variance term being governed by a novel measure of the complexity of the parameter set. We also show that the mixing structure affect the variance term by scaling the number of observations; depending on the decay rate of the mixing coefficients, this scaling can even affect the asymptotic behavior. Finally, we propose a data-driven method for choosing the tuning parameters of the regularized estimator which yield the same (up to constants) concentration bound as one that optimally balances the (squared) bias and variance terms. We illustrate the results with several canonical examples.
Book Synopsis A Class of Partially Adaptive One-step M-estimators for the Nonlinear Regression Model by : Benedikt M. Potscher
Download or read book A Class of Partially Adaptive One-step M-estimators for the Nonlinear Regression Model written by Benedikt M. Potscher and published by . This book was released on 1984 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Properties of Estimators for the Linear Panel Regression Model with Individual Effects and Serially Correlated Errors by : Badi H. Baltagi
Download or read book Asymptotic Properties of Estimators for the Linear Panel Regression Model with Individual Effects and Serially Correlated Errors written by Badi H. Baltagi and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper studies the asymptotic properties of standard panel data estimators in a simple panel regression model with error component disturbances. Both the regressor and the remainder disturbance term are assumed to be autoregressive and possibly non-stationary. Asymptotic distributions are derived for the standard panel data estimators including ordinary least squares, fixed effects, first-difference, and generalized least squares (GLS) estimators when both T and n are large. We show that all the estimators have asymptotic normal distributions and have different convergence rates dependent on the non-stationarity of the regressors and the remainder disturbances. We show using Monte Carlo experiments that the loss in efficiency of the OLS, FE and FD estimators relative to true GLS can be substantial.
Book Synopsis An Adaptive Choice of the Scale Parameter for M-estimators by : Robert Michael Bell
Download or read book An Adaptive Choice of the Scale Parameter for M-estimators written by Robert Michael Bell and published by . This book was released on 1980 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: Let x sub 1 to x sub n be a random sample from a distribution symmetric about the unknown location parameter theta. A major class of robust estimators of location is the class of M-estimators, each of which corresponds to a function psi defined on the reals. To be scale equivariant, these estimators require the use of a scale equivariant function of the sample. Commonly, this scale parameter is chosen to be a constant times the sample MAD (medial absolute deviation from the median). For a given function psi, the variance of the corresponding M-estimator vaires considerably with the value of the scale parameter. It is therefore proposed that the value which minimizes an estimate of the asymptotic variance of the M-estimator be used as the scaling factor. This adaptive method of scaling is shown to be asymptotically optimal (under fairly general conditions), in the sense that the resulting M-estimator has the smallest possible asymptotic variance among all M-estimators based on psi. In particular, when the underlying distribution is normal, the adaptive estimator based on any reasonable psi achieves full asymptotic efficiency, i.e., is asymptotically equivalent to the sample mean. The performance of the estimator for small samples is investigated by Monte Carlo methods for several choices of psi using the triefficiency criteria. A slight modification of the above estimator compares favorably with Tukey's bisquare M-estimator for sample sizes as small as 20. (Author).
Book Synopsis Asymptotic Properties of Estimators in Non-linear Regression Models with Autoregressive Disturbance Terms by : Friedrich Schmid
Download or read book Asymptotic Properties of Estimators in Non-linear Regression Models with Autoregressive Disturbance Terms written by Friedrich Schmid and published by . This book was released on 1982 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Properties of Weighted M-estimators for Variable Probabulity Samples by : Jeffrey M. Wooldridge
Download or read book Asymptotic Properties of Weighted M-estimators for Variable Probabulity Samples written by Jeffrey M. Wooldridge and published by . This book was released on 1997 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic properties of estimators in non-linear regression models with autoregressive disturbance terms by : Friedrich Schmid
Download or read book Asymptotic properties of estimators in non-linear regression models with autoregressive disturbance terms written by Friedrich Schmid and published by . This book was released on 1982 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Contributions to the Theory of Robust Estimation by : Frank R. Hampel
Download or read book Contributions to the Theory of Robust Estimation written by Frank R. Hampel and published by . This book was released on 1968 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis M-Estimators in Linear Models with Long Range Dependent Errors by : Hira L. Koul
Download or read book M-Estimators in Linear Models with Long Range Dependent Errors written by Hira L. Koul and published by . This book was released on 1990 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: This note discusses the asymptotic behavior of a class of M-estimators in linear models which errors are Gaussian, or a function of Gaussian random variables, that are long range dependent. The asymptotics are discussed when the design variables are either i.i.d. or long range dependent, independent of the errors, or known constants. It is observed that the class of M-estimators of the regression parameter vector corresponding to skew symmetric scores and symmetric errors asymptotically behave like the least squares estimators. Moreover, in these cases, if the design variables are either i.i.d. or known constants then the limiting distributions are Normal. But if the design variables are also long range dependent then the limiting distributions are nonnormal. Keywords: Hermite rank and polynomials. (kr).
Book Synopsis Robust Adaptively Weighted Estimators for Regression Models by : Wei Tu
Download or read book Robust Adaptively Weighted Estimators for Regression Models written by Wei Tu and published by . This book was released on 2015 with total page 73 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis introduces a new class of robust estimators for regression models. Specifically, a class of weighted least square estimators under linear regression models is introduced in Chapter 2, with a continuous adaptive weight function computed using the Kolmogorov-Smirnov statistic. Asymptotic properties, such as consistency and asymptotic normality, of the proposed estimator are established under the model. Simulation studies show that the proposed estimator attains almost full efficiency and have a better robustness properties than the initial estimators for finite sample sizes. An application to a real contaminated dataset shows that it's comparable to other robust estimators in practice. In Chapter 3, a class of weighted maximum likelihood estimators under logistic regression models is introduced, again with a continuous adaptive weight function computed using Mahalanobis distances of exploratory variables. Asymptotic consistency of the proposed estimator is proved under the model, and finite-sample properties are also studied by simulation. In simulation studies, it is observed that the proposed estimator is almost as efficient as the maximum likelihood estimator under the model, and under point-mass contamination models, the proposed estimator shows a comparable robustness. This is also verified in an application to a real data set. Chapter 4 contains some concluding remarks and future directions.
