Asymptotic Properties of a Class of Robust M-estimators for Nonlinear Regression Models with Momentless Distributed Errors and Regressors PDF Download

Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Asymptotic Properties of a Class of Robust M-estimators for Nonlinear Regression Models with Momentless Distributed Errors and Regressors PDF full book. Access full book title Asymptotic Properties of a Class of Robust M-estimators for Nonlinear Regression Models with Momentless Distributed Errors and Regressors by Hermanus Josephus Bierens. Download full books in PDF and EPUB format.

Asymptotic Properties of a Class of Robust M-estimators for Nonlinear Regression Models with Momentless Distributed Errors and Regressors

Asymptotic Properties of a Class of Robust M-estimators for Nonlinear Regression Models with Momentless Distributed Errors and Regressors PDF Author: Hermanus Josephus Bierens
Publisher:
ISBN:
Category :
Languages : en
Pages : 58

Get Book Here

Book Description

Asymptotic Properties of a Class of Robust M-estimators for Nonlinear Regression Models with Momentless Distributed Errors and Regressors

Asymptotic Properties of a Class of Robust M-estimators for Nonlinear Regression Models with Momentless Distributed Errors and Regressors PDF Author: Hermanus Josephus Bierens
Publisher:
ISBN:
Category :
Languages : en
Pages : 58

Get Book Here

Book Description

Asymptotic Properties of Maximum Likelihood Estimators in the Nonlinear Regression Model when the Errors are Neither Independent for Identically Distributed

Asymptotic Properties of Maximum Likelihood Estimators in the Nonlinear Regression Model when the Errors are Neither Independent for Identically Distributed PDF Author: R. D. H. Heijmans
Publisher:
ISBN:
Category :
Languages : en
Pages : 84

Get Book Here

Book Description

Asymptotic Properties of Maximum Likelihood Estimators in the Nonlinear Regression Model when the Errors are Neither Independent Nor Identically Distributed

Asymptotic Properties of Maximum Likelihood Estimators in the Nonlinear Regression Model when the Errors are Neither Independent Nor Identically Distributed PDF Author: R. D. H. Heijmans
Publisher:
ISBN:
Category :
Languages : en
Pages : 84

Get Book Here

Book Description

Economics Working Papers

Economics Working Papers PDF Author: John Fletcher
Publisher:
ISBN:
Category : Economics
Languages : en
Pages : 640

Get Book Here

Book Description

Asymptotic Distributions of Some Scale Estimators in Nonlinear Models With Long Memory Errors Having Infinite Variance

Asymptotic Distributions of Some Scale Estimators in Nonlinear Models With Long Memory Errors Having Infinite Variance PDF Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :

Get Book Here

Book Description

Asymptotic Properties of S-estimators for Nonlinear Regression Models with Dependent, Heterogeneous Processes

Asymptotic Properties of S-estimators for Nonlinear Regression Models with Dependent, Heterogeneous Processes PDF Author: Shinichi Sakata
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 52

Get Book Here

Book Description

Asymptotic Properties of S Estimators for Nonlinear Regression Models with de

Asymptotic Properties of S Estimators for Nonlinear Regression Models with de PDF Author: Shinichi Sakata
Publisher:
ISBN:
Category :
Languages : en
Pages :

Get Book Here

Book Description

On the Non-Asymptotic Properties of Regularized M-Estimators

On the Non-Asymptotic Properties of Regularized M-Estimators PDF Author: Demian Pouzo
Publisher:
ISBN:
Category :
Languages : en
Pages : 75

Get Book Here

Book Description
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.

A Class of Partially Adaptive One-step M-estimators for the Nonlinear Regression Model

A Class of Partially Adaptive One-step M-estimators for the Nonlinear Regression Model PDF Author: Benedikt M. Potscher
Publisher:
ISBN:
Category :
Languages : en
Pages : 30

Get Book Here

Book Description

Asymptotic Properties of Estimators for the Linear Panel Regression Model with Individual Effects and Serially Correlated Errors

Asymptotic Properties of Estimators for the Linear Panel Regression Model with Individual Effects and Serially Correlated Errors PDF Author: Badi H. Baltagi
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

Get Book Here

Book Description
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.