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

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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

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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

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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 :

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Book Description

Asymptotic Properties of Estimators in Non-linear Regression Models with Autoregressive Disturbance Terms

Asymptotic Properties of Estimators in Non-linear Regression Models with Autoregressive Disturbance Terms PDF Author: Friedrich Schmid
Publisher:
ISBN:
Category :
Languages : en
Pages : 45

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Book Description

Asymptotic Properties of Nonlinear Least Squares Estimates in Stochastic Regression Models

Asymptotic Properties of Nonlinear Least Squares Estimates in Stochastic Regression Models PDF Author: Stanford University. Department of Statistics
Publisher:
ISBN:
Category :
Languages : en
Pages : 12

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Book Description

Asymptotic properties of estimators in non-linear regression models with autoregressive disturbance terms

Asymptotic properties of estimators in non-linear regression models with autoregressive disturbance terms PDF Author: Friedrich Schmid
Publisher:
ISBN:
Category :
Languages : de
Pages : 90

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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

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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

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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

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Book Description

Asymptotic Properties of Nonlinear Least Squares Estimators in a Replicated Time Series Model

Asymptotic Properties of Nonlinear Least Squares Estimators in a Replicated Time Series Model PDF Author: Jeremy Sin-hing Wu
Publisher:
ISBN:
Category :
Languages : en
Pages : 246

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Book Description

Asymptotic Properties of Some Estimators in Moving Average Models

Asymptotic Properties of Some Estimators in Moving Average Models PDF Author: Stanford University. Department of Statistics
Publisher:
ISBN:
Category : Time-series analysis
Languages : en
Pages : 318

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Book Description
The author considers estimation procedures for the moving average model of order q. Walker's method uses k sample autocovariances (k> or = q). Assume that k depends on T in such a way that k nears infinity as T nears infinity. The estimates are consistent, asymptotically normal and asymptotically efficient if k = k (T) dominates log T and is dominated by (T sub 1/2). The approach in proving these theorems involves obtaining an explicit form for the components of the inverse of a symmetric matrix with equal elements along its five central diagonals, and zeroes elsewhere. The asymptotic normality follows from a central limit theorem for normalized sums of random variables that are dependent of order k, where k tends to infinity with T. An alternative form of the estimator facilitates the calculations and the analysis of the role of k, without changing the asymptotic properties.