Author: Jean-Marie DufourPublisher:
ISBN:
Category : Business enterprises
Languages : en
Pages : 0
Book Description
Asymptotic Distributions for Some Quasi-efficient Estimators in Echelon VARMA Models
Author: Jean-Marie DufourPublisher:
ISBN:
Category : Business enterprises
Languages : en
Pages : 0
Book Description
Asymptotic Distributions for Quasi-efficient Estimators in Echelon VARMA Models
Author: Jean-Marie DufourAsymptotic Distribution of a Simple Linear Estimator for VARMA Models in Echelon Form
Author: Jean-Marie DufourPublisher: Centre interuniversitaire de recherche en économie quantitative
ISBN: 9782893825045
Category : Business enterprises
Languages : en
Pages : 0
Book Description
Asymptotic Distribution of a Simple Linear Estimator for VARMA Models in Echelon Form
Author: Dufour, Jean-MariePublisher: Montréal : CIRANO
ISBN: 9782893825045
Category : Economics
Languages : en
Pages : 33
Book Description
Asymptotic Efficiency of Statistical Estimators: Concepts and Higher Order Asymptotic Efficiency
Author: Masafumi AkahiraPublisher: Springer
ISBN: 9781461259282
Category : Mathematics
Languages : en
Pages : 242
Book Description
This monograph is a collection of results recently obtained by the authors. Most of these have been published, while others are awaitlng publication. Our investigation has two main purposes. Firstly, we discuss higher order asymptotic efficiency of estimators in regular situa tions. In these situations it is known that the maximum likelihood estimator (MLE) is asymptotically efficient in some (not always specified) sense. However, there exists here a whole class of asymptotically efficient estimators which are thus asymptotically equivalent to the MLE. It is required to make finer distinctions among the estimators, by considering higher order terms in the expansions of their asymptotic distributions. Secondly, we discuss asymptotically efficient estimators in non regular situations. These are situations where the MLE or other estimators are not asymptotically normally distributed, or where l 2 their order of convergence (or consistency) is not n / , as in the regular cases. It is necessary to redefine the concept of asympto tic efficiency, together with the concept of the maximum order of consistency. Under the new definition as asymptotically efficient estimator may not always exist. We have not attempted to tell the whole story in a systematic way. The field of asymptotic theory in statistical estimation is relatively uncultivated. So, we have tried to focus attention on such aspects of our recent results which throw light on the area.
Asymptotic Efficient Robust Estimates in Some Semiparametric Models
Author: Colin Ou WuAsymptotic Efficiency and Some Quasi-method of Moments Estimators
Author: Robert R. ReadPublisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 0
Book Description
The report contains the asymptotic efficiencies of some candidate estimators which provide alternatives to maximum likelihood in some common probabilistic settings. The alternative estimators can be found with measurably less effort than solving the likelihood equations. They include the method of moments and similarly constructed estimators that involve the harmonic mean. The most successful example found deals with the negative binomial distribution. Here, the harmonic mean estimator has high efficiency in regions where the method of moments estimator has rather low efficiency. (Author).
The Asymptotic Distributions of Some Estimators for a Factor Analysis Model
Author: Y. AmemiyaAsymptotic Distributions of Some Scale Estimators in Nonlinear Models With Long Memory Errors Having Infinite Variance
Author: Asymptotic Properties of Some Estimators in Moving Average Models
Author: Stanford University. Department of StatisticsPublisher:
ISBN:
Category : Time-series analysis
Languages : en
Pages : 318
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.