Author: Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Asymptotic Distributions of Some Scale Estimators in Nonlinear Models With Long Memory Errors Having Infinite Variance
Asymptotic Distribution of the Bias Corrected Least Squares Estimators in Measurement Error Linear Regression Models Under Long Memory
Author: Hira KoulPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
This article derives the consistency and asymptotic distribution of the bias corrected least squares estimators (LSEs) of the regression parameters in linear regression models when covariates have measurement error (ME) and errors and covariates form mutually independent long memory moving average processes. In the structural ME linear regression model, the nature of the asymptotic distribution of suitably standardized bias corrected LSEs depends on the range of the values of where ,, and are the LM parameters of the covariate, ME and regression error processes respectively. This limiting distribution is Gaussian when and non-Gaussian in the case . In the former case some consistent estimators of the asymptotic variances of these estimators and a log()-consistent estimator of an underlying LM parameter are also provided. They are useful in the construction of the large sample confidence intervals for regression parameters. The article also discusses the asymptotic distribution of these estimators in some functional ME linear regression models, where the unobservable covariate is non-random. In these models, the limiting distribution of the bias corrected LSEs is always a Gaussian distribution determined by the range of the values of )-)
Asymptotic Distributions for Some Quasi-efficient Estimators in Echelon VARMA Models
Author: Jean-Marie DufourAsymptotic 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.
The Asymptotic Theory of L1-norm Estimation of a Unit Root
Author: Miguel Angel HercePublisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 392
Book Description
On the Asymptotic Distribution of the Likelihood Ratio in Some Problems on Mixed Variate Populations
Author: JunjirÅ OgawaPublisher:
ISBN:
Category : Asymptotic distribution (Probability theory)
Languages : en
Pages : 74
Book Description
Asymptotic Results in Non-regular Estimation
Author: Thomas PolfeldtPublisher:
ISBN:
Category : Asymptotic expansions
Languages : en
Pages : 86
Book Description
The Asymptotic Distributions of Some Estimators for a Factor Analysis Model
Author: Y. AmemiyaAsymptotic Properties for Estimators Derived from the Kaplan-Meier Estimator
Author: Joan M. SanderPublisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 150
Book Description
Asymptotic Efficiency of Statistical Estimators: Concepts and Higher Order Asymptotic Efficiency
Author: Masafumi AkahiraPublisher: Springer Science & Business Media
ISBN: 1461259274
Category : Mathematics
Languages : en
Pages : 253
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.