Author: Jeremy Sin-hing WuPublisher:
ISBN:
Category :
Languages : en
Pages : 246
Book Description
Asymptotic Properties of Nonlinear Least Squares Estimators in a Replicated Time Series Model
Author: Jeremy Sin-hing WuAsymptotics, Nonparametrics, and Time Series
Author: Subir GhoshPublisher: CRC Press
ISBN: 9780824700515
Category : Mathematics
Languages : en
Pages : 864
Book Description
"Contains over 2500 equations and exhaustively covers not only nonparametrics but also parametric, semiparametric, frequentist, Bayesian, bootstrap, adaptive, univariate, and multivariate statistical methods, as well as practical uses of Markov chain models."
Asymptotic Properties of Nonlinear Least Squares Estimates in Stochastic Regression Models
Author: Stanford University. Department of StatisticsPublisher:
ISBN:
Category :
Languages : en
Pages : 12
Book Description
Asymptotic Properties of Conditional Least-squares Estimators for Array Time Series
Author: Rajae AzralNBS Special Publication
Author: Publisher:
ISBN:
Category : Weights and measures
Languages : en
Pages : 574
Book Description
Nonlinear Detection and Estimation for Directional Stochastic Signals Observed on Arrays
Author: Sung-Eun KimAsymptotic Properties of Sample Autocorrelations, Least Squares Estimators and Predictors of Non-stationary Multivariate Time Series
Author: Vanniarachchige Amarasiri SamaranayakePublisher:
ISBN:
Category : Autocorrelation (Statistics)
Languages : en
Pages : 200
Book Description
On Asymptotic Properties of the Least Squares Estimators for Autoregressive Time Series with a Unit Root
Author: Sastry G. PantulaPublisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 42
Book Description
An Asymptotic Theory for Weighted Least Squares with Weights Estimated by Replication
Author: Raymond J. CarrollPublisher:
ISBN:
Category :
Languages : en
Pages : 19
Book Description
This document considers a heteroscedastic linear regression model with replication. To estimate the variances, one can use the sample variances or the sample average squared errors from a regression fit. The authors study the large sample properties of these weighted least squares estimates with estimated weights when the number of replicates is small. The estimates are generally inconsistent for asymmetrically distributed data. If sample variances are used based on m replicates, the weighted least squares estimates are inconsistent for m=2 replicates even when the data are normally distributed. With between 3 and 5 replicates, the rates of convergence are slower than the usual square root of N. With m> or = 6 replicates, the effect of estimating the weights is to increase variances by (m-5)/(m-3), relative to weighted least squares estimates with known weights. (KR).
Author: Enrico Gori