✦ LIBER ✦

Asymptotic Normality for Deconvolution Estimators of Multivariate Densities of Stationary Processes

✍ Scribed by E. Masry

Publisher: Elsevier Science
Year: 1993
Tongue: English
Weight: 580 KB
Volume: 44
Category: Article
ISSN: 0047-259X
DOI: 10.1006/jmva.1993.1003

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Marked Gibbs Processes and Asymptotic No

Marked Gibbs Processes and Asymptotic Normality of Maximum Pseudo-Likelihood Estimators

✍ Shigeru Mase 📂 Article 📅 2000 🏛 John Wiley and Sons 🌐 English ⚖ 248 KB

Asymptotic Normality of Parameter Estima

Asymptotic Normality of Parameter Estimators for Some Nearest Neighbour Models

✍ A. Dobek 📂 Article 📅 1994 🏛 John Wiley and Sons 🌐 English ⚖ 238 KB 👁 1 views

## Department of Mathematical and Statistical Methods, Academy of Agriculture, P o z n l Summory For auto-regression models and first and second difierences models it is shown that the REML estimators of unknown parameters are asymptotically normal when the number of observations tends to infinity

Asymptotic Properties of the Estimators

Asymptotic Properties of the Estimators for Multivariate Components of Variance

✍ S. Remadi; Y. Amemiya 📂 Article 📅 1994 🏛 Elsevier Science 🌐 English ⚖ 736 KB

Estimation of the covariance matrices in the multivariate balanced one-way random effect model is discussed. The rank of the between-group covariance matrix plays a large role in model building as well as in assessing asymptotic properties of the estimated covariance matrices. The restricted (residu

Estimation of the Asymptotic Variance of

Estimation of the Asymptotic Variance of Kernel Density Estimators for Continuous Time Processes

✍ Armelle Guillou; Florence Merlevède 📂 Article 📅 2001 🏛 Elsevier Science 🌐 English ⚖ 196 KB

In order to construct confidence sets for a marginal density f of a strictly stationary continuous time process observed over the time interval [0, T ], it is necessary to have at one's disposal a Central Limit Theorem for the kernel density estimator f T . In this paper we address the question of n

Optimality Properties of Empirical Estim

Optimality Properties of Empirical Estimators for Multivariate Point Processes

✍ P.E. Greenwood; W. Wefelmeyer 📂 Article 📅 1994 🏛 Elsevier Science 🌐 English ⚖ 531 KB

A multivariate point process is a random jump measure in time and space. Its distribution is determined by the compensator of the jump measure. By an empirical estimator we understand a linear functional of the jump measure. We give conditions for a nonparametric version of local asymptotic normalit

Asymptotic Properties of Serial Covarian

Asymptotic Properties of Serial Covariances for Nonlinear Stationary Processes

✍ K.C Chanda 📂 Article 📅 1993 🏛 Elsevier Science 🌐 English ⚖ 266 KB

Let \(\left\{X_{t} ; t \in \mathbb{Z}\right\}\) be a strictly stationary process with mean zero and autovariance function (a.c.v.f.) \(\gamma_{x}, v \in \mathbb{Z}\). Let \(\hat{\gamma}_{v}=n^{-1} \sum_{t=1}^{n-\mid v_{i}} X_{1} X_{r+|x|}\) be the serial covariance of order \(v\) computed from a sam