Asymptotic Theory for Sieve Estimators in Semimartingale Regression Models PDF Download
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Author: Ian W. McKeague
Publisher:
ISBN:
Category :
Languages : en
Pages : 35
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Book Description
This paper studies the estimation of functions alpha sub 1 ..., alpha sub p describing the temporal influence of p covariate processes in a regression model for semi-martingales. McKeague (1986) introduced sieve estimators for alpha sub 1 ..., alpha sub p and established consistency in sq l-norm. In this paper the asymptotic distribution theory for the integrated sieve estimators is developed. Smoothed sieve estimators are shown to be pointwise consistent and rates of convergence are provided. (Author).
Author: Ian W. McKeague
Publisher:
ISBN:
Category :
Languages : en
Pages : 35
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Book Description
This paper studies the estimation of functions alpha sub 1 ..., alpha sub p describing the temporal influence of p covariate processes in a regression model for semi-martingales. McKeague (1986) introduced sieve estimators for alpha sub 1 ..., alpha sub p and established consistency in sq l-norm. In this paper the asymptotic distribution theory for the integrated sieve estimators is developed. Smoothed sieve estimators are shown to be pointwise consistent and rates of convergence are provided. (Author).
Author: B.L.S. Prakasa Rao
Publisher: Routledge
ISBN: 1351416936
Category : Mathematics
Languages : en
Pages : 599
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Book Description
Statistical inference carries great significance in model building from both the theoretical and the applications points of view. Its applications to engineering and economic systems, financial economics, and the biological and medical sciences have made statistical inference for stochastic processes a well-recognized and important branch of statistics and probability. The class of semimartingales includes a large class of stochastic processes, including diffusion type processes, point processes, and diffusion type processes with jumps, widely used for stochastic modeling. Until now, however, researchers have had no single reference that collected the research conducted on the asymptotic theory for semimartingales. Semimartingales and their Statistical Inference, fills this need by presenting a comprehensive discussion of the asymptotic theory of semimartingales at a level needed for researchers working in the area of statistical inference for stochastic processes. The author brings together into one volume the state-of-the-art in the inferential aspect for such processes. The topics discussed include: Asymptotic likelihood theory Quasi-likelihood Likelihood and efficiency Inference for counting processes Inference for semimartingale regression models The author addresses a number of stochastic modeling applications from engineering, economic systems, financial economics, and medical sciences. He also includes some of the new and challenging statistical and probabilistic problems facing today's active researchers working in the area of inference for stochastic processes.
Author: Norbert Christopeit
Publisher:
ISBN:
Category :
Languages : de
Pages : 14
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Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 952
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Author: T. L. Lai
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 560
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Book Description
Presents a collection of 18 papers, many of which are surveys, on asymptotic theory in probability and statistics, with applications to a variety of problems. This volume comprises three parts: limit theorems, statistics and applications, and mathematical finance and insurance. It is suitable for graduate students in probability and statistics.
Author: I. W. McKeague
Publisher:
ISBN:
Category :
Languages : en
Pages : 17
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Book Description
Estimation by the method of sieves for a semimartingale regression model introduced by Aalen is studied. It is of interest to estimate functions which describe the influence of the covariates over time. An estimator for these functions is introduced and conditions which ensure consistency of the estimator in l sub 2 norm are given. Applications to diffusion processes and point processes with censored data are also discussed. (Author).
Author: A. V. Ivanov
Publisher: Springer
ISBN:
Category : Language Arts & Disciplines
Languages : en
Pages : 344
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Book Description
This book presents up-to-date mathematical results in asymptotic theory on nonlinear regression on the basis of various asymptotic expansions of least squares, its characteristics, and its distribution functions of functionals of Least Squares Estimator. It is divided into four chapters. In Chapter 1 assertions on the probability of large deviation of normal Least Squares Estimator of regression function parameters are made. Chapter 2 indicates conditions for Least Moduli Estimator asymptotic normality. An asymptotic expansion of Least Squares Estimator as well as its distribution function are obtained and two initial terms of these asymptotic expansions are calculated. Separately, the Berry-Esseen inequality for Least Squares Estimator distribution is deduced. In the third chapter asymptotic expansions related to functionals of Least Squares Estimator are dealt with. Lastly, Chapter 4 offers a comparison of the powers of statistical tests based on Least Squares Estimators. The Appendix gives an overview of subsidiary facts and a list of principal notations. Additional background information, grouped per chapter, is presented in the Commentary section. The volume concludes with an extensive Bibliography. Audience: This book will be of interest to mathematicians and statisticians whose work involves stochastic analysis, probability theory, mathematics of engineering, mathematical modelling, systems theory or cybernetics.
Author: Michael Mohr
Publisher:
ISBN:
Category :
Languages : de
Pages : 107
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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.
Author: Madan L. Puri
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110942003
Category : Mathematics
Languages : en
Pages : 456
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Book Description
No detailed description available for "Asymptotics in Statistics and Probability".
