Download or read book Asymptotic Properties of Parameter Estimates for Strongly Dependent Random Variables written by Robert William Fox and published by . This book was released on 1983 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Valid Asymptotic Expansions for the Maximum Likelihood Estimator of the Parameter of a Stationary Gaussian Strongly Dependent Process written by Offer Lieberman and published by . This book was released on 2002 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Asymptotic Theory of Statistical Inference for Time Series written by Masanobu Taniguchi and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 671 pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary aim of this book is to provide modern statistical techniques and theory for stochastic processes. The stochastic processes mentioned here are not restricted to the usual AR, MA, and ARMA processes. A wide variety of stochastic processes, including non-Gaussian linear processes, long-memory processes, nonlinear processes, non-ergodic processes and diffusion processes are described. The authors discuss estimation and testing theory and many other relevant statistical methods and techniques.

Download or read book Asymptotic Properties of Recursive M estimators in an Infinite dimensional Hilbert Space written by Xiaohong Chen and published by . This book was released on 1993 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Sciences and Data Analysis written by Kameo Matusita and published by Walter de Gruyter GmbH & Co KG. This book was released on 2020-05-18 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: No detailed description available for "Statistical Sciences and Data Analysis".

Download or read book Dependence in Probability and Statistics written by Patrice Bertail and published by Springer Science & Business Media. This book was released on 2006-09-24 with total page 491 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives an account of recent developments in the field of probability and statistics for dependent data. It covers a wide range of topics from Markov chain theory and weak dependence with an emphasis on some recent developments on dynamical systems, to strong dependence in times series and random fields. There is a section on statistical estimation problems and specific applications. The book is written as a succession of papers by field specialists, alternating general surveys, mostly at a level accessible to graduate students in probability and statistics, and more general research papers mainly suitable to researchers in the field.

Download or read book Asymptotic Properties and Computation of Maximum Likelihood Estimates in the Mixed Model of the Analysis of Variance written by Stanford University. Department of Statistics and published by . This book was released on 1973 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem considered is the estimation of the parameters in the mixed model of the analysis of variance, assuming normality of the random effects and errors. Both asymptotic properties of such estimates as the size of the design increases and numerical procedures for their calculation are discussed. Estimation is carried out by the method of maximum likelihood. It is shown that there is a sequence of roots of the likelihood equations which is consistent, asymptotically normal and asymptotically efficient in the sense of attaining the Cramer-Rao lower bound for the covariance matrix as the size of the design increases. This is accomplished using a Taylor series expansion of the log-likelihood. (Modified author abstract).

Download or read book Asymptotic Properties of Dynamic Stochastic Parameter Estimates I written by Bernt P. Stigum and published by . This book was released on 1972 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Weak Dependence With Examples and Applications written by Jérome Dedecker and published by Springer Science & Business Media. This book was released on 2007-07-29 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops Doukhan/Louhichi's 1999 idea to measure asymptotic independence of a random process. The authors, who helped develop this theory, propose examples of models fitting such conditions: stable Markov chains, dynamical systems or more complicated models, nonlinear, non-Markovian, and heteroskedastic models with infinite memory. Applications are still needed to develop a method of analysis for nonlinear times series, and this book provides a strong basis for additional studies.

Download or read book Asymptotic Properties of Dynamic Stochastic Parameter Estimates 3 written by B. P. Stigum and published by . This book was released on 1973 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book On Some Asymptotic Properties of Maximum Likelihood Estimates and Related Bayes Estimates written by Lucien Marie Le Cam and published by . This book was released on 1953 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dissertation Abstracts International written by and published by . This book was released on 2008 with total page 980 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book On the Asymptotic Properties of Parameter Estimates in a Regression Model with Non Normally Distributed Errors written by John L. Maryak and published by . This book was released on 1987 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: The usual assumption of normality for the error terms of a regression model is often untenable. When this assumption is dropped, it may be difficult to characterize parameter estimates for the model. For example, it is stated that if the regression errors are non-normal, one is not even sure of their (e.g., the generalized least squares parameter estimates') asymptotic properties. A partial answer presents an asymptotic distribution theory for Kalman filter estimates for cases where the random terms of the state space model are not necessarily Gaussian. Certain of these asymptotic distribution results are also discussed in the context of model validation (diagnostic checking). Keywords: Random coefficient regression, State-space model, Non-Gaussian, Kalman filters, Reprints. (JHD).

Download or read book Asymptotic Properties of Dynamic Stochastic Parameter Estimates 1 written by B. P. Stigum and published by . This book was released on 1972 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Asymptotic Analysis for Nonlinear Spatial and Network Econometric Models written by Xingbai Xu and published by . This book was released on 2016 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatial econometrics has been obtained more and more attention in the recent years. The spatial autoregressive (SAR) model is one of the most widely used and studied models in spatial econometrics. So far, most studies have been focused on linear SAR models. However, some types of spatial or network data, for example, censored data or discrete choice data, are very common and useful, but not suitable to study by a linear SAR model. That is why I study an SAR Tobit model and an SAR binary choice model in this dissertation. Chapter 1 studies a Tobit model with spatial autoregressive interactions. We consider the maximum likelihood estimation (MLE) for this model and analyze asymptotic properties of the estimator based on the spatial near-epoch dependence (NED) of the dependent variable process generated from the model structure. We show that the MLE is consistent and asymptotically normally distributed. Monte Carlo experiments are performed to verify finite sample properties of the estimator. Chapter 2 extends the MLE estimation of the SAR Tobit model studied in Chapter 1 to distribution-free estimation. We examine the sieve MLE of the model, where the disturbances are i.i.d. with an unknown distribution. This model can be applied to spatial econometrics and social networks when data are censored. We show that related variables are spatial NED. An important contribution of this chapter is that I develop some exponential inequalities for spatial NED random fields, which are also useful in other semiparametric studies when spatial correlation exists. With these inequalities, we establish the consistency of the estimator. Asymptotic distributions of structural parameters of the model are derived from a functional central limit theorem and projection. Simulations show that the sieve MLE can improve the finite sample performance upon misspecified normal MLEs, in terms of reduction in the bias and standard deviation. As an empirical application, we examine the school district income surtax rates in Iowa. Our results show that the spatial spillover effects are significant, but they may be overestimated if disturbances are restricted to be normally distributed. Chapter 3 studies the method of simulated moments (MSM) estimation of a binary choice game model with network links, where the network peer effects are non-negative, and there might be only one or few networks in the sample. The proposed estimation method can be applied to studies with binary dependent variables in the fields of empirical IO, social network and spatial econometrics. The model might have multiple Nash equilibria. We assume that the maximum Nash equilibrium, which always exists and is strongly coalition-proof and Pareto optimal, is selected. The challenging econometric issues are the possible correlation among all dependent variables and the discontinuous functional form of our simulated moments. We overcome these challenges via the empirical process theory and derive the spatial NED of the dependent variable. We establish a criterion for an NED random field to be stochastically equicontinuous and we apply it to develop the consistency and asymptotic normality of the estimator. We examine computational issues and finite sample properties of the MSM by some Monte Carlo experiments.

Download or read book Statistical Inference on Linear and Partly Linear Regression with Spatial Dependence written by Supachoke Thawornkaiwong and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The typical assumption made in regression analysis with cross-sectional data is that of independent observations. However, this assumption can be questionable in some economic applications where spatial dependence of observations may arise, for example, from local shocks in an economy, interaction among economic agents and spillovers. The main focus of this thesis is on regression models under three di§erent models of spatial dependence. First, a multivariate linear regression model with the disturbances following the Spatial Autoregressive process is considered. It is shown that the Gaussian pseudo-maximum likelihood estimate of the regression and the spatial autoregressive parameters can be root-n-consistent under strong spatial dependence or explosive variances, given that they are not too strong, without making restrictive assumptions on the parameter space. To achieve e¢ ciency improvement, adaptive estimation, in the sense of Stein (1956), is also discussed where the unknown score function is nonparametrically estimated by power series estimation. A large section is devoted to an extension of power series estimation for random variables with unbounded supports. Second, linear and semiparametric partly linear regression models with the disturbances following a generalized linear process for triangular arrays proposed by Robinson (2011) are considered. It is shown that instrumental variables estimates of the unknown slope parameters can be root-n-consistent even under some strong spatial dependence. A simple nonparametric estimate of the asymptotic variance matrix of the slope parameters is proposed. An empirical illustration of the estimation technique is also conducted. Finally, linear regression where the random variables follow a marked point process is considered. The focus is on a family of random signed measures, constructed from the marked point process, that are second-order stationary and their spectral properties are discussed. Asymptotic normality of the least squares estimate of the regression parameters are derived from the associated random signed measures under mixing assumptions. Nonparametric estimation of the asymptotic variance matrix of the slope parameters is discussed where an algorithm to obtain a positive deÖnite estimate, with faster rates of convergence than the traditional ones, is proposed.

Download or read book Strong Approximations in Probability and Statistics written by M. Csörgo and published by Academic Press. This book was released on 2014-07-10 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: Strong Approximations in Probability and Statistics presents strong invariance type results for partial sums and empirical processes of independent and identically distributed random variables (IIDRV). This seven-chapter text emphasizes the applicability of strong approximation methodology to a variety of problems of probability and statistics. Chapter 1 evaluates the theorems for Wiener and Gaussian processes that can be extended to partial sums and empirical processes of IIDRV through strong approximation methods, while Chapter 2 addresses the problem of best possible strong approximations of partial sums of IIDRV by a Wiener process. Chapters 3 and 4 contain theorems concerning the one-time parameter Wiener process and strong approximation for the empirical and quantile processes based on IIDRV. Chapter 5 demonstrate the validity of previously discussed theorems, including Brownian bridges and Kiefer process, for empirical and quantile processes. Chapter 6 illustrate the approximation of defined sequences of empirical density, regression, and characteristic functions by appropriate Gaussian processes. Chapter 7 deal with the application of strong approximation methodology to study weak and strong convergence properties of random size partial sum and empirical processes. This book will prove useful to mathematicians and advance mathematics students.