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Large Sample Inference For Long Memory Processes

procedures for long memory processes. This text makes an attempt in this direction. The authors provide in a concise style a text at the graduate;

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Statistical Inference for Financial Engineering

processes, non-linear processes, long-memory processes, locally stationary processes etc. are introduced and their optimal estimation is considered;

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Semimartingales and their Statistical Inference

of stochastic processes, including diffusion type processes, point processes, and diffusion type processes with jumps, widely used for stochastic;

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Semimartingales and Their Statistical Inference

of stochastic processes, including diffusion type processes, point processes, and diffusion type processes with jumps, widely used for stochastic;

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Statistical Inference for Fractional Diffusion Processes

Stochastic processes are widely used for model building in the social, physical, engineering and life sciences as well as in financial;

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Introduction To Statistical Time Series

, long memory processes, and the Kalman filter. Major topics include: Moving average and autoregressive processes Introduction to Fourier;

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Time Series Analysis with Long Memory in View

, Time Series Analysis with Long Memory in View offers chapters that cover: Stationary Processes; Moving Averages and Linear Processes; Frequency;

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Long Memory in Economics

pedagogical way some statistical methods for studying long-range dependent processes. Furthermore, the occurrence of long-memory in economic time;

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From Finite Sample To Asymptotic Methods In Statistics

justification for the use of asymptotic methods for large samples. Methodological results are developed on a concrete and yet rigorous mathematical;

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Statistical Inference for Diffusion Type Processes

processes. Diffusion type processes are a large class of continuous time processes which are widely used for stochastic modelling. the book aims to;

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Asymptotic Statistical Inference

Econometrics. It will also provide sufficient background information for studying inference in stochastic processes. The book will cater to the need;

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Asymptotic Statistical Inference

Econometrics. It will also provide sufficient background information for studying inference in stochastic processes. The book will cater to the need;

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Fundamentals of Nonparametric Bayesian Inference

Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly;

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Theory of Statistical Inference

Theory of Statistical Inference is designed as a reference on statistical inference for researchers and students at the graduate or;

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Statistical Inference in Science

A treatment of the problems of inference associated with experiments in science, with the emphasis on techniques for dividing the sample;

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Statistical Inference in Science

A treatment of the problems of inference associated with experiments in science, with the emphasis on techniques for dividing the sample;

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Statistics for Long-Memory Processes

Statistical Methods for Long Term Memory Processes covers the diverse statistical methods and applications for data with long-range;

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Time Series Analysis of Long Memory versus Structural Breaks

for long range dependence, a chapter with applications compares short and long memory models for financial data. The contribution of this work;

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Mathematical Statistics

uniquely based on the likelihood function This book addresses mathematical statistics for upper-undergraduates and first year graduate students;

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Self-Normalized Processes: Limit Theory and Statistical Applications

processes are approximate pivots for statistical inference. The present volume covers recent developments in the area, including self;

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Economic Dynamics with Memory

equations in describing economic dynamics with long memory. Generalizations of basic economic concepts, notions and methods for the economic;

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Parametric Inference: An Introduction

introduced. The tests of hypotheses for finite samples with classical Neyman - Pearson theory is developed pointing out its connection with Bayesian;

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Introduction to Probability Theory and Statistical Inference

descriptive statistics. Cramer-Rao bounds for variance of estimators, two-sample inference procedures, bivariate normal probability law, F;

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Statistical Inference

Intended as a text for the postgraduate students of statistics, this well-written book gives a complete coverage of Estimation theory and;

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Applied Statistical Science II

Contents: Nonparametric Perspectives in Regression Analysis; Record Values of Univariate Distribution; A Transfer Theorem for Multitype;

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Foundations of Social Cognition

analysis of the underlying processes that have long been the focus of Bob Wyer's own research, including attention, perception, inference, and;

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Parameter Estimation in Stochastic Volatility Models

well as nontraditional continuous time stochastic volatility models driven by fractional Levy processes. By incorporating jumps and long memory;

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