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Estimation of Spatial Panels

well as dynamic models. Estimation of Spatial Panels provides some recent developments on the specification and estimation of such models. The;

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Introduction to Time Series Modeling

, prediction, and signal extraction of times series, this book provides basic tools for analyzing time series that arise in real-world problems. It;

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Short-Memory Linear Processes and Econometric Applications

(CLTs) for weighted sums of short memory linear processes. This part contains the analysis of certain operators in Lp spaces and their;

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Hidden Markov Models for Time Series

discusses how to employ the freely available computing environment R to carry out computations for parameter estimation, model selection and;

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Statistical Methods for Spatial Data Analysis

Understanding spatial statistics requires tools from applied and mathematical statistics, linear model theory, regression, time series, and;

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Statistical Methods for Spatial Data Analysis

Understanding spatial statistics requires tools from applied and mathematical statistics, linear model theory, regression, time series, and;

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Regression Models For Time Series Analysis

concerning state space models. To conclude, they present a Bayesian approach to prediction and interpolation in spatial data adapted to time series;

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Spatial Statistics and Spatio-Temporal Data

In the spatial or spatio-temporal context, specifying the correct covariance function is fundamental to obtain efficient predictions, and;

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Empirical Likelihood and Quantile Methods for Time Series

estimation motivated by minimizing quantile prediction error without assumption of true model. It provides the reader with a new horizon for;

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Random Fields for Spatial Data Modeling

significant computational gains for the processing of big spatial data are presented and discussed. The final chapter concludes with a description;

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Nonlinear Time Series

methods in model estimation, specification testing, and selection of time series data. After a brief introduction, the book examines;

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Nonlinear Time Series

methods in model estimation, specification testing, and selection of time series data. After a brief introduction, the book examines;

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Adaptive Filtering Prediction and Control

estimation, and adaptive prediction and control. The second part examines stochastic systems, exploring optimal filtering and prediction, parameter;

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Hidden Markov Models for Time Series

presenting the basic model formulation, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference for;

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Hidden Markov Models for Time Series

the basic model formulation, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference for HMMs;

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Spatial Analysis for the Social Sciences

spatial models for time-series cross-sectional data, categorical and limited dependent variables, count data, and survival data.;

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Spatial Analysis for the Social Sciences

spatial models for time-series cross-sectional data, categorical and limited dependent variables, count data, and survival data.;

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Gaussian and Non-Gaussian Linear Time Series and Random Fields

with problems of prediction and estimation. New results for nonminimum phase non-Gaussian processes are exposited and open questions are noted;

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Time Series Models

another level from the first book, focusing on the estimation, testing, and specification of both univariate and multivariate time series models;

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Engineering Tools for Corrosion

estimation of main corrosion parameters for corrosion rate, electrochemical constraints, thresholds limits and initiation time. The algorithms;

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Modelling Nonlinear Economic Time Series

estimating parametric nonlinear models and another on estimating nonparametric ones. Forecasting is a major reason for building time series;

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Statistical Factor Analysis And Related Methods

of data including time series, spatial data, rank orders, and nominal variable* Applications of factor models to the estimation of functional;

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Inference and Prediction in Large Dimensions

prediction of continuous time processes. This work is in the Wiley-Dunod Series co-published between Dunod ( www.dunod.com ) and John Wiley and Sons;

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Mapping Species Distributions

distribution modeling (also called niche modeling) and presents a framework for spatial prediction of species distributions based on the attributes;

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Advances in Spatial Econometrics

World-renowned experts in spatial statistics and spatial econometrics present the latest advances in specification and estimation;

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