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Statistical Learning for Big Dependent Data

large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data;

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Advanced Time Series Data Analysis

, and triple time-series, which are developed by taking into account specific growth patterns of each dependent variables, starting with the;

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Models for Dependent Time Series

Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time;

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Models for Dependent Time Series

Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time;

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Regression Models For Time Series Analysis

series analysis for over a century. Recently, new developments have made major strides in such areas as non-continuous data where a linear model;

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Oscillatory Models in General Relativity

Part II: Variational principle for time dependent oscillations and dissipations Lagrangian and Hamilton descriptions Damped oscillator;

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Time Series Modeling of Neuroscience Data

approach, in which dynamic models of all kinds, such as linear/nonlinear differential equation models and time series models, are used for;

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Models and Algorithms of Time-Dependent Scheduling

algorithms for solving time-dependent scheduling problems. The book is suitable for researchers working on scheduling, problem complexity;

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Models and Algorithms of Time-Dependent Scheduling

algorithms for solving time-dependent scheduling problems. The book is suitable for researchers working on scheduling, problem complexity;

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Robust Nonparametric Statistical Methods

section on rank procedures for nonlinear models A new chapter on models with dependent error structure, covering rank methods for mixed models;

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Long-range Dependence and Self-similarity

give rise to long-range dependence and self-similarity; central and non-central limit theorems for long-range dependent series, and the;

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Athens Conference on Applied Probability and Time Series Analysis

series analysis. Other papers discuss methods for finite parameter Gaussian models, time series with infinite variance or stable marginal;

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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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Regularity Theory for Mean-Field Game Systems

stationary and time-dependent MFGs through a series of a-priori estimates for solutions of the Hamilton-Jacobi and Fokker-Planck equation. It shows;

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Studies in Econometric Theory

articles and papers on areas such as limited dependent variables, non-linear simultaneous equations models, time series analysis and error;

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Dual Jet Geometrization for Time-Dependent Hamiltonians and Applications

the development of the distinguished geometrization on dual 1-jet spaces for time-dependent Hamiltonians, in contrast with the time;

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Introduction to Time Series Modeling

. Illustrating how to build models for time series using basic methods, Introduction to Time Series Modeling covers numerous time series models and the;

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A Course in Time Series Analysis

series models for univariate and multivariate time series data. It brings together material previously available only in the professional;

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Data Analytic Techniques for Dynamical Systems

Each volume in the Notre Dame Series on Quantitative Methodology features leading methodologists and substantive experts who provide;

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Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates

This monograph provides a concise point of research topics and reference for modeling correlated response data with time-dependent;

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Unobserved Components and Time Series Econometrics

of Andrew Harvey, the work covers three main topics: the theory and methodology for unobserved components time series models; applications;

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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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Financial Econometrics

This book provides an essential toolkit for all students wishing to know more about the modelling and analysis of financial data;

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Financial Econometrics

This book provides an essential toolkit for all students wishing to know more about the modelling and analysis of financial data;

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Time Series and Panel Data Econometrics

This book is concerned with recent developments in time series and panel data techniques for the analysis of macroeconomic and financial;

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Hidden Markov and Other Models for Discrete-Valued Time Series

Discrete-valued time series are common in practice, but methods for their analysis are not well-known. In recent years, methods have been;

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