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Non-Gaussian Autoregressive-Type Time Series

This book brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data. This book collects and collates;

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Gaussian and Non-Gaussian Linear Time Series and Random Fields

questions also being discussed. The book contrasts Gaussian models with noncausal or noninvertible (nonminimum phase) non-Gaussian models and deals;

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Diagnostic Checks in Time Series

ARMA, threshold type, and bilinear models to conditional non-Gaussian and autoregressive heteroscedasticity (ARCH) models. Because of its broad;

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Time Series Algorithms Recipes

This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem;

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Time Series Econometrics

of autoregressive-moving average (ARMA) models and their relations to the covariance structure. The book then moves on to non-stationary time series;

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Stochastic Models for Time Series

This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools;

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Introduction to Time Series Modeling with Applications in R

the state-space model as a generic tool for time series modeling and presents the Kalman filter, the non-Gaussian filter and the particle;

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Statistical Modeling Using Local Gaussian Approximation

most well-known and most used distribution in statistics, to a large class of non-Gaussian and nonlinear situations through local approximation;

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Non-Gaussian Random Vibration Fatigue Analysis and Accelerated Test

This book discusses the theory, method and application of non-Gaussian random vibration fatigue analysis and test. The main contents;

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Asymptotic Properties of Permanental Sequences

sequences related to birth and death processes and autoregressive Gaussian sequences using techniques from the theory of Gaussian processes and;

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Time Series Econometrics Analysis

This text book presents the application of time series econometrics methods in economics. The main focus of this book is to describe 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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Random Fields for Spatial Data Modeling

. Other chapters focus on non-Gaussian random fields and stochastic simulation methods. The book also presents results based on the author's;

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Estimation in Conditionally Heteroscedastic Time Series Models

In his seminal 1982 paper, Robert F. Engle described a time series model with a time-varying volatility. Engle showed that this model;

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Stable Non-Gaussian Random Processes

This book presents similarity between Gaussian and non-Gaussian stable multivariate distributions and introduces the one-dimensional stable;

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Time Series Analysis in Climatology and Related Sciences

This book gives the reader the basic knowledge of the theory of random processes necessary for applying to study climatic time series. It;

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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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Time Series

estimates them equally, thereby handling non-Gaussian series and nonlinear systems directly. The included proofs, which are generally short, are;

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Time Series Analy By State Space Meth

. Additions to this second edition include the filtering of nonlinear and non-Gaussian series. Part I of the book obtains the mean and variance;

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Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

modern non-parametric models. By observing practical examples and the theory behind them, you will become confident with loading time-series;

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Some Random Series of Functions

Hilbert spaces, random Taylor or Fourier series, Brownian motion and other Gaussian processes, plus certain types of random sets and measures. The;

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Probability

limiting theorems, Bayesian computational methods, as well as a modern view on sampling and reconstruction of Gaussian and non-Gaussian random;

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ARCH Models and Financial Applications

1.1 The DevelopmentofARCH Models Time series models have been initially introduced either for descriptive purposes like prediction and;

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Macro-econometric Analysis on Determinants of Fertility Behavior

The book comprises three chapters, with each chapter assigned various type data such as time series data, cross sectional data and panel;

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Applied Economic Forecasting using Time Series Methods

-focusing on macroeconomic and financial topics. This is first and foremost a book aimed at applying time series methods to solve real-world;

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VaR Methodology for Non-Gaussian Finance

assumptions leading to what is called non-Gaussian finance. With the growing importance of Solvency II, Basel II and III regulatory rules for;

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