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Empirical Likelihood and Quantile Methods for Time Series

error. This is the first book to consider the generalized empirical likelihood applied to time series models in frequency domain and also the;

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Statistical Inference for Financial Engineering

This monograph provides the fundamentals of statistical inference for financial engineering and covers some selected methods suitable for;

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Time Series with Mixed Spectra

with Mixed Spectra focuses on the methods and theory for the statistical analysis of time series with mixed spectra. It presents detailed;

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Time Series with Mixed Spectra

with Mixed Spectra focuses on the methods and theory for the statistical analysis of time series with mixed spectra. It presents detailed;

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Inference for Heavy-Tailed Data

, empirical likelihood based interval estimation for tail index and high quantiles, hypothesis tests for heavy tails, the choice of sample fraction;

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Economic Applications of Quantile Regression

Quantile regression has emerged as an essential statistical tool of contemporary empirical economics and biostatistics. Complementing;

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Economic Applications of Quantile Regression

Quantile regression has emerged as an essential statistical tool of contemporary empirical economics and biostatistics. Complementing;

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Quantile Regression

Quantile Regression, the first book of Hao and Naiman's two-book series, establishes the seldom recognized link between inequality studies;

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Empirical Likelihood Methods in Biomedicine and Health

Empirical Likelihood Methods in Biomedicine and Health provides a compendium of nonparametric likelihood statistical techniques in the;

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Empirical Likelihood Methods in Biomedicine and Health

Empirical Likelihood Methods in Biomedicine and Health provides a compendium of nonparametric likelihood statistical techniques in the;

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Empirical Likelihood Method in Survival Analysis

empirical likelihood method for right censored survival data. The author uses R for calculating empirical likelihood and includes many worked out;

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Empirical Likelihood Method in Survival Analysis

empirical likelihood method for right censored survival data. The author uses R for calculating empirical likelihood and includes many worked out;

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Handbook of Discrete-Valued Time Series

series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the;

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Handbook of Discrete-Valued Time Series

series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the;

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Likelihood and Bayesian Inference

. Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes;

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Likelihood & Bayesian Inference

. Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes;

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Quantile Regression

robustness, expectiles, m-quantile, decomposition, time series, elemental sets and linear programming. Graphical representations are widely used to;

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Copula Based Markov Models for Time Series

This book provides statistical methodologies for time series data, focusing on copula-based Markov chain models for serially correlated;

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Empirical Likelihood

treatment of this method for constructing confidence regions and testing hypotheses. The author applies empirical likelihood to a range of problems;

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Bayesian Multivariate Time Series Methods for Empirical Macroeconomics

Bayesian Multivariate Time Series Methods for Empirical Macroeconomics provides a survey of the Bayesian methods used in modern empirical;

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Econometric Modelling with Time Series

This book provides a general framework for specifying, estimating and testing time series econometric models. Special emphasis is given to;

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Econometric Modelling with Time Series

This book provides a general framework for specifying, estimating and testing time series econometric models. Special emphasis is given to;

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Non-Linear Time Series

This book offers a useful combination of probabilistic and statistical tools for analyzing nonlinear time series. Key features of the book;

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Quantile Regression for Cross-Sectional and Time Series Data: Applications in Energy Markets Using R

methodology for the first time, and practitioners at private or public organizations who are interested in modeling different fragments of the;

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Applying Quantitative Bias Analysis to Epidemiologic Data

This textbook and guide focuses on methodologies for bias analysis in epidemiology and public health, not only providing updates to the;

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Empirical Bayes and Likelihood Inference

of Bayesian, likelihood and empirical Bayes methods, presented at a workshop held in Montreal, Canada. The goal of the workshop was to explore the;

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Handbook of Quantile Regression

gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By focusing;

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