Linear Models and Time-Series Analysis

By Marc S. Paolella. Wiley Series in Probability and Statistics, 2018

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Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH sets a strong foundation, in terms of distribution theory, for the linear model (regression and ANOVA), univariate time series analysis (ARMAX and GARCH), and some multivariate models associated primarily with modeling financial asset returns (copula-based structures and the discrete mixed normal and Laplace). It builds on the author's previous book, Fundamental Statistical Inference: A Computational Approach, which introduced the major concepts of statistical inference. Attention is explicitly paid to application and numeric computation, with examples of Matlab code throughout. The code offers a framework for discussion and illustration of numerics, and shows the mapping from theory to computation.

Subfields: Control and Systems Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2c79324060180150f2c0a6809eeab9955e5cfa65