000 02382nam a2200301Ia 4500
001 355562
003 0000000000
005 20211104093408.0
008 180629s2018 nju 000 0 eng c
010 _a2018023718
020 _a9781119431909 (hardcover)
035 _a20566778
040 _aWaSeSS/DLC
_cWaSeSS
_erda
050 _aQA 280
_b.P196 2019
100 _aPaolella, Marc S.,
_9128246
245 0 _aLinear models and time-series analysis :
_bregression, ANOVA, ARMA and GARCH /
_cDr. Marc S. Paolella.
264 _aHoboken, NJ :
_bJohn Wiley & Sons,
_cc2019.
300 _axvi, 880 pages
_c24 cm.
336 _atext
_2rdacontent
337 _aunmediated
_2rdamedia
338 _avolume
_2rdacarrier
520 _aA comprehensive and timely edition on an emerging new trend in time series. 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. The topic of time series analysis is on firm footing, with numerous textbooks and research journals dedicated to it. With respect to the subject/technology, many chapters in Linear Models and Time-Series Analysis cover firmly entrenched topics (regression and ARMA). Several others are dedicated to very modern methods, as used in empirical finance, asset pricing, risk management, and portfolio optimization, in order to address the severe change in performance of many pension funds, and changes in how fund managers work. --Wiley.com
650 _aLinear models (Statistics)
_953804
650 _aTime-series analysis.
_929980
942 _cCIR
999 _c91918
_d91918