A new edition of this popular text on robust statistics, thoroughly updated to include new and
improved methods and focus on implementation of methodology using the increasingly …

Provides a comprehensive and updated study of GARCH models and their applications in
finance, covering new developments in the discipline This book provides a comprehensive …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

The familiar Gaussian models do not allow for large deviations and are thus often
inadequate for modeling high variability. Non-Gaussian stable models do not possess such …

Statistical Methods for Long Term Memory Processes covers the diverse statistical methods
and applications for data with long-range dependence. Presenting material that previously …

Amongmanyexcitingdevelopmentsinstatistic…, nonlineartimeseriesanddata-
analyticnonparametricmethodshavegreatly advanced along seemingly unrelated paths. In …

The LAD estimator of the vector parameter in a linear regression is defined by minimizing
the sum of the absolute values of the residuals. This paper provides a direct proof of …

We consider quantile autoregression (QAR) models in which the autoregressive coefficients
can be expressed as monotone functions of a single, scalar random variable. The models …

We prove the strong consistency and asymptotic normality of the quasi-maximum likelihood
estimator of the parameters of pure generalized autoregressive conditional heteroscedastic …

We study statistical inference in quantile autoregression models when the largest
autoregressive coefficient may be unity. The limiting distribution of a quantile autoregression …