TY - BOOK ID - 33239512 TI - Finite Mixture of Skewed Distributions AU - Lachos Dávila, Víctor Hugo. AU - Cabral, Celso Rômulo Barbosa. AU - Zeller, Camila Borelli. PY - 2018 SN - 3319980297 3319980289 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Mixture distributions (Probability theory) KW - Mathematical statistics KW - Data processing. KW - Compound distributions (Probability theory) KW - Distributions, Mixture (Probability theory) KW - Mixed distributions (Probability theory) KW - Mixing distributions (Probability theory) KW - Mixtures of distributions (Probability theory) KW - Distribution (Probability theory) KW - Mathematical statistics. KW - Statistics. KW - Statistical Theory and Methods. KW - Statistics for Life Sciences, Medicine, Health Sciences. KW - Statistics and Computing/Statistics Programs. KW - Statistical analysis KW - Statistical data KW - Statistical methods KW - Statistical science KW - Mathematics KW - Econometrics KW - Statistical inference KW - Statistics, Mathematical KW - Statistics KW - Probabilities KW - Sampling (Statistics) KW - Statistics . UR - https://www.unicat.be/uniCat?func=search&query=sysid:33239512 AB - This book presents recent results in finite mixtures of skewed distributions to prepare readers to undertake mixture models using scale mixtures of skew normal distributions (SMSN). For this purpose, the authors consider maximum likelihood estimation for univariate and multivariate finite mixtures where components are members of the flexible class of SMSN distributions. This subclass includes the entire family of normal independent distributions, also known as scale mixtures of normal distributions (SMN), as well as the skew-normal and skewed versions of some other classical symmetric distributions: the skew-t (ST), the skew-slash (SSL) and the skew-contaminated normal (SCN), for example. These distributions have heavier tails than the typical normal one, and thus they seem to be a reasonable choice for robust inference. The proposed EM-type algorithm and methods are implemented in the R package mixsmsn, highlighting the applicability of the techniques presented in the book. This work is a useful reference guide for researchers analyzing heterogeneous data, as well as a textbook for a graduate-level course in mixture models. The tools presented in the book make complex techniques accessible to applied researchers without the advanced mathematical background and will have broad applications in fields like medicine, biology, engineering, economic, geology and chemistry. ER -