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Advances in Directional and Linear Statistics

by Wells, Martin T.
Authors: SenGupta, Ashis.%editor. | SpringerLink (Online service) Physical details: XX, 331p. 38 illus. online resource. ISBN: 3790826286 Subject(s): Statistics. | Mathematical statistics. | Statistics. | Statistical Theory and Methods.
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E-Book E-Book AUM Main Library 519.5 (Browse Shelf) Not for loan

Models for Axial Data -- Asymptotic Behavior of the Universally Consistent Conditional U-Statistics for Nonstationary and Absolutely Regular Processes -- Regression Models with STARMA Errors − An Application to the Study of Temperature Variations in the Antarctic Peninsula -- The Generalized von Mises-Fisher Distribution -- A New Nonparametric Test of Symmetry -- A Semiparametric Bayesian Method of Clustering Genes Using Time-Series of Expression Profiles -- On Implementation of the Markov Chain Monte Carlo Stochastic Approximation Algorithm -- Stochastic Comparisons of Spacings from Heterogeneous Samples -- The Distributions of the Peak to Average and Peak to Sum Ratios under Exponentiality -- Least Square Estimation for Regression Parameters under Lost Association -- On Tests of Fit Based on Grouped Data -- Innovation Processes in Logically Constrained Time Series -- Laws of Large Numbers and Nearest Neighbor Distances -- Nonparametric and Probabilistic Classification using NN-balls with Environmental and Remote Sensing Applications -- Probabilistic Recurrence Relations -- On Some Inequalities of Chernoff-Borovkov-Utev Type for Circular Distributions -- Revisiting Local Asymptotic Normality (LAN) and Passing on to Local Asymptotic Mixed Normality (LAMN) and Local Asymptotic Quadratic (LAQ) Experiments -- Long Range Dependence in Third Order for Non-Gaussian Time Series -- Graphical Models for Clustered Binary and Continuous Responses.

The present volume consists of papers written by students, colleagues and collaborators of Sreenivasa Rao Jammalamadaka from various countries, and covers a variety of research topics which he enjoys and contributed immensely to.

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