Advances in Directional and Linear Statistics A Festschrift for Sreenivasa Rao Jammalamadaka /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Wells, Martin T. (Επιμελητής έκδοσης), SenGupta, Ashis (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Heidelberg : Physica-Verlag HD, 2011.
• Θέματα: Statistics.; Statistical Theory and Methods.

 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. 

 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.