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This is a Nova Science Publication.
Univariate statistical distributions, with their basic properties,
are an important part of advance statistics. The "Handbook on
Univariate Statistical Distributions" includes most of the
univariate statistical distributions that are used in practice.
Author M. Ahsanullah has presented most of the common univariate
discrete and continuous statistical distributions with their basic
properties. For each distribution, most of the common basic
properties-such as distribution functions, moments, and generating
functions-are provided for easy reference. This information is
integral to understanding and using these distributions.The first
chapter includes definitions and concepts that are needed to study
the distributions and some mathematical functions that are used in
other chapters. Successive chapters include distributions and their
generalized forms with basic properties and relations with other
distributions. In addition, order statistics and record values are
discussed for some of the distributions. The "Handbook on
Univariate Statistical Distributions, " an excellent reference for
researchers and practitioners who conduct in-depth statistical
analysis, is the definitive guide to understanding the vitally
important statistical distributions, designed with upper level
undergraduate and graduate students in mind.
CONTENTS: Partially Adaptive Rank and Regression Rank Scores Tests
in Linear Models; An Analysis of Nonoparametric Smoothers;
Supercritical Branching Random Walk in D-Dimensional Random
Environment; Lack of Fit Tests in Regression With Non-Random
Design; Asymptotics of the Deepest Line; Multivariate Rank
Statistics Processes and Change Point Analysis; Improved Estimation
of the Parameters of an Autoagressive Gaussian Process Under
Uncertain Restrictions; Testing Normality For Censored Data; Large
Sample theory For Estimators of the Moments Based On Synthetic Data
Under Randomly Right-Censoring; The Stein Phenomenon in
Simultaneous Estimation: A Review; Two Techniques of Integration By
Parts and Some Applications; Conditional Confidence Intervals of
Regression Coefficients Following Rejection of Preliminary Test;
Order Preserving Estimators of Eigenvalues of the Scale Matrix in
the Multivariate F Distribution Under Stein's Loss Function;
Sequential Estimation of the Man of An Exponential Distribution Via
Partial Piece Wise Sampling; Recent Developments on Probability
Matching Priors; On the Informative Presentation of Likelihood;
Bahadur Risk, Exponential Families and Recursive Estimation; Some
Quick Estimators Based on Sample Maxima; Inferences of Power
Function Distribution Based on Ordered Random Variables; Estimation
of the Location Parameter of A Cauchy Distribution Using A Ranked
Set Sample; On A Delayed Service Queuing System With Random Server
Capacity and Impatient Customers; Canonical Co-ordinated for
Graphical Representation of Multivariate Data; Some Single Use
Confidence Regions in Multivariate Calibration Problem; The
Likelihood Ratio Test of Non-Nested Linear Regression Models; Exact
Power of Classical Tests for Bivariate Linear Hypothesis;
Characterisation of the Gamma and the Complex Case Wishart
Densities; Jack-knife and Robust Estimation for the Parameters in
Pharmocokinetes.
Computers have taken a permanent place in almost every human
endeavour in the last 20 years. This infiltration requires a
learning process on the part of the people utilising them and
realising where and how they can be best used beyond the basic and
obvious applications. Statistics is an example of their application
in many diverse fields to reach conclusions and make projections
never before possible. Beyond this, applied statistics is rapidly
becoming not only an instrument, but an integral part of the
advance of knowledge. There are many fields such as medicine,
biology, weather prediction, military planning, and many others
where the statistical studies are essential before the next step
can be taken. This new book presents the latest research in the
field.
Computers have taken a permanent place in almost every human
endeavor in the last 20 years. This infiltration requires a
learning process on the part of the people utilising them and
realising where and how they can be best used beyond the basic and
obvious applications. Statistics is an example of their application
in many diverse fields to reach conclusions and make projections
never before possible. Beyond this, applied statistics is rapidly
becoming not only an instrument, but an integral part of the
advance of knowledge. There are many fields such as medicine,
biology, weather prediction, military planning, and many others
where the statistical studies are essential before the next step
can be taken. This book presents recent research in the field from
around the globe.
Given the popularity of statistical explanations in virtually every
field of work, it is no wonder this collection of nine articles
addresses such a range of topics, ranging from applications of
classical stochastic processes to new approaches. Subjects include
non-normal distributions that describe the Bayesian updating of
atmospheric models, efficient uniform designs for mixture
experiments in three and four components, designs of accelerated
life tests for periodic inspection with Burr Type III
distributions, parameter estimation using Cressie-Read divergence
measures with exponential grouped censored data, estimation of
variance components of acceleration degradation models, production
of the ration of the symmetric differences of order statistics,
surface roughness measurements acquired by spatial statistics,
Diallel crosses, and the characterization of distributions by
conditional expectations of functions of generalized order
statistics. Some articles may not include classic rather than
leading-edge approaches and data.
Applied Statistical Science V
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