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Methods of Statistical Model Estimation

Methods of Statistical Model Estimation examines the most important and popular methods used to estimate parameters for statistical models;

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Methods of Statistical Model Estimation

Methods of Statistical Model Estimation examines the most important and popular methods used to estimate parameters for statistical models;

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An Introduction to Generalized Linear Models

; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore;

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Theory Of Preliminary Test And Stein-Type Estimation With Applications

of estimation in a variety of standard models used in applied statistical inference. It is an in-depth introduction to the estimation theory for;

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An Introduction to Generalized Linear Models

distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming;

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An Introduction to Generalized Linear Models

distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming;

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Multi-Pitch Estimation

. In this book, an introduction to pitch estimation is given and a number of statistical methods for pitch estimation are presented. The basic;

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Pricing Analytics

elasticities, which measure the effects of pricing on key business metrics. Elasticity estimation is the goal of statistical modeling, so attention;

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Pricing Analytics

from data. Specifically, itcomes from statistically modeling the data. This book gives the reader the statistical modeling tools needed to;

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Fundamental Statistical Inference

-Fundamental Statistical Inference: A Computational Approach offers comprehensive chapters on: Introducing Point and Interval Estimation; Goodness;

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Statistical Factor Analysis And Related Methods

models, factor identification, factor rotation, and the estimation of factor scores* The use of factor models in conjunction with various types;

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Maximum Entropy and Bayesian Methods

Mexico, USA, from July 31 to August 4, 1995. Maximum entropy and Bayesian methods are widely applied to statistical data analysis and scientific;

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Statistical Inference

Estimation is literally huge. Filling a statistical resource gap, Statistical Inference: The Minimum Distance Approach comprehensively overviews;

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Statistical Inference

different ways of constructing an appropriate distance between the data and the model: the scope of study referred to by Minimum Distance Estimation;

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Statistical Methods Of Geophysical Data Processing

data. The reader can also find many important topics, which are the basis for statistical methods of estimation and inverse problem solutions.;

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Handbook of Item Response Theory: Volume 2

identification issues. It also addresses recent developments in parameter estimation and model fit and comparison, such as Bayesian approaches;

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Handbook of Item Response

identification issues. It also addresses recent developments in parameter estimation and model fit and comparison, such as Bayesian approaches;

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Statistical Reliability Engineering

field, Statistical Reliability Engineering: Covers all mathematical models for statistical reliability analysis, including Bayesian estimation;

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Advanced Linear Models

This work details the statistical inference of linear models including parameter estimation, hypothesis testing, confidence intervals, and;

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Statistical Methods in Control & Signal Processing

work documents developments in statistical modelling, identification, estimation and signal processing. It also covers such topics as subspace;

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The Statistical Analysis of Recurrent Events

This book presents models and statistical methods for the analysis of recurrent event data. The authors provide broad, detailed coverage;

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The Statistical Analysis of Recurrent Events

This book presents models and statistical methods for the analysis of recurrent event data. The authors provide broad, detailed coverage;

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Applied Geostatistics

simulated deposit is the focus for the book. This model helps the student develop an understanding of how statistical tools work, serving as a;

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Small Area Estimation

researchers and practitioners. Journal of the American Statistical Association Written by two experts in the field, Small Area Estimation, Second;

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Statistical Reliability Engineering

applications, including: statistical methods and model selection for machine learning; models for maintenance and software;

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Multi-Pitch Estimation

. In this book, an introduction to pitch estimation is given and a number of statistical methods for pitch estimation are presented. The basic;

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Environmental and Ecological Statistics with R

explains the approach to solving a statistical problem, covering model specification, parameter estimation, and model evaluation. It includes many;

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