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Optimal Unbiased Estimation of Variance Components

The clearest way into the Universe is through a forest wilderness. John MuIr As recently as 1970 the problem of obtaining optimal estimates;

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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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Variance Components

field of variance component estimation. -Mathematical Reviews The authors have done an excellent job in collecting materials on a broad range;

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GPS-Based Optimal FIR Filtering of Clock Models

This book addresses novel results in the field of optimal finite impulse response (FIR) estimation and steering of the local clock time;

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Parametric Inference: An Introduction

variance unbiased estimator. There is a separate chapter on simultaneous estimation of several parameters. Large sample theory of estimation, based;

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Linear Model Theory

, estimability, best linear unbiased estimation and prediction, ANOVA, confidence intervals, simultaneous confidence intervals, hypothesis testing, and;

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Linear Model Theory

, estimability, best linear unbiased estimation and prediction, ANOVA, confidence intervals, simultaneous confidence intervals, hypothesis testing, and;

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Plane Answers to Complex Questions

matrices, variance component estimation, best linear and best linear unbiased prediction, collinearity, and variable selection. This new edition;

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Plane Answers to Complex Questions

of the standard topics are covered in depth: estimation including biased and Bayesian estimation, significance testing, ANOVA, multiple;

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Plane Answers to Complex Questions

of the standard topics are covered in depth: estimation including biased and Bayesian estimation, significance testing, ANOVA, multiple;

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Proceedings of the International Conference on Linear Statistical Inference Linstat '93

Inference LINSTAT '93, held in Poznan, Poland, from May 31 to June 4, 1993. Topics treated include estimation, prediction and testing in linear;

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Sampling Techniques for Forest Inventories

element sampling and the model-assisted estimation procedure. The author then develops the infinite population model/Monte Carlo approach for both;

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An Engineering Approach to Optimal Control and Estimation Theory

Optimal control and optimal estimation are methods used to estimate unknown variables such as velocity and altitude, and are essential to;

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U-Statistics

In 1946 Paul Halmos studied unbiased estimators of minimum variance, and planted the seed from which the subject matter of the present;

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Photogrammetric Computer Vision

statistically optimal estimation and test of geometric entities and transformations and their rela tions, tools that are useful also in the context;

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U-Statistics: Theory and Practice

In 1946 Paul Halmos studied unbiased estimators of minimum variance, and planted the seed from which the subject matter of the present;

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Mathematical Statistics

that form the basis of modern statistics, and examines the foundations of estimation theory, hypothesis testing theory and statistical game;

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Probability Concepts in Engineering Planning and Design

Randomness 1.2.2. Uncertainty Associated with Imperfect Modeling and Estimation 1.3. Design and Decision-Making Under Uncertainty 1.3.1. Planning and;

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Quantitative Genetics And Its Connections With Big Data And Sequenced Genomes

Genomes. It provides a detailed description of a direct method of estimation that will be a useful means of extracting information from a large;

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Quantitative Genetics And Its Connections With Big Data And Sequenced Genomes

Genomes. It provides a detailed description of a direct method of estimation that will be a useful means of extracting information from a large;

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Examples in Parametric Inference with R

discusses unbiased estimation. Chapter 3 focuses on the study of moments and maximum likelihood estimators, and Chapter 4 presents bounds for the;

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Probability and Statistics

basics of sampling distributions, estimation and hypothesis testing. As a companion for classes for engineers and scientists, the book also;

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Developments in Mean Variance Efficient Portfolio Selection

This book discusses new determinants for optimal portfolio selection. It reviews the existing modelling framework and creates mean-variance;

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RLS Wiener Smoother from Randomly Delayed Observations in Linear Discrete-Time Systems

input noise and the input matrix in the state equation, in comparison with the estimation technique by the Kalman filter. Hence, the RLS Wiener;

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Mathematical Statistics

fundamental concepts in statistical decision theory and inference. Subsequent chapters contain detailed studies on some important topics: unbiased;

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Optimal Estimation Of Parameters

capacity estimator. This approach allows the optimal estimation of real-valued parameters, their number and intervals, as well as providing common;

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Subjective And Objective Bayesian Statistics

distributions, predictivism, estimation, analysis of variance, and classification and imaging.* Includes topics not covered in other books, such as the;

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