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Journal ArticleDOI

Linear Statistical Inference and Its Applications

N. L. Johnson
- 01 Aug 1966 - 
- Vol. 8, Iss: 3, pp 551-553
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TLDR
Rao's Linear Statistical Inference and Its Applications as discussed by the authors is one of the earliest works in statistical inference in the literature and has been translated into six major languages of the world.
Abstract
"C. R. Rao would be found in almost any statistician's list of five outstanding workers in the world of Mathematical Statistics today. His book represents a comprehensive account of the main body of results that comprise modern statistical theory." -W. G. Cochran "[C. R. Rao is] one of the pioneers who laid the foundations of statistics which grew from ad hoc origins into a firmly grounded mathematical science." -B. Efrom Translated into six major languages of the world, C. R. Rao's Linear Statistical Inference and Its Applications is one of the foremost works in statistical inference in the literature. Incorporating the important developments in the subject that have taken place in the last three decades, this paperback reprint of his classic work on statistical inference remains highly applicable to statistical analysis. Presenting the theory and techniques of statistical inference in a logically integrated and practical form, it covers: * The algebra of vectors and matrices * Probability theory, tools, and techniques * Continuous probability models * The theory of least squares and the analysis of variance * Criteria and methods of estimation * Large sample theory and methods * The theory of statistical inference * Multivariate normal distribution Written for the student and professional with a basic knowledge of statistics, this practical paperback edition gives this industry standard new life as a key resource for practicing statisticians and statisticians-in-training.

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Citations
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Journal ArticleDOI

C ONDENSATION —Conditional Density Propagation forVisual Tracking

TL;DR: The Condensation algorithm uses “factored sampling”, previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set.
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On the Impossibility of Informationally Efficient Markets

TL;DR: In this paper, the authors propose a model in which there is an equilibrium degree of disequilibrium: prices reflect the information of informed individuals (arbitrageurs) but only partially, so that those who expend resources to obtain information do receive compensation.
Journal ArticleDOI

Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses

Quang Vuong
- 01 Mar 1989 - 
TL;DR: In this article, the authors propose simple and directional likelihood-ratio tests for discriminating and choosing between two competing models whether the models are nonnested, overlapping or nested and whether both, one, or neither is misspecified.
Journal ArticleDOI

On the convergence properties of the em algorithm

C. F. Jeff Wu
- 01 Mar 1983 - 
TL;DR: In this paper, the EM algorithm converges to a local maximum or a stationary value of the (incomplete-data) likelihood function under conditions that are applicable to many practical situations.