Statistical estimation

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  • In addition to covering statistical methods, most of the existing books on equating also focus on the practice of equating, the implications of test development and test use for equating practice and policies, and the daily equating challenges that need to be solved. In some sense, the scope of this book is narrower than of other existing books: to view the equating and linking process as a statistical estimation task.

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  • (BQ) Part 1 book "Design of experiments in chemical engineering" has contents: The simplest discrete and continuous distributions, discrete distributions, continuous distribution, normal distributions, statistical inference, statistical hypotheses, statistical estimation,...and other contents.

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  • Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article Improved Noise Minimum Statistics Estimation Algorithm for Using in a Speech-Passing Noise-Rejecting Headset

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  • We present a stochastic parsing system consisting of a Lexical-Functional Grammar (LFG), a constraint-based parser and a stochastic disambiguation model. We report on the results of applying this system to parsing the UPenn Wall Street Journal (WSJ) treebank. The model combines full and partial parsing techniques to reach full grammar coverage on unseen data. The treebank annotations are used to provide partially labeled data for discriminative statistical estimation using exponential models.

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  • (BQ) Part 2 book "Business statistics: A decision - making approach" has contents: Estimating single population parameters, introduction to hypothesis testing, estimation and hypothesis testing for two population parameters, analysis of variance,...and other contents.

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  • BQ) Part 2 book "Business statistics: A decision - making approach" has contents: Estimating single population parameters, introduction to hypothesis testing, estimation and hypothesis testing for two population parameters, analysis of variance,...and other contents.

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  • Since the publication of my book Mathematical Statistics (Shao, 2003), I have been asked many times for a solution manual to the exercises in my book. Without doubt, exercises form an important part of a textbook on mathematical statistics, not only in training students for their research ability in mathematical statistics but also in presenting many additional results as complementary material to the main text.

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  • (1) Since the simpler model features less regressor than the larger model, it follows that the VIF of the simpler model will be less than that of the larger model. The reason is that the more variables we include in the model, the greater multicollinearity, and, hence, the greater Rj 2 , unless the omitted variables happen to be orthogonal to the regressors included in the simpler model. The simpler model, which omits relevant variables, produces bias estimates but with smaller variances. Consequently, there appears to be a tradeoff between bias and precision. ...

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  • The original motivation for writing this book was rather personal. The first author, in the course of his teaching career in the Department of Pure Mathematics and Mathematical Statistics (DPMMS), University of Cambridge, and St John’s College, Cambridge, had many painful experiences when good (or even brilliant) students, who were interested in the subject of mathematics and its applications and who performed well during their first academic year, stumbled or nearly failed in the exams. This led to great frustration, which was very hard to overcome in subsequent undergraduate years.

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  • The main thrust is to provide students with a solid understanding of a number of important and related advanced topics in digital signal processing such as Wiener filters, power spectrum estimation, signal modeling and adaptive filtering. Scores of worked examples illustrate fine points, compare techniques and algorithms and facilitate comprehension of fundamental concepts. Also features an abundance of interesting and challenging problems at the end of every chapter.

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  • Applied statistics for civil and environmental engineers has many contents: Preliminary Data Analysis, Basic Probability Concepts, Random Variables and Their Properties, Model Estimation and Testing, Methods of Regression and Multivariate Analysis, Frequency Analysis of Extreme Events, Simulation Techniques for Design, Risk and Reliability Analysis, Bayesian Decision Methods and Parameter Uncertainty.

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  • A very popular approach for estimating the independent component analysis (ICA) model is maximum likelihood (ML) estimation. Maximum likelihood estimation is a fundamental method of statistical estimation; a short introduction was provided in Section 4.5. One interpretation of ML estimation is that we take those parameter values as estimates that give the highest probability for the observations. In this section, we show how to apply ML estimation to ICA estimation.

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  • Applied statistics and probabilty for engineers_This is an introductory textbook for a first course in applied statistics and probability for undergraduate students in engineering and the physical or chemical sciences. These individuals play a significant role in designing and developing new products and manufacturing systems and processes, and they also improve existing systems. Statistical methods are an important tool in these activities because they provide the engineer with both descriptive and analytical methods for dealing with the variability in observed data.

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  • (BQ) Part 2 book "Statistics for managers using - Microsoft excel" has contents: Sampling and sampling distributions, confidence interval estimation, fundamentals of hypothesis testing - One sample tests, simple linear regression, introduction to multiple regression.

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  • What would science be without measurement, and what would social science be without social measurement? Social measurement belongs to the widely accepted and fruitful stream of the empirical-analytic approach. Statistics and research methodology play a central role, and it is difficult to ascertain precisely when it all started in the social sciences. An important subfield of social measurement is the quantification of human behavior—that is, using measurement instruments of which educational and psychological tests are the most prominent representatives.

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  • We present a syntax-based statistical translation model. Our model transforms a source-language parse tree into a target-language string by applying stochastic operations at each node. These operations capture linguistic differences such as word order and case marking. Model parameters are estimated in polynomial time using an EM algorithm. The model produces word alignments that are better than those produced by IBM Model 5. is conditioned only on word classes and positions in the string, and the duplication and translation are conditioned only on the word identity. ...

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  • We present a statistical model of Japanese unknown words consisting of a set of length and spelling models classified by the character types that constitute a word. The point is quite simple: different character sets should be treated differently and the changes between character types are very important because Japanese script has both ideograms like Chinese (kanji) and phonograms like English (katakana). Both word segmentation accuracy and part of speech tagging accuracy are improved by the proposed model. ...

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  • This volume describes the essential tools and techniques of statistical signal processing. At every stage, theoretical ideas are linked to specific applications in communications and signal processing. The book begins with an overview of basic probability, random objects, expectation, and second-order moment theory, followed by a wide variety of examples of the most popular random process models and their basic uses and properties.

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  • (BQ) Part 1 book "Mathematical statistics" has contents: Probability theory, fundamentals of statistics, unbiased estimation, estimation in parametric models. Please refer to the content.

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  • (BQ) Part 1 book "Introduction to probability and statistics" has contents: Describing data with graphs, describing data with numerical measures, describing bivariate data, probability and probability distributions, several useful discrete distributions, the normal probability distribution, sampling distributions, large sample estimation.

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