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
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
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.
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.
(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
, 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.
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.
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.
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.
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.
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
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. ...
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. ...
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.
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.
This book was written for graduate students and researchers in statistics and the
social sciences. Our intent in writing the book was to bridge the gap between
recent theoretical developments in statistics and the application of these methods
to ordinal data. Ordinal data are the most common form of data acquired in the
social sciences, but the analysis of such data is generally performed without regard
to their ordinal nature.
In this paper, we present a novel global reordering model that can be incorporated into standard phrase-based statistical machine translation. Unlike previous local reordering models that emphasize the reordering of adjacent phrase pairs (Tillmann and Zhang, 2005), our model explicitly models the reordering of long distances by directly estimating the parameters from the phrase alignments of bilingual training sentences.
This paper presents a comparative study of five parameter estimation algorithms on four NLP tasks. Three of the five algorithms are well-known in the computational linguistics community: Maximum Entropy (ME) estimation with L2 regularization, the Averaged Perceptron (AP), and Boosting. We also investigate ME estimation with L1 regularization using a novel optimization algorithm, and BLasso, which is a version of Boosting with Lasso (L1) regularization. We first investigate all of our estimators on two re-ranking tasks: a parse selection task and a language model (LM) adaptation task. ...
World health statistics 2007 presents the most recent health statistics for WHO’s 193 Member States. This
third edition includes a section with 10 highlights of global health statistics for the past year as well as an
expanded set of 50 health statistics.
World health statistics 2007 has been collated from publications and databases produced by WHO’s
technical programmes and regional offi ces. The core set of indicators was selected on the basis of their
relevance to global health, the availability and quality of the data, and the accuracy and comparability
We describe a new loss function, due to Jeon and Lin (2006), for estimating structured log-linear models on arbitrary features. The loss function can be seen as a (generative) alternative to maximum likelihood estimation with an interesting information-theoretic interpretation, and it is statistically consistent. It is substantially faster than maximum (conditional) likelihood estimation of conditional random ﬁelds (Lafferty et al., 2001; an order of magnitude or more).
Discriminative methods have shown signiﬁcant improvements over traditional generative methods in many machine learning applications, but there has been diﬃculty in extending them to natural language parsing. One problem is that much of the work on discriminative methods conﬂates changes to the learning method with changes to the parameterization of the problem. We show how a parser can be trained with a discriminative learning method while still parameterizing the problem according to a generative probability model.