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Tuesday, October 16, 2018 , , , ,
Stata Software for Statistics and Data Science v15 [Size: 295.5 MB] ... Stata is a general-purpose statistical software package created in 1985 by StataCorp. Most of its users work in research, especially in the fields of economics, sociology, political science, biomedicine and epidemiology.[citation needed]Stata's capabilities include data management, statistical analysis, graphics, simulations, regression analysis (linear and multiple), and custom programming. The name Stata is a portmanteau of the words statistics and data. The correct English pronunciation of Stata "must remain a mystery"; any of "Stay-ta", "Sta-ta" or "Stah-ta" are considered acceptable.

Features: Linear models: regression • censored outcomes • endogenous regressors • bootstrap, jackknife, and robust and cluster–robust variance • instrumental variables • three-stage least squares • constraints • quantile regression • GLS • more. Panel/longitudinal data: random and fixed effects with robust standard errors • linear mixed models • random-effects probit • GEE • random- and fixed-effects Poisson • dynamic panel-data models • instrumental variables • panel unit-root tests • more. Multilevel mixed-effects models: continuous, binary, count, and survival outcomes • two-, three-, and higher-level models • generalized linear models • nonlinear models • random intercepts • random slopes • crossed random effects • BLUPs of effects and fitted values • hierarchical models • residual error structures • DDF adjustments • support for survey data • more. Binary, count, and limited outcomes: logistic, probit, tobit • Poisson and negative binomial • conditional, multinomial, nested, ordered, rank-ordered, and stereotype logistic • multinomial probit • zero-inflated and left-truncated count models • selection models • marginal effects • more. Extended regression models (ERMs): combine endogenous covariates, sample selection, and nonrandom treatment in models for continuous, interval-censored, binary, and ordinal outcomes • more.
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Sunday, October 14, 2018 , ,
Rex B. Kline ... 534 pages - Publisher: The Guilford Press; 4th edition (November, 2015) ... Language: English - ISBN-10: 146252334X - ISBN-13: 978-1462523344 ...

Emphasizing concepts and rationale over mathematical minutiae, this is the most widely used, complete, and accessible structural equation modeling (SEM) text. Continuing the tradition of using real data examples from a variety of disciplines, the significantly revised fourth edition incorporates recent developments such as Pearl's graphing theory and the structural causal model (SCM), measurement invariance, and more. Readers gain a comprehensive understanding of all phases of SEM, from data collection and screening to the interpretation and reporting of the results. Learning is enhanced by exercises with answers, rules to remember, and topic boxes. The companion website supplies data, syntax, and output for the book's examples--now including files for Amos, EQS, LISREL, Mplus, Stata, and R (lavaan). New to This Edition: Extensively revised to cover important new topics: Pearl's graphing theory and the SCM, causal inference frameworks, conditional process modeling, path models for longitudinal data, item response theory, and more. + Chapters on best practices in all stages of SEM, measurement invariance in confirmatory factor analysis, and significance testing issues and bootstrapping. + Expanded coverage of psychometrics. + Additional computer tools: online files for all detailed examples, previously provided in EQS, LISREL, and Mplus, are now also given in Amos, Stata, and R (lavaan). + Reorganized to cover the specification, identification, and analysis of observed variable models separately from latent variable models.
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Sunday, October 14, 2018 , ,
GeoStudio 2018 R2 v9.1.1.16749 [Size: 351 MB] ... GeoStudio [A powerful suite of integrated software products for geotechnical modeling & analysis.] is a product suite for geotechnical and geo-environmental modeling, broad enough to handle all your modeling needs. The suite consists of 8 products: SLOPE/W for slope stability; SEEP/W for groundwater seepage; SIGMA/W for stress-deformation; QUAKE/W for dynamic earthquake; TEMP/W for geothermal; CTRAN/W for contaminant transport; AIR/W for air flow; VADOSE/W for vadose zone & covers. Why choose GeoStudio? Rigorous analytical capability. Sophisticated product integration. Broad application to diverse geo-engineering and earth science problems.
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Saturday, October 13, 2018 , , , ,
Johan A. K. Suykens, Marco Signoretto ... 525 pages - Publisher: Chapman and Hall/CRC; (October, 2014) ... Language: English - ISBN-10: 1482241390 - ISBN-13: 978-1482241396 ...

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: Covers the relationship between support vector machines (SVMs) and the Lasso * Discusses multi-layer SVMs * Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing * Describes graph-based regularization methods for single- and multi-task learning * Considers regularized methods for dictionary learning and portfolio selection * Addresses non-negative matrix factorization * Examines low-rank matrix and tensor-based models * Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing * Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent. Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.
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Friday, October 12, 2018 , , ,
Boominathan Adimoolam, Subhadeep Banerjee ... 274 pages - Publisher: Springer; (June, 2018) ... Language: English - ASIN: B07DMTL38L by Amazon

This book gathers selected proceedings of the annual conference of the Indian Geotechnical Society, and covers various aspects of soil dynamics and earthquake geotechnical engineering. The book includes a wide range of studies on seismic response of dams, foundation-soil systems, natural and man-made slopes, reinforced-earth walls, base isolation systems and so on, especially focusing on the soil dynamics and case studies from the Indian subcontinent. The book also includes chapters addressing related issues such as landslide risk assessments, liquefaction mitigation, dynamic analysis of mechanized tunneling, and advanced seismic soil-structure-interaction analysis. Given its breadth of coverage, the book offers a useful guide for researchers and practicing civil engineers alike.
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Thursday, October 11, 2018 , , , ,
Jian Qing Shi, Taeryon Choi ... 216 pages - Publisher: CRC; (July, 2011) .. Language: English - ISBN-10: 1439837732 - ISBN-13: 978-1439837733 ...

Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables. Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dimensional data and variable selection. The remainder of the text explores advanced topics of functional regression analysis, including novel nonparametric statistical methods for curve prediction, curve clustering, functional ANOVA, and functional regression analysis of batch data, repeated curves, and non-Gaussian data. Many flexible models based on Gaussian processes provide efficient ways of model learning, interpreting model structure, and carrying out inference, particularly when dealing with large dimensional functional data. This book shows how to use these Gaussian process regression models in the analysis of functional data. Some MATLAB® and C codes are available on the first author’s website.
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