Tag Archives: Regression

Nonlinear Regression with R (Use R!)

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Nonlinear Regression with R (Use R!) By Christian Ritz, Jens Carl Streibig
2008 | 150 Pages | ISBN: 0387096159 | PDF | 4 MB

Nonlinear Regression with R (Use R!)
– Coherent and unified treatment of nonlinear regression with R.
– Example-based approach.
– Wide area of application.
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Multivariable Modelling: A pragmatic approach based on fractional polynomials for continuous variables

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Patrick Royston, Willi Sauerbrei, "Multivariable Modelling: A pragmatic approach based on fractional polynomials for continuous variables"
2008 | ISBN-10: 0470028424 | 322 pages | PDF | 8 MB

Multivariable Modelling: A pragmatic approach based on fractional polynomials for continuous variables
Multivariable regression models are of fundamental importance in all areas of science in which empirical data must be analyzed. This book proposes a systematic approach to building such models based on standard principles of statistical modeling. The main emphasis is on the fractional polynomial method for modeling the influence of continuous variables in a multivariable context, a topic for which there is no standard approach. Existing options range from very simple step functions to highly complex adaptive methods such as multivariate splines with many knots and penalisation. This new approach, developed in part by the authors over the last decade, is a compromise which promotes interpretable, comprehensible and transportable models.
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First (and Second) Steps in Statistics

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First (and Second) Steps in Statistics by ?Daniel B. Wright
English | 2009 | ISBN: 1412911419 | 248 pages | EPUB | 3 MB

First (and Second) Steps in Statistics
First (and Second) Steps in Statistics, Second Edition provides a clear and concise introduction to the main statistical procedures used in the psychological and social sciences. The rationale and procedure for analyzing data are presented through exciting examples, with an emphasis on understanding rather than computation. It is ideally suited for introductory courses in statistics. In addition to descriptive statistics, graphs, t tests, one way ANOVAs, Chi-square, and simple linear regression, this second edition includes factorial ANOVA and multiple regression. Emphasis is given to tests of median and other robust methods.
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Python for Finance: Analyze Big Financial Data

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Yves Hilpisch, "Python for Finance: Analyze Big Financial Data"
English | ISBN: 1491945281 | 2015 | 606 pages | True PDF | 10 MB

Python for Finance: Analyze Big Financial Data
The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance.

Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include:
Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices
Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression
Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies
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Python for Finance: Analyze Big Financial Data

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Yves Hilpisch, "Python for Finance: Analyze Big Financial Data"
English | ISBN: 1491945281 | 2015 | 606 pages | True PDF | 10 MB

Python for Finance: Analyze Big Financial Data
The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance.

Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include:
Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices
Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression
Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies
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Python for Finance: Analyze Big Financial Data (PDF)

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Yves Hilpisch, "Python for Finance: Analyze Big Financial Data"
English | ISBN: 1491945281 | 2015 | 606 pages | True PDF | 10 MB
The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance.

Python for Finance: Analyze Big Financial Data (PDF)
Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include:
Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matDescriptionlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices
Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression
Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies

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Statistical Methods and Reasoning for the Clinical Sciences

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Eiki Satake, "Statistical Methods and Reasoning for the Clinical Sciences"
English | ISBN: 1597564338 | 2015 | 303 pages | PDF | 10 MB

Statistical Methods and Reasoning for the Clinical Sciences
The growing emphasis on evidence-based practice has increased the importance of using clinical studies for empirical demonstration of the efficacy of clinical interventions. As a result, speech-language pathologists and audiologists must be well-versed in research methods and statistical analysis. In fact, a demonstrated knowledge of statistics (including a stand-alone course in statistics) is a requirement of ASHA certification effective September 1, 2014. is the ideal textbook to meet the need for a solid understanding of statistics for communication sciences and disorders. The author clearly defines and illustrates the foundational concepts of statistics, including statistical vocabulary, population parameters, sampling methods, and descriptive methods like measures, correlation, and regression. Emphasis is placed on the topic of probability because a firm grasp of the probabilistic approach is essential for any clinician to generate a precise diagnosis. The readers of this textbook will: Comprehend how clinical research reflects a series of steps that conform with the scientific method of problem solving (observation, hypothesis formation, hypothesis testing, verification, and evaluation). Appreciate the importance of including rationales in a research study that entail three interrelated tasks: description (why it was done), explanation (what was done and to whom), contextualization (how the results relate to other bodies of knowledge). Distinguish between "statistical significance" and "clinical significance." Value the importance of scientific literacy as a major ingredient of evidence practice. With its comprehensive scope and timely content is the ideal text for students of communication sciences and disorders who wish to engage in truly evidence-based practice.
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Nonparametric Regression and Generalized Linear Models:- A roughness penalty approach

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Nonparametric Regression and Generalized Linear Models: A roughness penalty approach By P.J. Green, Bernard. W. Silverman
1994 | 184 Pages | ISBN: 0412300400 | PDF | 4 MB

Nonparametric Regression and Generalized Linear Models:- A roughness penalty approach
In recent years, there has been a great deal of interest and activity in the general area of nonparametric smoothing in statistics. This monograph concentrates on the roughness penalty method and shows how this technique provides a unifying approach to a wide range of smoothing problems. The method allows parametric assumptions to be realized in regression problems, in those approached by generalized linear modelling, and in many other contexts.

The emphasis throughout is methodological rather than theoretical, and it concentrates on statistical and computation issues. Real data examples are used to illustrate the various methods and to compare them with standard parametric approaches. Some publicly available software is also discussed. The mathematical treatment is self-contained and depends mainly on simple linear algebra and calculus.

This monograph will be useful both as a reference work for research and applied statisticians and as a text for graduate students and other encountering the material for the first time.
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Nonparametric Regression and Generalized Linear Models:- A roughness penalty approach

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Nonparametric Regression and Generalized Linear Models: A roughness penalty approach By P.J. Green, Bernard. W. Silverman
1994 | 184 Pages | ISBN: 0412300400 | PDF | 4 MB

Nonparametric Regression and Generalized Linear Models:- A roughness penalty approach
In recent years, there has been a great deal of interest and activity in the general area of nonparametric smoothing in statistics. This monograph concentrates on the roughness penalty method and shows how this technique provides a unifying approach to a wide range of smoothing problems. The method allows parametric assumptions to be realized in regression problems, in those approached by generalized linear modelling, and in many other contexts.

The emphasis throughout is methodological rather than theoretical, and it concentrates on statistical and computation issues. Real data examples are used to illustrate the various methods and to compare them with standard parametric approaches. Some publicly available software is also discussed. The mathematical treatment is self-contained and depends mainly on simple linear algebra and calculus.

This monograph will be useful both as a reference work for research and applied statisticians and as a text for graduate students and other encountering the material for the first time.
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