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Semiparametric Wavelet-Based JPEG IV Estimator for Endogenously Truncated Data

Semiparametric Wavelet-Based JPEG IV Estimator for Endogenously Truncated Data
Author: Nir Billfeld
Publisher:
Total Pages: 58
Release: 2018
Genre:
ISBN:

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We show that when data are endogenously truncated the widely-used IV fails to render the relationship causal as well as introduces bias into the exogenous covariates. We offer a newly-introduced semiparametric biorthogonal wavelet-based JPEG IV estimator and its associated symmetry preserving kernel, which is closely related to object recognition methods in Artificial Intelligence. The newly-introduced enriched JPEG algorithm is a denoising tool amenable for identifying redundancies in a sequence of irregular noisy data points which also accommodates a reference-free criterion function for optimal denoising. This is suitable for situations where the original data distribution is unobservable such as in the case of endogenous truncation. This estimator corrects both biases, the one generated by endogenous truncation and the one generated by endogenous covariates, by means of denoising. We introduce a multifocal variant of the local GMM (MFGMM) estimator to establish jointly the entire parameter set asymptotic properties. Using Monte Carlo simulations attest to very high accuracy of our offered semiparametric JPEG IV estimator as well as high efficiency as reflected by √n consistency. These results have emerged from utilizing 2,000,000 different distribution functions, generating 100 million realizations to construct the various data sets.


Semiparametric Fourier-Dependent Sieve IV Estimator (SPIV) For Truncated Data

Semiparametric Fourier-Dependent Sieve IV Estimator (SPIV) For Truncated Data
Author: Nir Billfeld
Publisher:
Total Pages: 29
Release: 2019
Genre:
ISBN:

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The validity of the IV estimator relies on the orthogonality with respect to the random disturbance. However, in cases of endogenously truncated data as well as in other instances (e.g., censored data) which is very frequently the nature of data used in empirical research, there exists severe contamination in the disturbance due to the endogenous selection process. Such a contamination implies that even if the instrumental variable and the random disturbance are unconditionally independent, they are yet conditionally jointly dependent given the selection variable. The rationale is that the endogenous selection process generates a comovement between the IV and the disturbance which is related to the variation in the selection equation's covariates. This contamination propagates additional bias introduced into the parameter estimates of the various covariates. Consequently, not only does the conventional IV not solve the problem it is intended to, but rather it introduces additional bias into the parameter estimates of the various covariates of the substantive equation. Our empirical implementation shows that even under mild correlation between the random disturbances, the resulting bias in the estimated parameter of the endogenous covariate in the substantive equation can amount to almost tenfold the true parameter value. We offer a semiparametric Fourier-dependent Sieve IV (SPIV) estimator correcting for both truncation as well as endogeneity biases. The Fourier estimator is a functional of the orthonormal polynomial sequence family. The most attractive feature of this estimator for our purposes is that it intrinsically prevents potential multicollinearity problems, a feature the kernel estimator does not possess. The proposed estimator removes the hurdle which prevents orthogonality under truncation or other misspecifications. Using Monte Carlo simulations attest to very high accuracy of our offered semiparametric Sieve IV estimator as well as high efficiency as reflected by √n consistency. These results have been verified by utilizing 2,000,000 different distribution functions, generating 100 million realizations to construct the various data sets.


Applied Econometrics with R

Applied Econometrics with R
Author: Christian Kleiber
Publisher: Springer Science & Business Media
Total Pages: 229
Release: 2008-12-10
Genre: Business & Economics
ISBN: 0387773185

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R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.


Data Integration, Manipulation and Visualization of Phylogenetic Trees

Data Integration, Manipulation and Visualization of Phylogenetic Trees
Author: Guangchuang Yu
Publisher:
Total Pages: 0
Release: 2022
Genre: Business & Economics
ISBN: 9781032245546

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Data Integration, Manipulation and Visualization of Phylogenetic Trees introduces and demonstrates data integration, manipulation and visualization of phylogenetic trees using a suite of R packages, tidytree, treeio, ggtree and ggtreeExtra. Using the most comprehensive packages for phylogenetic data integration and visualization, contains numerous examples that can be used for teaching and learning. Ideal for undergraduate readers and researchers with a working knowledge of R and ggplot2. Key Features: Manipulating phylogenetic tree with associated data using tidy verbs Integrating phylogenetic data from diverse sources Visualizing phylogenetic data using grammar of graphics


Foundations and Applications of Statistics

Foundations and Applications of Statistics
Author: Randall Pruim
Publisher: American Mathematical Soc.
Total Pages: 842
Release: 2018-04-04
Genre: Computers
ISBN: 1470428482

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Foundations and Applications of Statistics simultaneously emphasizes both the foundational and the computational aspects of modern statistics. Engaging and accessible, this book is useful to undergraduate students with a wide range of backgrounds and career goals. The exposition immediately begins with statistics, presenting concepts and results from probability along the way. Hypothesis testing is introduced very early, and the motivation for several probability distributions comes from p-value computations. Pruim develops the students' practical statistical reasoning through explicit examples and through numerical and graphical summaries of data that allow intuitive inferences before introducing the formal machinery. The topics have been selected to reflect the current practice in statistics, where computation is an indispensible tool. In this vein, the statistical computing environment R is used throughout the text and is integral to the exposition. Attention is paid to developing students' mathematical and computational skills as well as their statistical reasoning. Linear models, such as regression and ANOVA, are treated with explicit reference to the underlying linear algebra, which is motivated geometrically. Foundations and Applications of Statistics discusses both the mathematical theory underlying statistics and practical applications that make it a powerful tool across disciplines. The book contains ample material for a two-semester course in undergraduate probability and statistics. A one-semester course based on the book will cover hypothesis testing and confidence intervals for the most common situations. In the second edition, the R code has been updated throughout to take advantage of new R packages and to illustrate better coding style. New sections have been added covering bootstrap methods, multinomial and multivariate normal distributions, the delta method, numerical methods for Bayesian inference, and nonlinear least squares. Also, the use of matrix algebra has been expanded, but remains optional, providing instructors with more options regarding the amount of linear algebra required.


The Birnbaum-Saunders Distribution

The Birnbaum-Saunders Distribution
Author: Victor Leiva
Publisher: Academic Press
Total Pages: 156
Release: 2015-10-26
Genre: Mathematics
ISBN: 0128038276

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The Birnbaum-Saunders Distribution presents the statistical theory, methodology, and applications of the Birnbaum-Saunders distribution, a very flexible distribution for modeling different types of data (mainly lifetime data). The book describes the most recent theoretical developments of this model, including properties, transformations and related distributions, lifetime analysis, and shape analysis. It discusses methods of inference based on uncensored and censored data, goodness-of-fit tests, and random number generation algorithms for the Birnbaum-Saunders distribution, also presenting existing and future applications. Introduces inference in the Birnbaum-Saunders distribution Provides a comprehensive review of the statistical theory and methodology of the Birnbaum-Distribution Discusses different applications of the Birnbaum-Saunders distribution Explains characterization and the lifetime analysis


Finding Groups in Data

Finding Groups in Data
Author: Leonard Kaufman
Publisher: Wiley-Interscience
Total Pages: 376
Release: 1990-03-22
Genre: Mathematics
ISBN:

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Partitioning around medoids (Program PAM). Clustering large applications (Program CLARA). Fuzzy analysis (Program FANNY). Agglomerative Nesting (Program AGNES). Divisive analysis (Program DIANA). Monothetic analysis (Program MONA). Appendix.


Applied Survival Analysis

Applied Survival Analysis
Author: David W. Hosmer, Jr.
Publisher: John Wiley & Sons
Total Pages: 285
Release: 2011-09-23
Genre: Mathematics
ISBN: 1118211588

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THE MOST PRACTICAL, UP-TO-DATE GUIDE TO MODELLING AND ANALYZING TIME-TO-EVENT DATA—NOW IN A VALUABLE NEW EDITION Since publication of the first edition nearly a decade ago, analyses using time-to-event methods have increase considerably in all areas of scientific inquiry mainly as a result of model-building methods available in modern statistical software packages. However, there has been minimal coverage in the available literature to9 guide researchers, practitioners, and students who wish to apply these methods to health-related areas of study. Applied Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical, epidemiological, biostatistical, and other health-related research. This book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory. It offers a clear and accessible presentation of modern modeling techniques supplemented with real-world examples and case studies. Key topics covered include: variable selection, identification of the scale of continuous covariates, the role of interactions in the model, assessment of fit and model assumptions, regression diagnostics, recurrent event models, frailty models, additive models, competing risk models, and missing data. Features of the Second Edition include: Expanded coverage of interactions and the covariate-adjusted survival functions The use of the Worchester Heart Attack Study as the main modeling data set for illustrating discussed concepts and techniques New discussion of variable selection with multivariable fractional polynomials Further exploration of time-varying covariates, complex with examples Additional treatment of the exponential, Weibull, and log-logistic parametric regression models Increased emphasis on interpreting and using results as well as utilizing multiple imputation methods to analyze data with missing values New examples and exercises at the end of each chapter Analyses throughout the text are performed using Stata® Version 9, and an accompanying FTP site contains the data sets used in the book. Applied Survival Analysis, Second Edition is an ideal book for graduate-level courses in biostatistics, statistics, and epidemiologic methods. It also serves as a valuable reference for practitioners and researchers in any health-related field or for professionals in insurance and government.


EnvStats

EnvStats
Author: Steven P. Millard
Publisher: Springer Science & Business Media
Total Pages: 305
Release: 2013-10-16
Genre: Computers
ISBN: 1461484561

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This book describes EnvStats, a new comprehensive R package for environmental statistics and the successor to the S-PLUS module EnvironmentalStats for S-PLUS (first released in 1997). EnvStats and R provide an open-source set of powerful functions for performing graphical and statistical analyses of environmental data, bringing major environmental statistical methods found in the literature and regulatory guidance documents into one statistical package, along with an extensive hypertext help system that explains what these methods do, how to use these methods, and where to find them in the environmental statistics literature. EnvStats also includes numerous built-in data sets from regulatory guidance documents and the environmental statistics literature. This book shows how to use EnvStats and R to easily: * graphically display environmental data * plot probability distributions * estimate distribution parameters and construct confidence intervals on the original scale for commonly used distributions such as the lognormal and gamma, as well as do this nonparametrically * estimate and construct confidence intervals for distribution percentiles or do this nonparametrically (e.g., to compare to an environmental protection standard) * perform and plot the results of goodness-of-fit tests * compute optimal Box-Cox data transformations * compute prediction limits and simultaneous prediction limits (e.g., to assess compliance at multiple sites for multiple constituents) * perform nonparametric estimation and test for seasonal trend (even in the presence of correlated observations) * perform power and sample size computations and create companion plots for sampling designs based on confidence intervals, hypothesis tests, prediction intervals, and tolerance intervals * deal with non-detect (censored) data * perform Monte Carlo simulation and probabilistic risk assessment * reproduce specific examples in EPA guidance documents EnvStats combined with other R packages (e.g., for spatial analysis) provides the environmental scientist, statistician, researcher, and technician with tools to “get the job done!”


The Black-Scholes Model

The Black-Scholes Model
Author: Marek Capiński
Publisher: Cambridge University Press
Total Pages: 179
Release: 2012-09-13
Genre: Business & Economics
ISBN: 1107001692

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Master the essential mathematical tools required for option pricing within the context of a specific, yet fundamental, pricing model.