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About 10,000 papers for “Statistical Methods and Applications”

Statistical methods and computer applications

Arora, PN, Guruprasad, N · CERN Document Server (European Organization for Nuclear Research) · 2009

Some of the exclusive features of the book are: Every concept has been explained with the help of solved examples. Working rules showing the various steps for the applications of formulae have also been given. The diagrams and graphs have been neatly and correctly drawn in such a way that the students have the complete understanding of the problem by simply looking at them. Efforts have been made to make the subject throughly exhaustive and nothing important has been omitted. Answer to all the problems have been throughly checked. It is a user-friendly book containing many, solved problems and

Cited by 0

Propensity Score Analysis: Statistical Methods and Applications

Shenyang Guo, Mark W. Fräser · 2009

List of Tables List of Figures Preface About the Authors Chapter 1: Introduction Observational Studies History and Development Randomized Experiments Why and When a Propensity Score Analysis Is Needed Computing Software Packages Plan of the Book Chapter 2: Counterfactual Framework and Assumptions Causality, Internal Validity, and Threats Counterfactuals and the Neyman-Rubin Counterfactual Framework The Ignorable Treatment Assignment Assumption The Stable Unit Treatment Value Assumption Methods for Estimating Treatment Effects The Underlying Logic of Statistical Inference Types of Treatment Effects Treatment Effect Heterogeneity Heckman's Econometric Model of Causality Conclusion Chapter 3: Conventional Methods for Data Balancing Why Is Data Balancing Necessary? A Heuristic Example Three Methods for Data Balancing Design of the Data Simulation Results of the Data Simulation Implications of the Data Simulation Key Issues Regarding the Application of OLS Regression Conclusion Chapter 4: Sample Selection and Related Models The Sample Selection Model Treatment Effect Model Overview of the Stata Programs and Main Features of treatreg Examples Conclusion Chapter 5: Propensity Score Matching and Related Models Overview The Problem of Dimensionality and the Properties of Propensity Scores Estimating Propensity Scores Matching Postmatching Analysis Propensity Score Matching With Multilevel Data Overview of the Stata and R Programs Examples Conclusion Chapter 6: Propensity Score Subclassification Overview The Overlap Assumption and Methods to Address Its Violation Structural Equation Modeling With Propensity Score Subclassification The Stratification-Multilevel Method Examples Conclusion Chapter 7: Propensity Score Weighting Overview Weighting Estimators Examples Conclusion Chapter 8: Matching Estimators Overview Methods of Matching Estimators Overview of the Stata Program nnmatch Examples Conclusion Chapter 9: Propensity Score Analysis With Nonparametric Regression Overview Methods of Propensity Score Analysis With Nonparametric Regression Overview of the Stata Programs psmatch2 and bootstrap Examples Conclusion Chapter 10: Propensity Score Analysis of Categorical or Continuous Treatments Overview Modeling Doses With a Single Scalar Balancing Score Estimated by an Ordered Logistic Regression Modeling Doses With Multiple Balancing Scores Estimated by a Multinomial Logit Model The Generalized Propensity Score Estimator Overview of the Stata gpscore Program Examples Conclusion Chapter 11: Selection Bias and Sensitivity Analysis Selection Bias: An Overview A Monte Carlo Study Comparing Corrective Models Rosenbaum's Sensitivity Analysis Overview of the Stata Program rbounds Examples Conclusion Chapter 12: Concluding Remarks Common Pitfalls in Observational Studies: A Checklist for Critical Review Approximating Experiments With Propensity Score Approaches Other Advances in Modeling Causality Directions for Future Development References Index

Cited by 1,852

Applications and Comments on Contemporary Statistical Methods

Qiao Xiao-chun · 2005

This paper introduces some most widely used statistical methods in social science in regards to data pattern and structures, including linear regression, categorical data analysis, survival analysis, structural equation models, and multilevel and longitudinal data analysis. Its focus is not on the method itself, but on the structural understanding in application of social statistics in western countries.

Cited by 0

Statistical Methods in Data Science and Applications

Tang, Niansheng, Lee, Shen-Ming · 2024

The rise of big data has significantly elevated the significance of data science, catalyzing extensive research across multiple fields, including mathematics, statistics, computer science, and artificial intelligence. Data science encompasses modeling, computation, and learning processes to transform data into information, information into knowledge, and knowledge into actionable decisions. However, the intricacies of big data pose numerous challenges, such as dealing with missing data, high- and ultra-high-dimensional data, response dependencies, time series analysis, and distributed storage. Existing theories, methods, and algorithms for analyzing big data encounter significant hurdles, especially concerning fundamental statistical concepts like estimation, hypothesis testing, confidence intervals, and variable selection, spanning frequentist and Bayesian approaches. This reprint offers an array of tools within the realm of data science aimed at tackling these challenges. It encompasses various topics, including handling measurement errors or missing data, cognitive diagnosis modeling, constructing credit risk scorecards using logistic regression models, geographically weighted regression modeling, privacy protection practices in data mining, clustering methods, and model selection for high-dimensional datasets. Furthermore, it delves into predicting sensitive features under indirect questioning. These discussions aim to provide valuable tools and examples for the practical application of data science.

Cited by 2Open access

Statistical Methods of SNP Data Analysis and Applications

Alexander Bulinski, Oleg Butkovsky, V. A. Sadovnichy et al. · Open Journal of Statistics · 2012

We develop various statistical methods important for multidimensional genetic data analysis. Theorems justifying application of these methods are established. We concentrate on the multifactor dimensionality reduction, logic regression, random forests, stochastic gradient boosting along with their new modifications. We use complementary approaches to study the risk of complex diseases such as cardiovascular ones. The roles of certain combinations of single nucleotide polymorphisms and non-genetic risk factors are examined. To perform the data analysis concerning the coronary heart disease and myocardial infarction the Lomonosov Moscow State University supercomputer “Chebyshev” was employed.

Cited by 14Open access