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Statistical Models For Causal Analysis

This volume provides an overview of statistical models commonly used in the causal analyses of non-experimental data in the social and;

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Big Data in Omics and Imaging, Two Volume Set

analysis and the modern advanced statistical methods for big data Provides tools for high dimensional data reduction Discusses searching;

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Cause & Correlation In Biology

of statistical jargon, the concept of testing multivariate causal hypotheses using structural equations and path analysis is demystified. Assuming only a;

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Big Data in Omics and Imaging: Integrated Analysis and Causal Inference

to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism;

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Big Data in Omics and Imaging: Integrated Analysis and Causal Inference

to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism;

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Direction Dependence in Statistical Modeling

incorporates the latest research for the statistical analysis of hypotheses that are compatible with the causal direction of dependence of variable;

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Agent-based Models and Causal Inference

Inference connects the literature from various fields, including causality, social mechanisms, statistical and experimental methods for causal;

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Statistical Methods for Drug Safety

selection bias in the analysis of large-scale longitudinal observational data. Choose the Right Statistical Approach for Analyzing Your Drug Safety;

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Statistical Methods for Drug Safety

selection bias in the analysis of large-scale longitudinal observational data. Choose the Right Statistical Approach for Analyzing Your Drug;

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Time Series Data Analysis Using EViews

Do you want to recognize the most suitable models for analysis of statistical data sets? This book provides a hands-on practical guide to;

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Sense and Nonsense of Statistical Inference: Controversy

epistemology; reveals fallacious practices in statistical causal inference, stressing the misuse of regression models and time-series analysis as;

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Sense and Nonsense of Statistical Inference

epistemology; reveals fallacious practices in statistical causal inference, stressing the misuse of regression models and time-series analysis as;

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Introduction to Statistical Decision Theory

Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis provides the theoretical background to approach decision;

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Introduction to Statistical Decision Theory

Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis provides the theoretical background to approach decision;

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Event History Analysis With Stata

Nowadays, event history analysis can draw on a well-established set of statistical tools for the description and causal analysis of event;

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Event History Analysis With Stata

Nowadays, event history analysis can draw on a well-established set of statistical tools for the description and causal analysis of event;

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Statistical Approaches to Causal Analysis

A practical, up-to-date, step-by-step guidance on causal analysis; which features worked example datasets throughout to see methods;

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Propensity Score Analysis: Statistical Methods and Applications

Provides readers with a systematic review of the origins, history, and statistical foundations of Propensity Score Analysis (PSA) and;

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Statistical Models and Methods for Reliability and Survival Analysis

Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as;

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Causal Analysis in Biomedicine and Epidemiology

Provides current models, tools, and examples for the formulation and evaluation of scientific hypotheses in causal terms. Introduces a new;

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Causal Analysis in Biomedicine and Epidemiology

Provides current models, tools, and examples for the formulation and evaluation of scientific hypotheses in causal terms. Introduces a new;

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Statistical Rethinking

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from;

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Handbook of Graphical Models

, directed graphical models allow intuitive causal interpretations and have become a cornerstone for causal inference. While there exist a number;

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Handbook of Graphical Models

, directed graphical models allow intuitive causal interpretations and have become a cornerstone for causal inference. While there exist a number;

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Design and Analysis of Time Series Experiments

Design and Analysis of Time Series Experiments presents the elements of statistical time series analysis while also addressing recent;

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Advanced Statistics for Testing Assumed Causal Relationships

This book concentrates on linear regression, path analysis and logistic regressions, the most used statistical techniques for the test;

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Elements of Causal Inference

offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models;

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