Peng Ding – författare

This book introduces experiential knowledge of the intersectionality of disability, sexuality, and gender equality issues. Scholars and disabled persons’ organizations in different Asian countries such as China, Vietnam, Myanmar, Nepal, and Japan have contributed to the book. It is a preliminary introduction of the frontline practice of Asian disability activism and the experience of women and LGBTIQ people with disabilities. It presents the direct participation of disability advocates in mapping how both women with disabilities and LGBTIQ individuals with disabilities realize their rights such as identity, work rights, personal safety, and sexual rights. Studies presented here explore the experience of empowering diverse disability groups and advocating for equality and non-discrimination. It explains how to use the leverage of the Convention on the Rights of Persons with Disabilities (CRPD) for further human rights campaigns in a broader context for disadvantaged groups.This collection is the product of a participatory research project, which aims to increase the capabilities of local disabled persons’ organizations and NGOs in utilizing human rights laws and encourage dialogue and collaboration between academia, people with disabilities, and human rights advocates. It will be essential reading for academics, researchers, policy-makers, and campaign groups.

This book introduces experiential knowledge of the intersectionality of disability, sexuality, and gender equality issues. Scholars and disabled persons’ organizations in different Asian countries such as China, Vietnam, Myanmar, Nepal, and Japan have contributed to the book. It is a preliminary introduction of the frontline practice of Asian disability activism and the experience of women and LGBTIQ people with disabilities. It presents the direct participation of disability advocates in mapping how both women with disabilities and LGBTIQ individuals with disabilities realize their rights such as identity, work rights, personal safety, and sexual rights. Studies presented here explore the experience of empowering diverse disability groups and advocating for equality and non-discrimination. It explains how to use the leverage of the Convention on the Rights of Persons with Disabilities (CRPD) for further human rights campaigns in a broader context for disadvantaged groups.This collection is the product of a participatory research project, which aims to increase the capabilities of local disabled persons’ organizations and NGOs in utilizing human rights laws and encourage dialogue and collaboration between academia, people with disabilities, and human rights advocates. It will be essential reading for academics, researchers, policy-makers, and campaign groups.

The past decade has witnessed an explosion of interest in research and education in causal inference, due to its wide applications in biomedical research, social sciences, artificial intelligence etc. This textbook, based on the author's course on causal inference at UC Berkeley taught over the past seven years, only requires basic knowledge of probability theory, statistical inference, and linear and logistic regressions. It assumes minimal knowledge of causal inference, and reviews basic probability and statistics in the appendix. It covers causal inference from a statistical perspective and includes examples and applications from biostatistics and econometrics.Key Features:All R code and data sets available at Harvard Dataverse.Solutions manual available for instructors upon request from the author.Includes over 100 exercises.This book is suitable for an advanced undergraduate or graduate-level course on causal inference, or postgraduate and PhD-level course in statistics and biostatistics departments.

The linear model and its extensions play fundamental roles in both theoretical and applied statistics, due to their transparency and interpretability in modeling empirical data. This textbook, based on the author’s course on linear modeling at UC Berkeley taught over the past ten years, only requires basic knowledge of linear algebra, probability theory, and statistical inference. It assumes minimal knowledge of linear modeling, and reviews basic linear algebra, probability, and statistics in the appendix. It covers linear regression, logistic regression, Poisson regression, generalized estimating equation, quantile regression, and Cox regression, which are widely used statistical models across many areas. It balances rigorous theory, simulation, and data analysis.Key Features:- All R code and data sets available at Harvard Dataverse.- Includes over 200 exercises.- Solutions manual available for instructors, upon request from the author.This book is suitable for advanced undergraduate or graduate-level courses on linear modeling, or graduate-level courses on generalized linear modeling. It can also be used as a reference for researchers who are searching for basic properties of the linear model and its extensions.

The linear model and its extensions play fundamental roles in both theoretical and applied statistics, due to their transparency and interpretability in modeling empirical data. This textbook, based on the author’s course on linear modeling at UC Berkeley taught over the past ten years, only requires basic knowledge of linear algebra, probability theory, and statistical inference. It assumes minimal knowledge of linear modeling, and reviews basic linear algebra, probability, and statistics in the appendix. It covers linear regression, logistic regression, Poisson regression, generalized estimating equation, quantile regression, and Cox regression, which are widely used statistical models across many areas. It balances rigorous theory, simulation, and data analysis.Key Features:- All R code and data sets available at Harvard Dataverse.- Includes over 200 exercises.- Solutions manual available for instructors, upon request from the author.This book is suitable for advanced undergraduate or graduate-level courses on linear modeling, or graduate-level courses on generalized linear modeling. It can also be used as a reference for researchers who are searching for basic properties of the linear model and its extensions.

The past decade has witnessed an explosion of interest in research and education in causal inference, due to its wide applications in biomedical research, social sciences, artificial intelligence etc. This textbook, based on the author's course on causal inference at UC Berkeley taught over the past seven years, only requires basic knowledge of probability theory, statistical inference, and linear and logistic regressions. It assumes minimal knowledge of causal inference, and reviews basic probability and statistics in the appendix. It covers causal inference from a statistical perspective and includes examples and applications from biostatistics and econometrics.Key Features:All R code and data sets available at Harvard Dataverse.Solutions manual available for instructors upon request from the author.Includes over 100 exercises.This book is suitable for an advanced undergraduate or graduate-level course on causal inference, or postgraduate and PhD-level course in statistics and biostatistics departments.