A guide to the systematic analytical results for ridge, LASSO, preliminary test, and Stein-type estimators with applications Theory of Ridge Regression Estimation with Applications offers a comprehensive guide to the theory and methods of estimation. Ridge regression and LASSO are at the center of all penalty estimators in a range of standard models that are used in many applied statistical analyses. Written by noted experts in the field, the book contains a thorough introduction to penalty and shrinkage estimation and explores the role that ridge, LASSO, and logistic regression play in the computer intensive area of neural network and big data analysis. Designed to be accessible, the book presents detailed coverage of the basic terminology related to various models such as the location and simple linear models, normal and rank theory-based ridge, LASSO, preliminary test and Stein-type estimators. The authors also include problem sets to enhance learning. This book is a volume in the Wiley Series in Probability and Statistics series that provides essential and invaluable reading for all statisticians. This important resource: Offers theoretical coverage and computer-intensive applications of the procedures presented Contains solutions and alternate methods for prediction accuracy and selecting model procedures Presents the first book to focus on ridge regression and unifies past research with current methodology Uses R throughout the text and includes a companion website containing convenient data sets Written for graduate students, practitioners, and researchers in various fields of science, Theory of Ridge Regression Estimation with Applications is an authoritative guide to the theory and methodology of statistical estimation.
Rank-Based Methods for Shrinkage and Selection A practical and hands-on guide to the theory and methodology of statistical estimation based on rank Robust statistics is an important field in contemporary mathematics and applied statistical methods. Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning describes techniques to produce higher quality data analysis in shrinkage and subset selection to obtain parsimonious models with outlier-free prediction. This book is intended for statisticians, economists, biostatisticians, data scientists and graduate students. Rank-Based Methods for Shrinkage and Selection elaborates on rank-based theory and application in machine learning to robustify the least squares methodology. It also includes: Development of rank theory and application of shrinkage and selection Methodology for robust data science using penalized rank estimators Theory and methods of penalized rank dispersion for ridge, LASSO and Enet Topics include Liu regression, high-dimension, and AR(p) Novel rank-based logistic regression and neural networks Problem sets include R code to demonstrate its use in machine learning
This book summarizes the results of various models under normal theory with a brief review of the literature. Statistical Inference for Models with Multivariate t-Distributed Errors: Includes a wide array of applications for the analysis of multivariate observations Emphasizes the development of linear statistical models with applications to engineering, the physical sciences, and mathematics Contains an up-to-date bibliography featuring the latest trends and advances in the field to provide a collective source for research on the topic Addresses linear regression models with non-normal errors with practical real-world examples Uniquely addresses regression models in Student's t-distributed errors and t-models Supplemented with an Instructor's Solutions Manual, which is available via written request by the Publisher
A guide to the systematic analytical results for ridge, LASSO, preliminary test, and Stein-type estimators with applications Theory of Ridge Regression Estimation with Applications offers a comprehensive guide to the theory and methods of estimation. Ridge regression and LASSO are at the center of all penalty estimators in a range of standard models that are used in many applied statistical analyses. Written by noted experts in the field, the book contains a thorough introduction to penalty and shrinkage estimation and explores the role that ridge, LASSO, and logistic regression play in the computer intensive area of neural network and big data analysis. Designed to be accessible, the book presents detailed coverage of the basic terminology related to various models such as the location and simple linear models, normal and rank theory-based ridge, LASSO, preliminary test and Stein-type estimators. The authors also include problem sets to enhance learning. This book is a volume in the Wiley Series in Probability and Statistics series that provides essential and invaluable reading for all statisticians. This important resource: Offers theoretical coverage and computer-intensive applications of the procedures presented Contains solutions and alternate methods for prediction accuracy and selecting model procedures Presents the first book to focus on ridge regression and unifies past research with current methodology Uses R throughout the text and includes a companion website containing convenient data sets Written for graduate students, practitioners, and researchers in various fields of science, Theory of Ridge Regression Estimation with Applications is an authoritative guide to the theory and methodology of statistical estimation.
Rank-Based Methods for Shrinkage and Selection A practical and hands-on guide to the theory and methodology of statistical estimation based on rank Robust statistics is an important field in contemporary mathematics and applied statistical methods. Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning describes techniques to produce higher quality data analysis in shrinkage and subset selection to obtain parsimonious models with outlier-free prediction. This book is intended for statisticians, economists, biostatisticians, data scientists and graduate students. Rank-Based Methods for Shrinkage and Selection elaborates on rank-based theory and application in machine learning to robustify the least squares methodology. It also includes: Development of rank theory and application of shrinkage and selection Methodology for robust data science using penalized rank estimators Theory and methods of penalized rank dispersion for ridge, LASSO and Enet Topics include Liu regression, high-dimension, and AR(p) Novel rank-based logistic regression and neural networks Problem sets include R code to demonstrate its use in machine learning
The memoir of two young dancers from vastly different cultures, tells of their ill-fated marriage during Iran's Islamic Revolution. For several years Mohammad and I had wanted to write about a significantly dramatic part of our personal journey through life. We eventually put pen to paper after meeting up again in England. 'For the Love of Mohammad A Memoir' is a true story of love and all its complexities. It is also the story of a young English girl living in a Middle Eastern country which is torn apart by an Islamic revolution and war, and the story of a young Iranian man struggling to come to terms with his homosexuality after being manipulated into marriage whilst being deeply in love with another man. Mohammad and I feel our story is also more than just a story of our love. As the world continues to struggle with human rights issues, in particular the human rights of the LGBTQ communities in such countries as Iran, Uganda, Pakistan, Nigeria, India, Russia and often in our own backyard; Mohammad and I believe that our story speaks out for those who have their voices silenced; contributes to the awareness of their situation; and carries with it a message of hope and courage by demonstrating how, with the power of love, courage and understanding, life's adversities can be overcome.
A city of stories – short, fragmented, amorphous, and at times contradictory – Tehran is an impossible tale to tell. For the capital city of one of the most powerful nations in the Middle East, its literary output is rarely acknowledged in the West. This unique celebration of its writing brings together ten stories exploring the tensions and pressures that make the city what it is: tensions between the public and the private, pressures from without – judgemental neighbours, the expectations of religion and society – and from within – family feuds, thwarted ambitions, destructive relationships. The psychological impact of these pressures manifests in different ways: a man wakes up to find a stranger relaxing in his living room and starts to wonder if this is his house at all; a struggling writer decides only when his girlfriend breaks his heart will his work have depth... In all cases, coping with these pressures leads us, the readers, into an unexpected trove of cultural treasures – like the burglar, in one story, descending into the basement of a mysterious antique collector’s house – treasures of which we, in the West, are almost wholly ignorant. Translated by: Sara Khalili, Sholeh Wolpé, Alireza Abiz, Caroline Croskery, Farzaneh Doosti, Shahab Vaezzadeh, Niloufar Talebi, Lida Nosrati, Susan Niazi and Poupeh Missaghi. Foreword by Orkideh Behrouzan. Developed in partnership with Visiting Arts. 'The aesthetic sensibility of Iranian culture appears, to the West, as mainly pre-modern, if not actually anti-modern... The fiction showcased in The Book of Tehran is a welcome corrective to this tendency... These stories feel decidedly contemporary in style and subject matter alike, with their protagonists' inner lives and interpersonal relationships at the fore.' - The Times Literary Supplement 'Fiction exploring the interior life of contemporary Iranians is not well represented in translations readily available in the West. The Book of Tehran aims to begin to redress the shortage...' - Asian Review of Books
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.