10 Best Books on Nonparametric Inference

We have compiled a list of the Best Reference Books on Nonparametric Inference, which are used by students of top universities, and colleges. This will help you choose the right book depending on if you are a beginner or an expert. Here is the complete list of Nonparametric Inference Books with their authors, publishers, and an unbiased review of them as well as links to the Amazon website to directly purchase them. If permissible, you can also download the free PDF books on Nonparametric Inference below.

1."Nonparametric Statistical Inference" by Jean Dickinson Gibbons and Subhabrata Chakraborti “Nonparametric Statistical Inference” Book Review: The contents of this book have been updated and revised based on recent journal articles in the literature. It offers practical guidance on selecting among various nonparametric procedures and includes additional problems and examples. The book covers the most commonly used nonparametric procedures and discusses their assumptions while developing the theory behind them. The techniques are demonstrated using realistic research examples, drawn from the social, behavioral, and life sciences.
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2."Parametric and Nonparametric Inference for Statistical Dynamic Shape Analysis with Applications (SpringerBriefs in Statistics)" by Lara Fontanella and Luigi Ippoliti “Parametric and Nonparametric Inference for Statistical Dynamic Shape Analysis with Applications (SpringerBriefs in Statistics)” Book Review: This book explores how to analyze dynamic shapes, using probability models and nonparametric tests to solve problems. It provides a useful way to understand the overall dynamics of landmark configurations. The book focuses on the use of the Gaussian distribution in the configuration space, with similarity transformations integrated out. It uses the offset-normal shape distribution as a probability model, allowing for inferences for Gaussian processes from configurations onto the shape space.
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3."Nonparametric Inference" by Zakkula Govindarajulu
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4."Nonparametric Inference on Manifolds: With Applications to Shape Spaces (Institute of Mathematical Statistics Monographs)" by Abhishek Bhattacharya and Rabi Bhattacharya “Nonparametric Inference on Manifolds: With Applications to Shape Spaces (Institute of Mathematical Statistics Monographs)” Book Review: This book offers a comprehensive nonparametric statistical theory for manifolds in a systematic manner, with a particular emphasis on shapes. The theory presented in this book has diverse and significant applications in medical diagnostics, image analysis, and machine vision. It demonstrates the effectiveness of the newly developed methods and highlights their superiority over parametric counterparts.
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5."Fundamentals of Nonparametric Bayesian Inference (Cambridge Series in Statistical and Probabilistic Mathematics)" by Subhashis Ghosal and Aad van der Vaart
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6."Nonparametric Bayesian Inference in Biostatistics (Frontiers in Probability and the Statistical Sciences)" by Peter Müller and Riten Mitra
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7."Estimation and Inference in Nonparametric Frontier Models: Recent Developments and Perspectives" by Leopold Simar and Paul W Wilson “Estimation and Inference in Nonparametric Frontier Models: Recent Developments and Perspectives” Book Review: The topic of this book is thoroughly examined to serve as a guide for students and researchers. The book covers widely used nonparametric estimators to estimate productive efficiency without making any statistical inference. It includes statistical properties of these estimators and methods that can be used for making statistical inference. The book establishes a link between frontier estimation and extreme value theory.
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8."Parametric and Nonparametric Inference from Record-Breaking Data (Lecture Notes in Statistics)" by Sneh Gulati and William J Padgett
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9."Nonparametric Inference of Utilites" by Matthias Herfert “Nonparametric Inference of Utilites” Book Review: This book aims to clarify certain aspects of theories that are relevant to understanding the thesis. It provides an overview of inferential statistics, Conjoint Analysis, and selective entropy, as well as a more recent version of it. The book also covers the concept of assumptions as a general inference principle. Furthermore, it presents La Mura’s decision-theoretic entropy, which is a version of entropy used as an inference technique for expected utilities.
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10."Associated Sequences, Demimartingales and Nonparametric Inference (Probability and Its Applications)" by B L S Prakasa Rao “Associated Sequences, Demimartingales and Nonparametric Inference (Probability and Its Applications)” Book Review: This book offers a comprehensive survey of the current findings for related sequences and demimartingales. It focuses specifically on demimartingales and associated processes, providing an in-depth analysis of their probabilistic properties. Additionally, it explores several applications of these results to a variety of problems. The book also covers nonparametric statistical inference techniques for these processes, which are thoroughly examined in the final three chapters. advertisement advertisement
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