Kernel smoothing has greatly evolved since its inception to become an essential methodology in the Data Science tool kit for the 21st century. Its widespread adoption is due to its fundamental role for multivariate exploratory data analysis, as well as the crucial role it plays in composite solution
Kernel Smoothing: Principles, Methods and Applications
β Scribed by Sucharita Ghosh
- Publisher
- Wiley
- Year
- 2018
- Tongue
- English
- Leaves
- 267
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Subjects
Probability & Statistics;Applied;Mathematics;Science & Math;Statistics;Mathematics;Science & Mathematics;New, Used & Rental Textbooks;Specialty Boutique
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<p>The heart of the book is the development of a short-time asymptotic expansion for the heat kernel. This is explained in detail and explicit examples of some advanced calculations are given. In addition some advanced methods and extensions, including path integrals, jump diffusion and others are p
<p>The heart of the book is the development of a short-time asymptotic expansion for the heat kernel. This is explained in detail and explicit examples of some advanced calculations are given. In addition some advanced methods and extensions, including path integrals, jump diffusion and others are p
A general class of powerful and flexible modeling techniques, spline smoothing has attracted a great deal of research attention in recent years and has been widely used in many application areas, from medicine to economics. Smoothing Splines: Methods and Applications covers basic smoothing spline mo
Methods of kernel estimates represent one of the most effective nonparametric smoothing techniques. These methods are simple to understand and they possess very good statistical properties. This book provides a concise and comprehensive overview of statistical theory and in addition, emphasis is giv