Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Bivin Philip Sadler"'
Autor:
Bivin Philip Sadler, S. Lynne Stokes
Publikováno v:
Recent Advances on Sampling Methods and Educational Statistics ISBN: 9783031145247
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dd6659da86c7dd8e6de8a5fab950a8c5
https://doi.org/10.1007/978-3-031-14525-4_12
https://doi.org/10.1007/978-3-031-14525-4_12
Data Science students and practitioners want to find a forecast that “works” and don't want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble modelling for
Autor:
George Casella, Roger Berger
This classic textbook builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are
Autor:
John Kloke, Joseph McKean
Praise for the first edition:“This book would be especially good for the shelf of anyone who already knows nonparametrics, but wants a reference for how to apply those techniques in R.”-The American StatisticianThis thoroughly updated and expande
Autor:
Peng Ding
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 ca
Autor:
Per Kragh Andersen, Henrik Ravn
Multi-state models provide a statistical framework for studying longitudinal data on subjects when focus is on the occurrence of events that the subjects may experience over time. They find application particularly in biostatistics, medicine, and pub
Developed from the authors'graduate-level biostatistics course, Applied Categorical and Count Data Analysis, Second Edition explains how to perform the statistical analysis of discrete data, including categorical and count outcomes. The authors have
Autor:
Steffen Lauritzen
Fundamentals of Mathematical Statistics is meant for a standard one-semester advanced undergraduate or graduate-level course in Mathematical Statistics. It covers all the key topics—statistical models, linear normal models, exponential families, es
This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to unde