All of Statistics: A Concise Course in Statistical InferenceSpringer Science & Business Media, 11 de dez. de 2013 - 442 páginas Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data. |
Outras edições - Ver todos
All of Statistics: A Concise Course in Statistical Inference Larry Wasserman Visualização parcial - 2004 |
All of Statistics: A Concise Course in Statistical Inference Larry Wasserman Prévia não disponível - 2010 |
Termos e frases comuns
95 percent confidence algorithm approximate assume bandwidth Bayesian inference Bernoulli(p bootstrap C₁ called causal central limit theorem classifier compute confidence band confidence interval convergence covariates cross-validation d-separated defined Definition delta method denote density estimator distribution error rate Example f(Xi Figure Find finite Fn(x frequentist fx(x graph H₁ Hence histogram Hoeffding's inequality independent inequality kernel large numbers Lemma Let X1 likelihood function linear log-linear model Markov chain mass function matrix minimax Multivariate nonparametric Normal null hypothesis observations p-value P(AB P(Xn parameter percent confidence interval plot Poisson prior probability PROOF random variables reject risk risk function sample space Show simulation standard error Suppose test statistic variance vector versus Wald test wavelets write X₁ Y₁ βο θη θο μο σ² Χη