"Optimal" One-Sample Distribution-Free Tests and Their Two-Sample Extensions
نویسندگان
چکیده
منابع مشابه
One- and two-sample t tests.
The R function t.test() can be used to perform both one and two sample t-tests on vectors of data. The function contains a variety of options and can be called as follows: > t.test(x, y = NULL, alternative = c("two.sided", "less", "greater"), mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95) Here x is a numeric vector of data values and y is an optional numeric vector of data values...
متن کاملOne-Sample Logrank Tests
Introduction This module computes the sample size and power of the one-sample logrank test which is used to compare the survival curve of a single treatment group to that of a historic control. Such is often the case in clinical phase-II trials with survival endpoints. Accrual time, follow-up time, and hazard rates are parameters that can be set. Several authors have presented sample size formu...
متن کاملThe Optimal Distribution-Free Sample Complexity of Distribution-Dependent Learning
This work establishes a new upper bound on the worst-case number of labeled samples sufficient for PAC learning in the realizable case, if the learning algorithm is allowed dependence on the data distribution, or an additional pool of unlabeled samples. The bound matches known lower bounds up to constant factors. This resolves a long-standing open problem on the sample complexity of distributio...
متن کاملBayesian two-sample tests
In this paper, we present two classes of Bayesian approaches to the twosample problem. Our first class of methods extends the Bayesian t-test to include all parametric models in the exponential family and their conjugate priors. Our second class of methods uses Dirichlet process mixtures (DPM) of such conjugate-exponential distributions as flexible nonparametric priors over the unknown distribu...
متن کاملOptimal kernel choice for large-scale two-sample tests
Given samples from distributions p and q, a two-sample test determines whether to reject the null hypothesis that p = q, based on the value of a test statistic measuring the distance between the samples. One choice of test statistic is the maximum mean discrepancy (MMD), which is a distance between embeddings of the probability distributions in a reproducing kernel Hilbert space. The kernel use...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Mathematical Statistics
سال: 1966
ISSN: 0003-4851
DOI: 10.1214/aoms/1177699603