Exact confidence sets and goodness-of-fit methods for stable distributions
نویسندگان
چکیده
منابع مشابه
Goodness-of-Fit Tests for Symmetric Stable Distributions – Empirical Characteristic Function Approach
We consider goodness-of-fit tests of symmetric stable distributions based on weighted integrals of the squared distance between the empirical characteristic function of the standardized data and the characteristic function of the standard symmetric stable distribution with the characteristic exponent α estimated from the data. We treat α as an unknown parameter, but for theoretical simplicity w...
متن کاملGoodness-of-fit and confidence intervals of approximate models
To test whether the model fits the data well, a goodness-of-fit (GOF) test can be used. The chi-square GOF test is often used to test the null hypothesis that a function describes the mean of the data well. The null hypothesis with this test is rejected too often, however, because the nominal significance level (usually 0.05) is exceeded. Alternatively, the level of Hotelling’s test is accurate...
متن کاملAn empirical goodness-of-fit test for multivariate distributions
An empirical test is presented by which one may determine whether a specified multivariate probability model is suitable to describe the underlying distribution of a set of observations. This test is based on the premise that, given any probability distribution, the Mahalanobis distances corresponding to data generated from that distribution will likewise follow a distinct distribution that can...
متن کاملNew Goodness-of-fit Tests for Pareto Distributions*
A new approach to goodness-of-fit for Pareto distributions is introduced. Based on Euclidean distances between sample elements, the family of statistics and tests is indexed by an exponent in (0,2) on Euclidean distance. The corresponding tests are statistically consistent and have excellent performance when applied to heavy-tailed distributions. The exponent can be tailored to the particular P...
متن کاملNonparametric Goodness-of-Fit Tests for Discrete Null Distributions
Methodology extending nonparametric goodness-of-fit tests to discrete null distributions has existed for several decades. However, modern statistical software has generally failed to provide this methodology to users. We offer a revision of R’s ks.test() function and a new cvm.test() function that fill this need in the R language for two of the most popular nonparametric goodness-of-fit tests. ...
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: Journal of Econometrics
سال: 2014
ISSN: 0304-4076
DOI: 10.1016/j.jeconom.2014.02.003