Distance-based parametric bootstrap tests for clustering of species ranges
نویسندگان
چکیده
منابع مشابه
Consistent bootstrap tests of parametric regression functions
This paper introduces speci"cation tests of parametric mean-regression models. The null hypothesis of interest is that the parametric regression function is correctly speci"ed. The proposed tests are generalizations of the Kolmogorov}Smirnov and Cramer}von Mises tests to the regression framework. They are consistent against all alternatives to the null hypothesis, powerful against 1/Jn local al...
متن کاملConsistent model-specification tests based on parametric bootstrap
In this paper we establish consistent tests of L2-type for the parametric functional form of the conditional mean of time series with values in Rd. A recent result on asymptotic distributions of U -statistics of weakly dependent observations is invoked to obtain the limit distributions of the test statistics. Since the asymptotic distributions depend on unknown parameters in a complicated way, ...
متن کاملParametric bootstrap and approximate tests for two Poisson variates
The parametric bootstrap tests and the asymptotic or approximate tests for detecting difference of two Poisson means are compared. The test statistics used are the Wald statistics with and without log-transformation, the Cox F statistic and the likelihood ratio statistic. It is found that the type I error rate of an asymptotic/approximate test may deviate too much from the nominal significance ...
متن کاملCopula model evaluation based on parametric bootstrap
Copulas are used to model multivariate data as they account for the dependence structure and provide a flexible representation of the multivariate distribution. A great number of copulas has been proposed with various dependence aspects. One important issue is the choice of an appropriate copula from a large set of candidate families to model the data at hand. A large number of copulas are comp...
متن کاملParametric model-based clustering
Parametric, model-based algorithms learn generative models from the data, with each model corresponding to one particular cluster. Accordingly, the model-based partitional algorithm will select the most suitable model for any data object (Clustering step), and will recompute parametric models using data specifically from the corresponding clusters (Maximization step). This Clustering-Maximizati...
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: Computational Statistics & Data Analysis
سال: 2004
ISSN: 0167-9473
DOI: 10.1016/s0167-9473(03)00091-4