A semiparametric density estimator based on elliptical distributions
نویسندگان
چکیده
منابع مشابه
Locally Efficient Semiparametric Estimators for Generalized Skew-Elliptical Distributions
We consider a class of generalized skew-normal distributions that is useful for selection modeling and robustness analysis and derive a class of semiparametric estimators for the location and scale parameters of the central part of the model. We show that these estimators are consistent and asymptotically normal. We present the semiparametric efficiency bound and derive the locally efficient es...
متن کاملSemiparametric Inference Based on a Class of Zero-Altered Distributions
In modeling count data collected from manufacturing processes, economic series, disease outbreaks and ecological surveys, there are usually a relatively large or small number of zeros compared to positive counts. Such low or high frequencies of zero counts often require the use of under or over dispersed probability models for the underlying data generating mechanism. The commonly used models s...
متن کاملThe semiparametric case-only estimator.
We propose a semiparametric case-only estimator of multiplicative gene-environment or gene-gene interactions, under the assumption of conditional independence of the two factors given a vector of potential confounding variables. Our estimator yields valid inferences on the interaction function if either but not necessarily both of two unknown baseline functions of the confounders is correctly m...
متن کاملQuantile-based inference for elliptical distributions
We estimate the parameters of an elliptical distribution by means of a multivariate extension of the Method of Simulated Quantiles (MSQ) of Dominicy and Veredas (2010). The multivariate extension entails the challenge of the construction of a function of quantiles that is informative about the covariation parameters. The interquantile range of a projection of pairwise random variables onto the ...
متن کاملA Semiparametric Estimator of Random Eeects
The use of a linear estimator to estimate random eeects in a Mixed Model is not necessarily optimal if the prior distribution is non-normal. Either a frequentist or Bayesian approach leads to the Best Linear Unbiased Predictor (BLUP), but an empirical Bayes approach produces a multivariate, non-linear, single-pass kernel-based estimator (the General Empirical Bayes or GEB es-timator) that allow...
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 Multivariate Analysis
سال: 2005
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2003.09.007