Cristina Tortora,Antonio Punzo,Brian C Franczak
Cristina Tortora
Model-based clustering is a powerful approach used in data analysis to unveil underlying patterns or groups within a data set. However, when applied to clusters that exhibit skewness, heavy tails, or both, the classification of data points ...
Maximum likelihood estimation under the Emax model: existence, geometry and efficiency [0.03%]
EMax模型下的最大似然估计：存在性、几何特性及有效性
Giacomo Aletti,Nancy Flournoy,Caterina May et al.
Giacomo Aletti et al.
This study focuses on the estimation of the Emax dose-response model, a widely utilized framework in clinical trials, experiments in pharmacology, agriculture, environmental science, and more. Existing challenges in obtaining maximum likeli...
Local linear smoothing for regression surfaces on the simplex using Dirichlet kernels [0.03%]
Dirichlet核的单纯形上的回归表面的局部线性平滑方法
Christian Genest,Frédéric Ouimet
Christian Genest
This paper introduces a local linear smoother for regression surfaces on the simplex. The estimator solves a least-squares regression problem weighted by a locally adaptive Dirichlet kernel, ensuring good boundary properties. Asymptotic res...
Statistical Inferences for Missing Response Problems Based on Modified Empirical Likelihood [0.03%]
基于修正经验似然的缺失响应问题的统计推断
Sima Sharghi,Kevin Stoll,Wei Ning
Sima Sharghi
In this paper, we advance the application of empirical likelihood (EL) for missing response problems. Inspired by remedies for the shortcomings of EL for parameter hypothesis testing, we modify the EL approach used for statistical inference...
On some problems of Bayesian region construction with guaranteed coverages [0.03%]
具有保证覆盖率的贝叶斯置信区域构造中的若干问题研究
Michael Evans,Miaoshiqi Liu,Michael Moon et al.
Michael Evans et al.
The general problem of constructing regions that have a guaranteed coverage probability for an arbitrary parameter of interest ψ ∈ Ψ is considered. The regions developed are Bayesian in nature and the coverage probabilities...
Timo Dimitriadis,Tobias Fissler,Johanna Ziegel
Timo Dimitriadis
Given a statistical functional of interest such as the mean or median, a (strict) identification function is zero in expectation at (and only at) the true functional value. Identification functions are key objects in forecast validation, st...
Discrimination between Gaussian process models: active learning and static constructions [0.03%]
高斯过程模型的区分：主动学习和静态构造
Elham Yousefi,Luc Pronzato,Markus Hainy et al.
Elham Yousefi et al.
The paper covers the design and analysis of experiments to discriminate between two Gaussian process models with different covariance kernels, such as those widely used in computer experiments, kriging, sensor location and machine learning....
Finite mixtures of mean-parameterized Conway-Maxwell-Poisson models [0.03%]
均值参数化Conway-Maxwell-Poisson混合模型
Dongying Zhan,Derek S Young
Dongying Zhan
For modeling count data, the Conway-Maxwell-Poisson (CMP) distribution is a popular generalization of the Poisson distribution due to its ability to characterize data over- or under-dispersion. While the classic parameterization of the CMP ...
A method of correction for heaping error in the variables using validation data [0.03%]
基于验证数据修正变量齐整误差的方法研究
Amar S Ahmad,Munther Al-Hassan,Hamid Y Hussain et al.
Amar S Ahmad et al.
When self-reported data are used in statistical analysis to estimate the mean and variance, as well as the regression parameters, the estimates tend, in many cases, to be biased. This is because interviewees have a tendency to heap their an...
Ori Davidov,Tamás Rudas
Ori Davidov
The use of historical, i.e., already existing, estimates in current studies is common in a wide variety of application areas. Nevertheless, despite their routine use, the uncertainty associated with historical estimates is rarely properly a...