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A dual active set algorithm for optimal sparse convex regression
Vestnik Samarskogo Gosudarstvennogo Tehničeskogo Universiteta. Seriâ: Fiziko-Matematičeskie Nauki, 2019 The shape-constrained problems in statistics have attracted much attention in recent decades. One of them is the task of finding the best fitting monotone regression. The problem of constructing monotone regression (also called isotonic regression) is to
Aleksandr A. Gudkov +3 more
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Mixture Modeling of Time-to-Event Data in the Proportional Odds Model
Mathematics, 2022 Subgroup analysis with survival data are most essential for detailed assessment of the risks of medical products in heterogeneous population subgroups. In this paper, we developed a semiparametric mixture modeling strategy in the proportional odds model ...
Xifen Huang +4 more
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Spatio-Temporal Forecasting of Global Horizontal Irradiance Using Bayesian Inference
Applied Sciences, 2022 Accurate global horizontal irradiance (GHI) forecasting promotes power grid stability. Most of the research on solar irradiance forecasting has been based on a single-site analysis.
Caston Sigauke +2 more
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Mapping wind erosion hazard with regression-based machine learning algorithms
Scientific Reports, 2020 Land susceptibility to wind erosion hazard in Isfahan province, Iran, was mapped by testing 16 advanced regression-based machine learning methods: Robust linear regression (RLR), Cforest, Non-convex penalized quantile regression (NCPQR), Neural network ...
Hamid Gholami +3 more
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Mathematics, 2022 Clustered and heterogeneous interval-censored data occur in many fields such as medical studies. For example, in a migraine study with the Netherlands Twin Registry, the information including time to diagnosis of migraine and gender was collected for ...
Xifen Huang, Jinfeng Xu
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Monotonicity is a key feature of genotype-phenotype maps
Frontiers in Genetics, 2013 It was recently shown that monotone gene action, i.e. order-preservation between allele content and corresponding genotypic values in the mapping from genotypes to phenotypes, is a prerequisite for achieving a predictable parent-offspring relationship ...
Arne Bjørke Gjuvsland +4 more
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bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors
Journal of Statistical Software, 2019 The Bayesian spectral analysis model (BSAM) is a powerful tool to deal with semiparametric methods in regression and density estimation based on the spectral representation of Gaussian process priors. The bsamGP package for R provides a comprehensive set
Seongil Jo +3 more
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Journal of Applied Mathematics, 2014 The imputation of missing data is often a crucial step in the analysis of survey data. This study reviews typical problems with missing data and discusses a method for the imputation of missing survey data with a large number of categorical variables ...
Machelle D. Wilson, Kerstin Lueck
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Robust and Sparse Regression via γ-Divergence
Entropy, 2017 In high-dimensional data, many sparse regression methods have been proposed. However, they may not be robust against outliers. Recently, the use of density power weight has been studied for robust parameter estimation, and the corresponding divergences ...
Takayuki Kawashima, Hironori Fujisawa
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Bayesian Semiparametric Regression Analysis of Multivariate Panel Count Data
Stats, 2022 Panel count data often occur in a long-term recurrent event study, where the exact occurrence time of the recurrent events is unknown, but only the occurrence count between any two adjacent observation time points is recorded.
Chunling Wang, Xiaoyan Lin
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