Using penalized-distance likelihood functions to analyze high-dimensional sparse/non-sparse data

S. K. Ghoreishi (), Jingjing Wu (), Qingrun Zhang () and Ghazal S. Ghoreishi ()
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S. K. Ghoreishi: University of Qom
Jingjing Wu: University of Calgary
Qingrun Zhang: University of Calgary
Ghazal S. Ghoreishi: Shahid Beheshti University

AStA Advances in Statistical Analysis, 2025, vol. 109, issue 3, No 6, 509-528

Abstract: Abstract In this paper, we define a penalized-distance likelihood function. This function is much more flexible than the available likelihood functions and can be used in many disciplines. Based on this function, we introduce a statistic for hypothesis testing and derive its asymptotic distribution. This statistic can be used to test a partial hypothesis in the parameter space for both non-sparse and sparse high-dimensional data. Relevant Bayesian analysis using the Markov chain Monte Carlo (MCMC) method will be discussed. Finally, we carry out a simulation study and apply our model to a real dataset.

Keywords: Empirical likelihood; Estimating equations; Pseudo-likelihood; Quasi-likelihood; Restricted empirical likelihood (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:alstar:v:109:y:2025:i:3:d:10.1007_s10182-025-00527-4

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DOI: 10.1007/s10182-025-00527-4

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