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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年5月30日

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  • S. K. Ghoreishi,
  • Jingjing Wu,
  • Qingrun Zhang,
  • Ghazal S. Ghoreishi

摘要

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.