Reinforcement learning is one of the paradigms and methodologies of machine learning developed in the computational intelligence community. Reinforcement learning algorithms present a major challenge in complex dynamics recently. In the perspective of variable selection, we often come across situations where too many variables are included in the full model at the initial stage of modeling. Due to a high-dimensional and intractable integral of longitudinal data, likelihood inference is computationally challenging. It can be computationally difficult such as very slow convergence or even nonconvergence, for the computationally intensive methods. Recently, hierarchical likelihood (h-likelihood) plays an important role in inferences for models having unobservable or unobserved random variables. This paper focuses linear models with random effects in the mean structure and proposes a penalized h-likelihood algorithm which incorporates variable selection procedures in the setting of mean modeling via h-likelihood. The penalized h-likelihood method avoids the messy integration for the random effects and is computationally efficient. Furthermore, it demonstrates good performance in relevant-variable selection. Throughout theoretical analysis and simulations, it is confirmed that the penalized h-likelihood algorithm produces good fixed effect estimation results and can identify zero regression coefficients in modeling the mean structure.

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具有随机效应的广义线性回归模型的惩罚h似然变量选择算法

强化学习是在计算智能社区中开发的机器学习的范例和方法之一。强化学习算法最近在复杂的动力学中提出了一个重大挑战。从变量选择的角度来看，我们经常会遇到这样的情况：在建模的初始阶段，完整模型中包含太多变量。由于纵向数据具有高维且难处理的积分，因此似然推断在计算上具有挑战性。对于计算量大的方法而言，这可能在计算上很困难，例如非常缓慢的收敛甚至不收敛。最近，对于具有不可观察或不可观察的随机变量的模型，层次似然性（h似然性）起着重要作用。本文着重研究在均值结构中具有随机效应的线性模型，并提出了一种惩罚性h似然算法，该算法在通过h似然进行均值建模的过程中结合了变量选择程序。惩罚h似然方法避免了因随机效应而造成的混乱整合，并且计算效率很高。此外，它在相关变量选择中表现出良好的性能。通过理论分析和仿真，可以证明惩罚h似然算法产生了良好的固定效应估计结果，并且在建模均值结构时可以识别零回归系数。惩罚h似然方法避免了因随机效应而造成的混乱整合，并且计算效率很高。此外，它在相关变量选择中表现出良好的性能。通过理论分析和仿真，可以证明惩罚h似然算法产生了良好的固定效果估计结果，并且在建模均值结构时可以识别零回归系数。惩罚h似然法避免了因随机效应而造成的混乱整合，并且计算效率高。此外，它在相关变量选择中表现出良好的性能。通过理论分析和仿真，可以证明惩罚h似然算法产生了良好的固定效应估计结果，并且在建模均值结构时可以识别零回归系数。