The identification of factors associated with mental and behavioral disorders in early childhood is critical both for psychopathology research and the support of primary health care practices. Motivated by the Millennium Cohort Study, in this paper we study the effect of a comprehensive set of covariates on children's emotional and behavioural trajectories in England. To this end, we develop a Quantile Mixed Hidden Markov Model for joint estimation of multiple quantiles in a linear regression setting for multivariate longitudinal data. The novelty of the proposed approach is based on the Multivariate Asymmetric Laplace distribution which allows to jointly estimate the quantiles of the univariate conditional distributions of a multivariate response, accounting for possible correlation between the outcomes. Sources of unobserved heterogeneity and serial dependency due to repeated measures are modeled through the introduction of individual-specific, time-constant random coefficients and time-varying parameters evolving over time with a Markovian structure, respectively. The inferential approach is carried out through the construction of a suitable Expectation-Maximization algorithm without parametric assumptions on the random effects distribution.
翻译:确定与幼儿期精神和行为紊乱有关的因素对精神病学研究和支持初级保健做法至关重要。在千年科霍特研究的推动下,我们在本文件中研究了一套综合共变因素对英格兰儿童情感和行为轨迹的影响。为此,我们开发了一个量性混合隐藏马克夫模型,用于在多变量纵向数据的线性回归设置中联合估计多个量。拟议方法的新颖性基于多变量对称拉普尔分布法,该分布法能够共同估计多变量反应的单项有条件分布,并计算结果之间的可能关联。由于反复措施而未观察到的异性和序列依赖性来源,通过采用与马尔科维亚结构一起随着时间变化的个别、时间-时间-随机系数和时间变化参数来建模。推论方法是通过设计一个适当的预测-马克西姆分解法来进行,而没有参数的随机分布假设。