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首页> 外文期刊>Australian & New Zealand journal of statistics >ESTIMATION OF A SEMIPARAMETRIC RECURSIVE BIVARIATE PROBIT MODEL WITH NONPARAMETRIC MIXING
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ESTIMATION OF A SEMIPARAMETRIC RECURSIVE BIVARIATE PROBIT MODEL WITH NONPARAMETRIC MIXING

机译：具有非参数混合的半参数递归布尔变量概率模型的估计

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摘要

We consider an extension of the recursive bivariate probit model for estimating the effect of a binary variable on a binary outcome in the presence of unobserved confounders, nonlinear covariate effects and overdispersion. Specifically, the model consists of a system of two binary outcomes with a binary endogenous regressor which includes smooth functions of covariates, hence allowing for flexible functional dependence of the responses on the continuous regressors, and arbitrary random intercepts to deal with overdispersion arising from correlated observations on clusters or from the omission of non-confounding covariates. We fit the model by maximizing a penalized likelihood using an Expectation-Maximisation algorithm. The issues of automatic multiple smoothing parameter selection and inference are also addressed. The empirical properties of the proposed algorithm are examined in a simulation study. The method is then illustrated using data from a survey on health, aging and wealth.
机译：我们考虑了在没有观察到的混杂因素，非线性协变量效应和过度分散的情况下，递归双变量概率模型的扩展，用于估计二进制变量对二进制结果的影响。具体来说，该模型由一个包含两个二进制结果的系统组成，该系统具有一个二进制内生回归变量，该变量包含协变量的平滑函数，因此允许对连续回归变量的响应进行灵活的函数依赖性，并使用任意随机截距来处理因相关观测值引起的过度分散聚类或忽略无混杂的协变量。我们使用Expectation-Maximisation算法通过最大化惩罚可能性来拟合模型。自动多重平滑参数选择和推断的问题也得到解决。仿真研究中检验了所提出算法的经验性质。然后使用来自健康，老龄化和财富调查的数据说明该方法。

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