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首页> 外文期刊>Journal of applied statistics >Prior distribution elicitation for generalized linear and piecewise-linear models
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Prior distribution elicitation for generalized linear and piecewise-linear models

机译:广义线性和分段线性模型的先验分布启发

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摘要

An elicitation method is proposed for quantifying subjective opinion about the regression coefficients of a generalized linear model. Opinion between a continuous predictor variable and the dependent variable is modelled by a piecewise-linear function, giving a flexible model that can represent a wide variety of opinion. To quantify his or her opinions, the expert uses an interactive computer program, performing assessment tasks that involve drawing graphs and bar-charts to specify medians and other quantiles. Opinion about the regression coefficients is represented by a multivariate normal distribution whose parameters are determined from the assessments. It is practical to use the procedure with models containing a large number of parameters. This is illustrated through practical examples and the benefit from using prior knowledge is examined through cross-validation.
机译:提出了一种启发式方法,用于量化关于广义线性模型的回归系数的主观意见。连续预测变量和因变量之间的观点是通过分段线性函数建模的,从而给出了可以表示各种各样观点的灵活模型。为了量化他或她的意见,专家使用交互式计算机程序执行评估任务,其中涉及绘制图形和条形图以指定中位数和其他分位数。关于回归系数的观点由多元正态分布表示,其正态分布由评估确定。将过程与包含大量参数的模型一起使用是很实际的。通过实际示例对此进行了说明,并通过交叉验证来检验使用先验知识的好处。

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