Analysis of Patent Data—A Mixed-Poisson-Regression-Model Approach
用有限混合泊松回归模型分析企业专利与研发支出的关系，处理过度离散问题，并通过蒙特卡洛研究评估模型选择准则和参数估计的稳健性。
Count-data models are used to analyze the relationship between patents and research and development spending at the firm level, accounting for overdispersion using a finite mixed Poisson regression model with covariates in both Poisson rates and mixing probabilities. Maximum likelihood estimation using the EM and quasi-Newton algorithms is discussed. Monte Carlo studies suggest that (a) penalized likelihood criteria are a reliable basis for model selection and can be used to determine whether continuous or finite support for the mixing distribution is more appropriate and (b) when the mixing distribution is incorrectly specified, parameter estimates remain unbiased but have inflated variances.