COUNT DATA REGRESSION USING SERIES EXPANSIONS: WITH APPLICATIONS
提出一类新的参数回归模型，适用于过分散和欠分散的计数数据，基于泊松基线密度的平方多项式展开，并应用于企业收购报价和就医次数数据。
A new class of parametric regression models for both under- and overdispersed count data is proposed. These models are based on squared polynomial expansions around a Poisson baseline density. The approach is similar to that for continuous data using squared Hermite polynomials proposed by Gallant and Nychka and applied to ®nancial data by, among others, Gallant and Tauchen. The count models are applied to underdispersed data on the number of takeover bids received by targeted ®rms, and to overdispersed data on the number of visits to health practitioners. The models appear to be particularly useful for underdispersed