Constrained Polynomial Likelihood
提出一种非负多项式最小范数似然比方法，仅利用矩信息估计两个分布的密度比，并允许施加形状约束，应用于跳跃扩散过程转移密度估计和期权价格隐含正密度提取。
We develop a nonnegative polynomial minimum-norm likelihood ratio (PLR) of two distributions of which only moments are known. The sample PLR converges to the unknown population PLR under mild conditions. The methodology allows for additional shape restrictions, as we illustrate with two empirical applications. The first develops a PLR for the unknown transition density of a jump-diffusion process, while the second extracts a positive density directly from option prices. In both cases, we show the importance of implementing the non-negativity restriction.