A kernel regression model for panel count data with time-varying coefficients

For the conditional mean function of panel count model with time-varying coefficients, we propose to use local kernel regression method for estimation. Partial log-likelihood polynomial is formed Under some regularity conditions, strong uniform consistency rates are obtained estimator. At target time point, show that estimator converges in distribution normal distribution. The baseline also shown be consistent. Simulation studies coefficient close true value, empirical coverage probabilities confidence interval nominal level. We applied proposed analyze a clinical study on childhood wheezing.