Maximum approximate Bernstein likelihood estimation in proportional hazard model for interval-censored data

Maximum approximate Bernstein likelihood estimates of the baseline density function and the regression coefficients in the proportional hazard regression models based on interval-censored event time data result in smooth estimates of the survival functions which enjoys an almost n(1/2)-rate of convergence faster than the n(1/3)-rate for the existing estimates. The proposed method was shown by a simulation to have better finite sample performance than its main competitors. Some examples including real data are used to illustrate the usage of the proposed method.