Like mean, median, and standard deviation, mode as the value that appears most often in a set of data is an important feature of a distribution. The numerical value of the mode is the same as that of the mean and median in a symmetric distribution but may be very differentina highly skewed distribution. Moderegression, which models therelationship between the mode of a dependent variable and some covariates, was first introduced by Lee interms of truncated dependent variables. Some modifications of the truncated mode regression have been proposed recently. However, little progress is made on the computation or algorithm of fitting a mode regression due to an NP-hard optimization problem. In this paper we first introduce the popular simulated annealing (SA) to solve the truncated mode regression optimization. Experiments with simulations compare favorably to SA. Then, amoderegression with theproposedalgorithmisapplied toexplore the typical income structure of China. We also compare the income returns to gender, education, experience, jobsector, and districtbetweenthemajority of workers with typical income and the workers with mean, middle income via comparison between mode regression, mean regression, and median regression.