Constructing a Basefile for Simulating Kunming’s Medical Insurance Scheme of Urban Employees

Focusing on China’s medical insurance scheme which covers all employers and employees in urban areas, this research aims to assess the distributional impacts of medical insurance policies and to predict medical expenses by using microsimulation techniques. As an important part of the project, this article provides a brief overview of China’s medical insurance reform of urban employees and detail the techniques and processes to construct a basefile in 2005 for projecting the medical expenditures for urban employees over the period of 2006-2010. The main data used are administrative medical records of medical insurance participants provided by the Bureau of Labour and Social Security of Kunming, Yunnan Province. Along with the initial analysis for the raw datasets and age processing and adjustment for the individual records, monthly income information was imputed and personal savings accounts were established for each individual record. Important modelling parameters such as death rates and income adjustment factors were constructed. Furthermore, this article identifies medical insurance for government officials by using the combination of logarithm curve fitting and binary discriminant analysis. Based on this basefile, a static microsimulation model can be built to assess the implementation effects of the medical insurance policy and analyse the impact of the medical insurance scheme on urban employees.Urban medical insurance, China, microsimulation, basefile, Policy Research

A New Clustering Algorithm for Categorical Attributes

Clustering over categorical attributes is an important yet tough task. In this paper, we present a new algorithm K-meansⅡ to extend the famous K-means algorithm which is efficient only on numerical clustering, by using new cluster center definitions and new similarity measures. Thus, our algorithm can be used in categorical clustering while preserving the efficiency. Experiments on both real-life datasets and synthetic datasets show that the K-meansⅡ algorithm can produce high quality results and deserve good scalability at the same time

Acceleration of stochastic gradient descent with momentum by averaging: finite-sample rates and asymptotic normality

Stochastic gradient descent with momentum (SGDM) has been widely used in many machine learning and statistical applications. Despite the observed empirical benefits of SGDM over traditional SGD, the theoretical understanding of the role of momentum for different learning rates in the optimization process remains widely open. We analyze the finite-sample convergence rate of SGDM under the strongly convex settings and show that, with a large batch size, the mini-batch SGDM converges faster than mini-batch SGD to a neighborhood of the optimal value. Furthermore, we analyze the Polyak-averaging version of the SGDM estimator, establish its asymptotic normality, and justify its asymptotic equivalence to the averaged SGD