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We discuss the application of the Bayesian statistical paradigm in conjunction with Monte Carlo methods to practical problems. We begin by describing the basic constructs of the Bayesian paradigm. We then discuss two applications. The first entails the simulation of a two-stage model of a property-casualty insurance operation. The second application(More)
Many older Americans who own houses have most of their wealth in their houses. Some may not have sufficient wealth to pay for (1) medical bills resulting from sudden medical problems, (2) major repairs to their houses, and/or (3) everyday expenses for food, clothing, and so on. Home Equity Conversion Mortgages (HECMs) are designed to allow older people to(More)
We fit a linear mixed model and a Bayesian hierarchical model to data provided by an insurance company located in the Midwest. We used models fit to the 1994 data to predict health insurance claims costs for 1995. We implemented the linear mixed model in SAS and used two different prediction methods to predict 1995 costs. In the linear mixed model we(More)
There are a number of reasons why data quality is important to business and government: 1. High-quality data can be a major business asset, a unique source of competitive advantage. 2. Poor quality data can lower customer satisfaction. 3. Poor quality data can lower employee job satisfaction. 4. Poor quality data can breed organizational mistrust. The(More)