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Longitudinal Data Analysis and Time Series Commons™
Open Access. Powered by Scholars. Published by Universities.®
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Articles 1 - 2 of 2
Full-Text Articles in Longitudinal Data Analysis and Time Series
A Longitudinal Study Of Hospitalization Rates For Patients With Chronic Disease: Results From The Medical Outcomes Study., Eugene C. Nelson, Colleen A. Mchorney, Willard G. Manning, W H. Rogers
A Longitudinal Study Of Hospitalization Rates For Patients With Chronic Disease: Results From The Medical Outcomes Study., Eugene C. Nelson, Colleen A. Mchorney, Willard G. Manning, W H. Rogers
Dartmouth Scholarship
To prospectively compare inpatient and outpatient utilization rates between prepaid (PPD) and fee-for-service (FFS) insurance coverage for patients with chronic disease. Data from the Medical Outcomes Study, a longitudinal observational study of chronic disease patients conducted in Boston, Chicago, and Los Angeles.A four-year prospective study of resource utilization among 1,681 patients under treatment for hypertension, diabetes, myocardial infarction, or congestive heart failure in the practices of 367 clinicians.
Additive Nonparametric Regression With Autocorrelated Errors, Michael S. Smith, C Wong, Robert Kohn
Additive Nonparametric Regression With Autocorrelated Errors, Michael S. Smith, C Wong, Robert Kohn
Michael Stanley Smith
A Bayesian approach is presented for nonparametric estimation of an additive regression model with autocorrelated errors. Each of the potentially nonlinear components is modelled as a regression spline using many knots, while the errors are modelled by a high order stationary autoregressive process parameterised in terms of its autocorrelations. The distribution of significant knots and partial autocorrelations is accounted for using subset selection. Our approach also allows the selection of a suitable transformation of the dependent variable. All aspects of the model are estimated simultaneously using Markov chain Monte Carlo. It is shown empirically that the proposed approach works well …