@article{davis_eder_nychka_yang_1998, title={Modeling the effects of meteorology on ozone in Houston using cluster analysis and generalized additive models}, volume={32}, ISSN={["1352-2310"]}, DOI={10.1016/S1352-2310(98)00008-9}, abstractNote={This paper compares the results from a single-stage clustering technique (average linkage) with those of a two-stage technique (average linkage then k-means) as part of an objective meteorological classification scheme designed to better elucidate ozone’s dependence on meteorology in the Houston, Texas, area. When applied to twelve years of meteorological data (1981–1992), each clustering technique identified seven statistically distinct meteorological regimes. The majority of these regimes exhibited significantly different daily 1 h maximum ozone (O3) concentrations, with the two-stage approach resulting in a better segregation of the mean concentrations when compared to the single-stage approach. Both approaches indicated that the largest daily 1 h maximum concentrations are associated with migrating anticyclones that occur most often during spring and summer, and not with the quasi-permanent Bermuda High that often dominates the southeastern United States during the summer. As a result, maximum ozone concentrations are just as likely during the months of April, May, September and October as they are during the summer months. Generalized additive models were then developed within each meteorological regime in order to identify those meteorological covariates most closely associated with O3 concentrations. Three surface wind covariates: speed, and the u and v components were selected nearly unanimously in those meteorological regimes dominated by anticyclones, indicating the importance of transport within these O3 conducive meteorological regimes.}, number={14-15}, journal={ATMOSPHERIC ENVIRONMENT}, author={Davis, JM and Eder, BK and Nychka, D and Yang, Q}, year={1998}, month={Aug}, pages={2505–2520} }
 @article{lu_park_yang_1997, title={Statistical inference of a time-to-failure distribution derived from linear degradation data}, volume={39}, ISSN={["0040-1706"]}, DOI={10.2307/1271503}, abstractNote={In the study of semiconductor degradation, records of transconductance loss or threshold voltage shift over time are useful in constructing the cumulative distribution function (cdf) of the time until the degradation reaches a specified level. In this article, we propose a model with random regression coefficients and a standard-deviation function for analyzing linear degradation data. Both analytical and empirical motivations of the model are provided. We estimate the model parameters, the cdf, and its quantiles by the maximum likelihood (ML) method and construct confidence intervals from the bootstrap, from the asymptotic normal approximation, and from inverting likelihood ratio tests. Simulations are conducted to examine the properties of the ML estimates and the confidence intervals. Analysis of an engineering dataset illustrates the proposed procedures.}, number={4}, journal={TECHNOMETRICS}, author={Lu, JC and Park, J and Yang, Q}, year={1997}, month={Nov}, pages={391–400} }