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Full-Text Articles in Physical Sciences and Mathematics
Semiparametric Regression Analysis Of Bivariate Interval-Censored Data, Naichen Wang
Semiparametric Regression Analysis Of Bivariate Interval-Censored Data, Naichen Wang
Theses and Dissertations
Survival analysis is a long-lasting and popular research area and has numerous applications in all fields such as social science, engineering, economics, industry, and public health. Interval-censored data are a special type of survival data, in which the survival time of interest is never exactly observed but is known to fall within some observed interval. Interval-censored data arise commonly in real-life studies, in which subjects are examined at periodical or irregular follow-up visits. In this dissertation, we develop efficient statistical approaches for regression analysis of bivariate intervalcensored data, in which the two survival times of interest are correlated and both …
Ranking World Class Chess Players Using Only Results From Head-To-Head Games, Sterling Swygert
Ranking World Class Chess Players Using Only Results From Head-To-Head Games, Sterling Swygert
Senior Theses
This honors thesis explores a method of ranking the world’s top ten chess grand- masters using only the outcomes of games containing only players in that very set. This method allows for players in a single era to be quickly ranked via algorithmic and numerical means, including very specific information, from a statistical stand- point. Furthermore, unlike the rating systems that are commonly used, the Elo and the Glicko systems, this method is Classicist in its statistical approach, rather than Bayesian. Finally, this ranking method also differs from others as it limits the infor- mation to games between the individuals …
Methods For Clustering Mixed Data, Jeanmarie L. Hendrickson
Methods For Clustering Mixed Data, Jeanmarie L. Hendrickson
Theses and Dissertations
We give a brief introduction to cluster analysis and then propose and discuss a few methods for clustering mixed data. In particular, a model-based clustering method for mixed data based on Everitt's (1988) work is described, and we use a simulated annealing method to estimate the parameters for Everitt's model. A penalized log likelihood with the simulated annealing method is proposed as a remedy for the parameter estimates being drawn to extremes. Everitt's approach and the proposed method are compared based on their performance in clustering simulated data. We then use the penalized log likelihood method on a heart disease …