Myrto Lefkopoulou Distinguished Lecture
Jeffrey S. Morris, Ph.D.
Professor, Deputy Chair Ad Interim, Department of Biostatistics, Division of Quantitative Sciences, University of Texas MD Anderson Cancer Center

"Looking Beyond the Lamppost: Bringing Light into the Dark Alleys of Complex Data"

ABSTRACT

Distinguished Alum Award Lecture
Manning Feinleib, M.D., Dr.P.H.
Professor Emeritus, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health

"Further Thoughts on the Demographic/Epidemiologic Transition Model"

ABSTRACT: None Given

Emanuel Parzen, Ph.D.
Distinguished Professor, Department of Statistics, Texas A&M University

"Classification of High Dimensional Data, Fundamental Statistical Methods"

ABSTRACT: Widely applicable Statistical and Machine Learning Classification problem observes variables (Y,X), Y class 0 or 1, X p-dimensional features. We start with report by Fix and Hodges (1951). We outline a new approach based on our research on fundamental statistical methods that extend from simple data to complex data and unify discrete and continuous random variables. Our approach estimates Pr[Y=1|X] by density estimation of comparison densities d(u), 0‹u‹1; typical d(u)=f(Q(u))/g(Q(u)), where Q(u) quantile function of G(x). We estimate f starting with g by weighted distribution formula f(x)=g(x) d(G(x)). Define comparison probability ComPr[B|A]=Pr[B|A]/Pr[B]. Bayes rule can be stated ComPr[A|B]=ComPr[B|A]. Then ComPr[Y=1|X=x]=ComPr[X=x|Y=1]=d(u), letting x=Q(u), the quantile function of X. Interpret d(u) as probability density of mid-ranks mid-distribution transform W=Fmid(X)=(Rank(X)-.5)/n; Fmid(x)=F(x)-.5p(x), p(x)=Pr[X=x]. We discuss perfect classification Pr[Y=1|X} equals 0 or 1. We discuss many approaches to estimation of d(u); emphasize MaxEnt exponential model with sufficient statistics W_j that are nonparametric statistics, means of score functions S(X) constructed for each variable X from Fmid(X). We compute Wilcoxon type W_j from CORR(Y=1,S(X)). Our modeling approach illustrates the profound chicken and egg puzzle: which comes first, the parameters or the sufficient statistics? It depends on whether parameters are scientific or statistical. We first assume independent features; extension to dependence is via nonparametric estimation of copula density functions. As example we discuss famous Hepatitus data. Our constructed score functions S(X) can also be used to perform logistic regression estimation of Pr[Y=1|X].