Skip to main content
Skip to main menu Skip to spotlight region Skip to secondary region Skip to UGA region Skip to Tertiary region Skip to Quaternary region Skip to unit footer

Slideshow

Confidence Inference Function in Big Data

Cohen Room, Statistics Building
Peter Song
UM School of Public Health

Statistical inference along with the strategy of divide-and-combine for Big Data analysis has been little studied.  As an effective inferential tool, confidence distribution (CD) has attracted a surge of renewed attention. The essence in constructing confidence distribution pertains to the availability of suitable pivotal quantities, which are usually obtained from the (asymptotical) distribution of point maximum likelihood estimator. We propose to use inference function, from which the parameter estimate is obtained, as the basis of constructing the pivotal. Compared to the existing CD, the proposed confidence inference function (CIF) inherits several advantages of estimating functions. In addition, the proposed CIF is closely related to the generalized method of moments (GMM) and Crowder’s optimality.  Thus, CIF, which includes maximum likelihood estimation as a special case, provides us a unified framework for many kinds of statistical methods, which is illustrated via numerical examples in the context of divide-and-combine approaches to Big Data analysis. 

http://www-personal.umich.edu/~pxsong/ 

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar given has a direct impact upon our students and faculty.