Invited Speaker Series 2016-2017

Fall 2016

Dennis Lin | Penn State University

September 1, 2016 | 4-5pm | 052 SHD

"Dimensional Analysis and Its Applications in Statistics"


Dimensional Analysis (DA) is a fundamental method in the engineering and physical sciences for analytically reducing the number of experimental variables prior to the experimentation. The principle use of dimensional analysis is to reduce from a study of dimensions of the variables on the form of any possible relationship between those variables. The method is of great generality. In this talk, an overview/introduction of DA will be first given. A basic guideline for applying DA will be proposed, using examples for illustration. Some initial ideas on using DA for Data Analysis and Data Collection will be discussed. Future research issues will be proposed.

Mark Daniel Ward | Purdue University

September 29, 2016 | 4-5pm | 052 SHD

"An Overview of an Analytic Approach for Branching Processes"


One approach to solving some questions in probability theory--especially questions about asymptotic properties of algorithms and data structures--is to take an analytic approach, i.e., to utilize complex-valued methods of attack. These methods are especially useful with several types of branching processes, leader election algorithms, pattern matching in trees, data compression, etc. This talk will focus on some of the highlights of this approach. I endeavor to keep it at a level that is accessible for graduate students

Petar Jevtic| McMaster University

October 20, 2016 | 4-5pm | 052 SHD

"The Joint Mortality of Couples in Continuous Time"


This paper introduces a probabilistic framework for the joint survivorship of couples in the context of dynamic stochastic mortality models. In contrast to previous literature, where the dependence between male and female times of death was achieved using a copula approach, this new framework gives an intuitive and flexible pairwise cohort-based probabilistic mechanism that can accommodate both deterministic and stochastic effects which the death of one member of couple causes on the other. It is sufficiently flexible to allow modeling of effects that are short term (broken heart) or long term in their durations. In addition, it can account for the state of health of the both the surviving and dying spouse and thus can allow for dynamic and asymmetric reactions of varying complexity. Finally, it can accommodate the dependence of lives before the first death. Analytical expressions for bivariate survivorship in representative models are given, and their estimation, done in two stages, is seen to be straightforward. First, marginal survivorship functions are calibrated based on UK mortality data for males and females of chosen cohorts. Second, the maximum likelihood approach is used to estimate the remaining parameters from simulated joint survival data. We show that the calibration methodology is simple, robust and fast, and can be readily used in practice

Spring 2017

Keith Task| BASF

March 30, 2017 | 4-5pm | 275 UPH

"Industrial Statistics at BASF: From Small Experiment to Big Data and Everywhere In-Between"


The applications of statistics in the industrial world are endless, and this is no exception in the chemical industry. With the almost never-ending amount of data being collected and stored, as well as the constant need to streamline experiments, statisticians and modelers play an integral role in increasing productivity, especially in this digital age. BASF has recognized and embraced this fact; there has been a focus on data analytics and modeling, and in the Digitalization in R&D group at BASF, we are making strides to accelerate development through computational means. In this talk, I will provide an overview of BASF, and how modeling, in particular statistics, is contributing to BASF's overall goals. We will discuss some success stories in the areas of experimental design, time series analysis, and data mining, as well as how we are &spreading the word on the power of data analytics through statistical and design of experiment seminars. We will also discuss ways in which BASF is contributing to the statistical community through development of advanced tools, such as robust methods in R and non-linear design of experiments