Seminario Núcleo Milenio MiDaS

Scientific seminar of the Millenium Nucleus Center for the Discovery of Structures in Complex Data (MiDaS). More information in: midas.mat.uc.cl
2020-01-29
12:00 hrs.
José Quinlan. Pontificia Universidad Católica de Chile
On the Support of Yao-based Random Ordered Partitions for Change-Point Analysis
Sala 1, Facultad de Matemáticas
Abstract:

In Bayesian change-point analysis for univariate time series, prior distributions on the set of ordered partitions play a key role for change-point detection. In this context, mixtures of product partition models based on Yao's cohesion are very popular due to their tractability and simplicity. However, how flexible are these prior processes to describe different beliefs about the number and locations of change-points? In this talk I will address the previous question in terms of its weak support.

2020-01-22
12:00 hrs.
Miles Ott. Smith College
Respondent-Driven Sampling: Challenges and Opportunities
Sala 1, Facultad de Matemáticas
Abstract:
Respondent-driven sampling leverages social networks to sample hard-to-reach human populations, including among those who inject drugs, sexual minority, sex worker, and migrant populations.  As with other link-tracing sampling strategies, sampling involves recruiting a small convenience sample, who invite their contacts into the sample, and in turn invite their contacts until the desired sample size is reached. Typically, the sample is used to estimate prevalence, though multivariable analyses of data collected through respondent-driven sampling are becoming more common. Although respondent-driven sampling may allow for quickly attaining large and varied samples, its reliance on social network contacts, participant recruitment decisions, and self-report of ego-network size makes it subject to several concerns for statistical inference.  After introducing respondent-driven sampling I will discuss how these data are actually being collected and analyzed, and opportunities for statisticians to improve upon this widely-adopted method.
2020-01-15
12:00 hrs.
Nicolas Kuschinski. Pontificia Universidad Católica de Chile
FATSO: Una familia de operadores para selección de variables en modelos lineales
Sala 1, Facultad de Matemáticas
Abstract:
En modelos lineales es común encontrarse con situaciones donde varios de los coeficientes de regresión son 0. En estas situaciones, una herramienta común es un operador de selección de variables de tipo "sparsity promoting". El más común de estos operadores es el LASSO, el cual promueve estimaciones en 0. Sin embargo, el LASSO y sus derivados dan poco en términos de parámetros fácilmente interpretables para controlar el grado de selectividad. En esta plática se propondrá una nueva familia de operadores de selección, la cual toma como base la geometría del LASSO, pero que tienen forma analítica distinta, y que dan una manera fácilmente interpretable de controlar el grado de selectividad. Estos operadores corresponden con densidades a priori propias, y por ende se pueden usar para hacer inferencia Bayesiana.