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AUEB SEMINARS - 29/6/2016: Challenges in Modeling Rate Data with Time Factors – the Identifiability Problem with Linear Confounders Forumgrstats

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AUEB SEMINARS - 29/6/2016: Challenges in Modeling Rate Data with Time Factors – the Identifiability Problem with Linear Confounders Forumgrstats
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AUEB SEMINARS - 29/6/2016: Challenges in Modeling Rate Data with Time Factors – the Identifiability Problem with Linear Confounders Empty AUEB SEMINARS - 29/6/2016: Challenges in Modeling Rate Data with Time Factors – the Identifiability Problem with Linear Confounders

Tue 28 Jun 2016 - 16:04
AUEB SEMINARS - 29/6/2016: Challenges in Modeling Rate Data with Time Factors – the Identifiability Problem with Linear Confounders 2edma2e

ΚΥΚΛΟΣ ΣΕΜΙΝΑΡΙΩΝ ΣΤΑΤΙΣΤΙΚΗΣ – ΙΟΥΝΙΟΣ 2016

Wenjiang Fu
Department of Mathematics, University of Houston

Challenges in Modeling Rate Data with Time Factors – the Identifiability Problem with Linear Confounders

ΤΕΤΑΡΤΗ 29/6/2016 13:15-14:00

ΑΙΘΟΥΣΑ 607, 6ος ΟΡΟΦΟΣ,
ΚΤΙΡΙΟ ΜΕΤΑΠΤΥΧΙΑΚΩΝ ΣΠΟΥ∆ΩΝ
(ΕΥΕΛΠΙ∆ΩΝ & ΛΕΥΚΑ∆ΟΣ)


ΠΕΡΙΛΗΨΗ

In monitoring human behavior and disease development, event rate (disease incidence or mortality, crime, consumer consumption, investment and sales, etc) are often collected / archived by age and calendar years. The aim of modeling such type of data in a rectangular table is to estimate accurately the temporal trend in calendar year or compare rate in different years or groups. Although the data seems to be simple and straightforward, the statistical models, however, suffer from two major challenges in modeling such one rate per cell data. The first one is the identifiability problem in the regression model with linearly dependent covariates: age (rows), period (columns) and birth cohort (diagonals) are linearly dependent. The second one seems to avoid the identifiability problem using a summary rate approach for each period (column), but incurs another major problem – the direct age-standardization with a serious issue of selecting the reference age-structure for fair comparison. Although both problems have been extensively studied during the past few decades, major issues still remain.
In this talk, I will briefly introduce these twin-sister problems with US cancer mortality data, crime rate data and life insurance purchase data. I will then introduce the intrinsic estimator method and provide theoretical justification based on large sample theory to resolve the identifiability problem. I will then examine the method through simulation studies and real data in cancer studies, marketing research, and sociological studies. This work was partly supported by grants from the NIH/NCI of the US.
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