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+ | <WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP> | ||
+ | |||
+ | <WRAP 70% center> | ||
+ | ^ **DATE:**|Thursday, November 29, 2018 | | ||
+ | ^ **TIME:**|1:15pm -- 2:15pm | | ||
+ | ^ **LOCATION:**|WH 100E | | ||
+ | ^ **SPEAKER:**|Qiqing Yu, Binghamton University | | ||
+ | ^ **TITLE:**|The Proportional Hazards Model with Linearly Time-dependent Covariates and Interval-censored Data | | ||
+ | </WRAP> | ||
+ | \\ | ||
+ | |||
+ | <WRAP center box 80%> | ||
+ | <WRAP centeralign>**Abstract**</WRAP> | ||
+ | The semi-parametric estimation | ||
+ | under the proportional hazards (PH) model with a linearly time-dependent | ||
+ | covariates and with interval-censored data has not been investigated before. | ||
+ | The partial likelihood approach does not work and one has to use the | ||
+ | generalized likelihood function (GLF). | ||
+ | There is a challenge in this problem. | ||
+ | The GLF must be in the form of | ||
+ | the baseline hazard function, rather than the baseline survival function | ||
+ | as in the PH model with time-independent covariates, | ||
+ | and a feasible way to specify the hazard function is a piece-wise constant | ||
+ | function. However, | ||
+ | several naive ways do not yield a consistent estimator. | ||
+ | We propose proper modifications of the GLF. | ||
+ | </WRAP> | ||
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