Beyond first-order asymptotics for Cox regression

Weak Dependence beyond Mixing and Asymptotics for Nonparametric Regression

Asymptotics for Hazard Regression

In hazard regression (HARE), the logarithm of the conditional hazard function of a survival time given a covariate is modeled by a sum of polynomial splines and their tensor products. Under appropriate conditions, it has been shown that the (nonadaptive) HARE estimate of the conditional log-hazard function possesses an optimal L 2 rate of convergence. The current paper considers the L oo rates ...

Beyond First Order Logic: From number of structures to structure of numbers: Part I

We study the history and recent developments in nonelementarymodel theory focusing on the framework of abstractelementary classes. We discuss the role of syntax and semanticsand the motivation to generalize first order model theory to nonelementaryframeworks and illuminate the study with concrete examplesof classes of models. This first part introduces the main conceps and philosophies anddiscu...

Beyond first order logic: From number of structures to structure of numbers: Part II

We study the history and recent developments in nonelementarymodel theory focusing on the framework of abstractelementary classes. We discuss the role of syntax and semanticsand the motivation to generalize first order model theory to nonelementaryframeworks and illuminate the study with concrete examplesof classes of models. This second part continues to study the question of catecoricitytrans...

Penalized Estimators in Cox Regression Model

The proportional hazard Cox regression models play a key role in analyzing censored survival data. We use penalized methods in high dimensional scenarios to achieve more efficient models. This article reviews the penalized Cox regression for some frequently used penalty functions. Analysis of medical data namely ”mgus2” confirms the penalized Cox regression performs better than the cox regressi...