While the classic off-policy evaluation (OPE) literature commonly assumes decision time points to be evenly spaced for simplicity, in many real-world scenarios, such as those involving user-initiated visits, decisions are made at irregularly-spaced and potentially outcome-dependent time points. For a more principled evaluation of the dynamic policies, this paper constructs a novel OPE framework, which concerns not only the state-action process but also an observation process dictating the time points at which decisions are made. The framework is closely connected to the Markov decision process in computer science and with the renewal process in the statistical literature. Within the framework, two distinct value functions, derived from cumulative reward and integrated reward respectively, are considered, and statistical inference for each value function is developed under revised Markov and time-homogeneous assumptions. The validity of the proposed method is further supported by theoretical results, simulation studies, and a real-world application from electronic health records (EHR) evaluating periodontal disease treatments.
Multi-Resolution Spatial Random-Effects Models for Irregularly Spaced Data
The spatial random-effects model is flexible in modeling spatial covariance functions , and is computationally efficient for spatial prediction via fixed rank kriging. However, the success of this model depends on an appropriate set of basis functions. In this research, we propose a class of basis functions extracted from thin-plate splines. These functions are ordered in terms of their degrees of smoothness with a higher-order function corresponding to larger-scale features and a lower-order one corresponding to smaller-scale details, leading to a parsimonious representation for a nonstationa…
Earth and space observations meet complex algebras: from complex to octonions for multivariate autoregressive time series analysis
Faculty of Engineering and Science, Universidad Adolfo Ibáñez（阿道夫·伊巴涅斯大学工程与科学学院）；Data Observatory Foundation, ANID Technology Center No. DO210001（数据观测基金会，ANID技术中心No. DO210001）；Millennium Institute of Astrophysics, ICM-ANID ICN12_009（天体物理学千年研究所，ICM-ANID ICN12_009）；Department of Mathematics and Computer Science, Faculty of ScienceUniversidad de Santiago（圣地亚哥大学理学院数学与计算机科学系）
Parameter Estimation for Time-Scaled Inhomogeneous Phase-Type Distributions from Discrete Observations
Facultad de Ciencias, Universidad Nacional Autónoma de México（墨西哥国立自治大学理学院）；Department of Statistics, University of Wisconsin, Madison（威斯康星大学麦迪逊分校统计学系）
