A semiparametric multiply robust multiple imputation method for causal inference [0.03%]
一种半参数多重稳健的因果推断缺失数据多重插补方法
Benjamin Gochanour,Sixia Chen,Laura Beebe et al.
Benjamin Gochanour et al.
Evaluating the impact of non-randomized treatment on various health outcomes is difficult in observational studies because of the presence of covariates that may affect both the treatment or exposure received and the outcome of interest. In...
Gabriela Ciuperca,Matúš Maciak,Michal Pešta
Gabriela Ciuperca
An online changepoint detection procedure based on conditional expectiles is introduced. The key contribution is threefold: nonlinearity of the underlying model improves the overall flexibility while a parametric form of the unknown regress...
Sergey Tarima,Nancy Flournoy
Sergey Tarima
We extended the application of uniformly most powerful tests to sequential tests with different stage-specific sample sizes and critical regions. In the one parameter exponential family, likelihood ratio sequential tests are shown to be uni...
On a stochastic order induced by an extension of Panjer's family of discrete distributions [0.03%]
由离散分布泛化诱导的随机序
Aleksandr Beknazaryan,Peter Adamic
Aleksandr Beknazaryan
We factorize probability mass functions of discrete distributions belonging to Panjer's family and to its certain extensions to define a stochastic order on the space of distributions supported on N 0 . Main properties of this order are pr...
Checking for model failure and for prior-data conflict with the constrained multinomial model [0.03%]
带有约束多项式模型的统计模型失效及先验与数据冲突检测
Berthold-Georg Englert,Michael Evans,Gun Ho Jang et al.
Berthold-Georg Englert et al.
Multinomial models can be difficult to use when constraints are placed on the probabilities. An exact model checking procedure for such models is developed based on a uniform prior on the full multinomial model. For inference, a nonuniform ...
Andreas Anastasiou,Piotr Fryzlewicz
Andreas Anastasiou
We introduce a new approach, called Isolate-Detect (ID), for the consistent estimation of the number and location of multiple generalized change-points in noisy data sequences. Examples of signal changes that ID can deal with are changes in...
Classes of Multiple Decision Functions Strongly Controlling FWER and FDR [0.03%]
强烈控制FWER和FDR的多决策函数类
Edsel A Peña,Joshua D Habiger,Wensong Wu
Edsel A Peña
Two general classes of multiple decision functions, where each member of the first class strongly controls the family-wise error rate (FWER), while each member of the second class strongly controls the false discovery rate (FDR), are descri...
James Robins,Lingling Li,Eric Tchetgen et al.
James Robins et al.
We discuss a new method of estimation of parameters in semiparametric and nonparametric models. The method is based on U-statistics constructed from quadratic influence functions. The latter extend ordinary linear influence functions of the...
Albert Vexler,Chengqing Wu,Kai Fun Yu
Albert Vexler
We propose and examine statistical test-strategies that are somewhat between the maximum likelihood ratio and Bayes factor methods that are well addressed in the literature. The paper shows an optimality of the proposed tests of hypothesis....