arXiv: Methodology

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#2Marcos NettoH-Index: 7

Last. Lamine MiliH-Index: 33

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The paper develops a robust estimation method that makes the dynamic mode decomposition method resistant to outliers while being fast to compute and statistically efficient (i.e. accurate) at the Gaussian and non-Gaussian thick tailed distributions. The proposed robust dynamic mode decomposition (RDMD) is anchored on the theory of robust statistics. Specifically, it relies on the Schweppe-type Huber generalized maximum-likelihood estimator that minimizes a convex weighted Huber loss function, wh...

#2Majid AsadiH-Index: 17

Last. Ehsan ZamanzadeH-Index: 11

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The mean past lifetime (MPL) is an important tool in reliability and survival analysis for measuring the average time elapsed since the occurrence of an event, under the condition that the event has occurred before a specific time t>0 This article develops a nonparametric estimator for MPL based on observations collected according to ranked set sampling (RSS) design. It is shown that the estimator that we have developed is a strongly uniform consistent. It is also proved that the introduced e...

#1Zheng Zhao (TKK: Helsinki University of Technology)H-Index: 3

#2Rui GaoH-Index: 4

Last. Simo SärkkäH-Index: 30

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This paper is concerned with regularized extensions of hierarchical non-stationary temporal Gaussian processes (NSGPs) in which the parameters (e.g., length-scale) are modeled as GPs. In particular, we consider two commonly used NSGP constructions which are based on explicitly constructed non-stationary covariance functions and stochastic differential equations, respectively. We extend these NSGPs by including L^1regularization on the processes in order to induce sparseness. To solve the resu...

#1David S. Robertson (University of Cambridge)H-Index: 5

#2Babak Choodari-OskooeiH-Index: 9

Last. Thomas JakiH-Index: 25

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Recent FDA guidance on adaptive clinical trial designs defines bias as "a systematic tendency for the estimate of treatment effect to deviate from its true value", and states that it is desirable to obtain and report estimates of treatment effects that reduce or remove this bias. In many adaptive designs, the conventional end-of-trial point estimates of the treatment effects are prone to bias, because they do not take into account the potential and realised trial adaptations. While much of the m...

#2Rui Miao

Last. Xiaoke Zhang

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Data-driven individualized decision making has recently received increasing research interests. Most existing methods rely on the assumption of no unmeasured confounding, which unfortunately cannot be ensured in practice especially in observational studies. Motivated by the recent proposed proximal causal inference, we develop several proximal learning approaches to estimating optimal individualized treatment regimes (ITRs) in the presence of unmeasured confounding. In particular, we establish s...

#1M.A. WortmanH-Index: 9

Last. Pranav Kannan

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Core Damage Frequency (CDF) is a risk metric often employed by nuclear regulatory bodies worldwide. Numerical values for this metric are required by U.S. regulators, prior to reactor licensing, and reported values can trigger regulatory inspections. CDF is reported as a constant, sometimes accompanied by a confidence interval. It is well understood that CDF characterizes the arrival rate of a stochastic point process modeling core damage events. However, consequences of the assumptions imposed o...

A flexible Bayesian non-confounding spatial model for analysis of dispersed count data in clinical studies.

#2Hossein BaghishaniH-Index: 3

Last. Afshin FallahH-Index: 2

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In employing spatial regression models for counts, we usually meet two issues. First, ignoring the inherent collinearity between covariates and the spatial effect would lead to causal inferences. Second, real count data usually reveal over or under-dispersion where the classical Poisson model is not appropriate to use. We propose a flexible Bayesian hierarchical modeling approach by joining non-confounding spatial methodology and a newly reconsidered dispersed count modeling from the renewal the...

#1Shubhadeep ChakrabortyH-Index: 2

#2Xianyang ZhangH-Index: 11

Change-point detection has been a classical problem in statistics and econometrics. This work focuses on the problem of detecting abrupt distributional changes in the data-generating distribution of a sequence of high-dimensional observations, beyond the first two moments. This has remained a substantially less explored problem in the existing literature, especially in the high-dimensional context, compared to detecting changes in the mean or the covariance structure. We develop a nonparametric ...

#1Jakob Richter (Technical University of Dortmund)H-Index: 6

#2Tim Friede (GAU: University of Göttingen)H-Index: 40

Last. Jörg Rahnenführer (Technical University of Dortmund)H-Index: 41

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We propose to use Bayesian optimization (BO) to improve the efficiency of the design selection process in clinical trials. BO is a method to optimize expensive black-box functions, by using a regression as a surrogate to guide the search. In clinical trials, planning test procedures and sample sizes is a crucial task. A common goal is to maximize the test power, given a set of treatments, corresponding effect sizes, and a total number of samples. From a wide range of possible designs we aim to s...

#2Thorsten Dickhaus (University of Bremen)H-Index: 21

We present simulated standard curves for the calibration of empirical likelihood ratio (ELR) tests of means. With the help of these curves, the nominal significance level of the ELR test can be adjusted in order to achieve (quasi-) exact type I error rate control for a given, finite sample size. By theoretical considerations and by computer simulations, we demonstrate that the adjusted significance level depends most crucially on the skewness and on the kurtosis of the parent distribution. For p...

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