Results 31 to 40 of about 36,300 (256)
A Map of the Poor or a Poor Map?
Mathematics, 2021 This paper evaluates the performance of different small area estimation methods using model and design-based simulation experiments. Design-based simulation experiments are carried out using the Mexican Intra Censal survey as a census of roughly 3.9 ...
Paul Corral +3 more
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Comparison of Parametric and Bootstrap Method in Bioequivalence Test [PDF]
The Korean Journal of Physiology and Pharmacology, 2009 The estimation of 90% parametric confidence intervals (CIs) of mean AUC and Cmax ratios in bioequivalence (BE) tests are based upon the assumption that formulation effects in log-transformed data are normally distributed. To compare the parametric CIs with those obtained from nonparametric methods we performed repeated estimation of bootstrap-resampled
Byung-Jin, Ahn, Dong-Seok, Yim
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Comparison of nonparametric estimators versus parametric for reliability function
Ingeniería Investigación y Desarrollo, 2015 One of the main objectives of the area of realiability is to estimate the function of reliability, where traditionally are used non parametric estimators, being more efficient in sample of big sizes.
Javier Ramírez-Montoya
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Mathematics, 2023 In this paper, a one-way error component regression model with measurement errors is considered. The unknown parameter vector is estimated by using the bias-corrected method, and its corresponding asymptotic properties are also developed.
Lili Yue +3 more
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Estimator Statistics from Simulation-Free Dirichlet Block-Bootstrap Resampling
StatsSince the initiation of two variants of the bootstrap method by Efron and Rubin in the late 1970s, a variety of advancements has emerged in the literature. The subsampling of blocks enabled the estimation of the actual variance of the sample mean.
Tillmann Rosenow
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Parametric Bootstrap for Differentially Private Confidence Intervals
, 2020 The goal of this paper is to develop a practical and general-purpose approach to construct confidence intervals for differentially private parametric estimation. We find that the parametric bootstrap is a simple and effective solution. It cleanly reasons about variability of both the data sample and the randomized privacy mechanism and applies "out of ...
Cecilia Ferrando +2 more
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Mathematics, 2023 With the development of modern data collection techniques, researchers often encounter high-dimensional data across various research fields. An important problem is to determine whether several groups of these high-dimensional data originate from the ...
Zhi Peng Ong +3 more
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Algorithms for Molecular Biology, 2020 Non-parametric and semi-parametric resampling procedures are widely used to perform support estimation in computational biology and bioinformatics. Among the most widely used methods in this class is the standard bootstrap method, which consists of ...
Wei Wang +3 more
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On Testing the Adequacy of the Inverse Gaussian Distribution
Mathematics, 2022 We propose a new class of goodness-of-fit tests for the inverse Gaussian distribution based on a characterization of the cumulative distribution function (CDF). The new tests are of weighted L2-type depending on a tuning parameter.
James S. Allison +3 more
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Molecular Oncology, EarlyView.Detecting circulating tumor cells (CTCs) in blood before surgery may help predict outcomes in patients with head and neck squamous cell carcinoma (HNSCC). Here, we show when combined with tumor size and lymph node involvement from routine imaging, CTC status identifies high‐risk patients with poorer survival—offering a simple, minimally invasive tool ...
Susanne Flach +9 more
wiley +1 more source