Results 11 to 20 of about 40,800 (302)
Bayesian Parametric Bootstrap for Models with Intractable Likelihoods [PDF]
Bayesian Analysis, 2019 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Vo, Brenda +2 more
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A parametric bootstrap test for cycles [PDF]
Journal of Econometrics, 2005 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Violetta Dalla, Javier Hidalgo
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Edgeworth Expansion of the Parametric Bootstrap t-statistic for Linear Regression Processes with Strongly Dependent Errors [PDF]
Journal of Statistical Theory and Applications (JSTA), 2015 The purpose of this paper is to provide a valid Edgeworth expansion for the parametric bootstrap t-statistic of a linear regression process whose error terms are stationary, Gaussian, and strongly dependent time series.
Mosisa Aga
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Parametric bootstrapping for biological sequence motifs [PDF]
BMC Bioinformatics, 2016 Abstract Background Biological sequence motifs drive the specific interactions of proteins and nucleic acids. Accordingly, the effective computational discovery and analysis of such motifs is a central theme in bioinformatics.
Patrick K. O'Neill, Ivan Erill
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Gene expression analysis with the parametric bootstrap [PDF]
Biostatistics, 2001 Recent developments in microarray technology make it possible to capture the gene expression profiles for thousands of genes at once. With this data researchers are tackling problems ranging from the identification of 'cancer genes' to the formidable task of adding functional annotations to our rapidly growing gene databases.
Van der Laan, Mark J., Bryan, Jenny
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The Percentile Bootstrap: A Primer With Step-by-Step Instructions in R
Advances in Methods and Practices in Psychological Science, 2021 The percentile bootstrap is the Swiss Army knife of statistics: It is a nonparametric method based on data-driven simulations. It can be applied to many statistical problems, as a substitute to standard parametric approaches, or in situations for which ...
Guillaume A. Rousselet +2 more
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Robust variance estimation and inference for causal effect estimation
Journal of Causal Inference, 2023 We present two novel approaches to variance estimation of semi-parametric efficient point estimators of the treatment-specific mean: (i) a robust approach that directly targets the variance of the influence function (IF) as a counterfactual mean outcome ...
Tran Linh +3 more
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BMC Medical Research Methodology, 2022 Background Healthcare provider profiling involves the comparison of outcomes between patients cared for by different healthcare providers. An important component of provider profiling is risk-adjustment so that providers that care for sicker patients are
Peter C. Austin
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Confidence intervals and sample size planning for optimal cutpoints.
PLoS ONE, 2023 Various methods are available to determine optimal cutpoints for diagnostic measures. Unfortunately, many authors fail to report the precision at which these optimal cutpoints are being estimated and use sample sizes that are not suitable to achieve an ...
Christian Thiele, Gerrit Hirschfeld
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Parametric Bootstrap Methods for Estimating Model Parameters of Non-homogeneous Gamma Process [PDF]
International Journal of Mathematical, Engineering and Management Sciences, 2018 Non-Homogeneous Gamma Process (NHGP) is characterized by an arbitrary trend function and a gamma renewal distribution. In this paper, we estimate the confidence intervals of model parameters of NHGP via two parametric bootstrap methods: simulation-based ...
Yasuhiro Saito, Tadashi Dohi
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