Frailty, Profile Likelihood, and Medfly Mortality

2013 ◽  
pp. 227-237 ◽  
Author(s):  
Roger Koenker ◽  
Jiaying Gu
Keyword(s):  
Profile Likelihood

scholarly journals A Review of Basic Energy Reconstruction Techniques in Liquid Xenon and Argon Detectors for Dark Matter and Neutrino Physics Using NEST

Instruments ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Matthew Szydagis ◽  
Grant A. Block ◽  
Collin Farquhar ◽  
Alexander J. Flesher ◽  
Ekaterina S. Kozlova ◽  
...  
Keyword(s):  
Dark Matter ◽  
Neutrino Physics ◽  
Electric Fields ◽  
Direct Detection ◽  
Profile Likelihood ◽  
Liquid Argon ◽  
Energy Scale ◽  
Liquid Xenon ◽  
Nuclear Recoils ◽  
Available Information

Detectors based upon the noble elements, especially liquid xenon as well as liquid argon, as both single- and dual-phase types, require reconstruction of the energies of interacting particles, both in the field of direct detection of dark matter (weakly interacting massive particles WIMPs, axions, etc.) and in neutrino physics. Experimentalists, as well as theorists who reanalyze/reinterpret experimental data, have used a few different techniques over the past few decades. In this paper, we review techniques based on solely the primary scintillation channel, the ionization or secondary channel available at non-zero drift electric fields, and combined techniques that include a simple linear combination and weighted averages, with a brief discussion of the application of profile likelihood, maximum likelihood, and machine learning. Comparing results for electron recoils (beta and gamma interactions) and nuclear recoils (primarily from neutrons) from the Noble Element Simulation Technique (NEST) simulation to available data, we confirm that combining all available information generates higher-precision means, lower widths (energy resolution), and more symmetric shapes (approximately Gaussian) especially at keV-scale energies, with the symmetry even greater when thresholding is addressed. Near thresholds, bias from upward fluctuations matters. For MeV-GeV scales, if only one channel is utilized, an ionization-only-based energy scale outperforms scintillation; channel combination remains beneficial. We discuss here what major collaborations use.

Bayesian and profile likelihood change point methods for modeling cognitive function over time

2003 ◽  
Vol 42 (1-2) ◽  
pp. 91-109 ◽  
Author(s):  
Charles B. Hall ◽  
Jun Ying ◽  
Lynn Kuo ◽  
Richard B. Lipton
Keyword(s):  
Cognitive Function ◽  
Change Point ◽  
Profile Likelihood ◽  
Over Time

scholarly journals Interval estimation of the overall treatment effect in random-effects meta-analyses: Recommendations from a simulation study comparing frequentist, Bayesian, and bootstrap methods

2020 ◽  
Author(s):  
Frank Weber ◽  
Guido Knapp ◽  
Anne Glass ◽  
Günther Kundt ◽  
Katja Ickstadt
Keyword(s):  
Literature Review ◽  
Random Effects ◽  
Simulation Study ◽  
Treatment Effect ◽  
Profile Likelihood ◽  
Meta Analysis ◽  
Artery Bypass ◽  
Interval Length ◽  
Interval Estimators ◽  
Meta Analyses

There exists a variety of interval estimators for the overall treatment effect in a random-effects meta-analysis. A recent literature review summarizing existing methods suggested that in most situations, the Hartung-Knapp/Sidik-Jonkman (HKSJ) method was preferable. However, a quantitative comparison of those methods in a common simulation study is still lacking. Thus, we conduct such a simulation study for continuous and binary outcomes, focusing on the medical field for application.Based on the literature review and some new theoretical considerations, a practicable number of interval estimators is selected for this comparison: the classical normal-approximation interval using the DerSimonian-Laird heterogeneity estimator, the HKSJ interval using either the Paule-Mandel or the Sidik-Jonkman heterogeneity estimator, the Skovgaard higher-order profile likelihood interval, a parametric bootstrap interval, and a Bayesian interval using different priors. We evaluate the performance measures (coverage and interval length) at specific points in the parameter space, i.e. not averaging over a prior distribution. In this sense, our study is conducted from a frequentist point of view.We confirm the main finding of the literature review, the general recommendation of the HKSJ method (here with the Sidik-Jonkman heterogeneity estimator). For meta-analyses including only 2 studies, the high length of the HKSJ interval limits its practical usage. In this case, the Bayesian interval using a weakly informative prior for the heterogeneity may help. Our recommendations are illustrated using a real-world meta-analysis dealing with the efficacy of an intramyocardial bone marrow stem cell transplantation during coronary artery bypass grafting.

Likelihood-Based Inference for Multi-Color Optical Mapping

2007 ◽  
Vol 6 (1) ◽  
Author(s):  
Liping Tong ◽  
Laurens Mets ◽  
Mary Sara McPeek
Keyword(s):  
Latent Variable ◽  
Optical Mapping ◽  
Physical Map ◽  
Profile Likelihood ◽  
Simulated Data ◽  
Likelihood Estimation ◽  
Error Rates ◽  
Mapping Procedure ◽  
Recognition Sites ◽  
Estimate Error

Multi-color optical mapping is a new technique being developed to obtain detailed physical maps (indicating relative positions of various recognition sites) of DNA molecules. We consider a study design in which the data consist of noisy observations of multiple copies of a DNA molecule marked with colors at recognition sites. The primary goal is to estimate a physical map. A secondary goal is to estimate error rates associated with the experiment, which are potentially useful for analysis and refinement of the biochemical steps in the mapping procedure. We propose statistical models for various sources of error and use maximum likelihood estimation (MLE) to construct a physical map and estimate error rates. To overcome difficulties arising in the maximization process, a latent-variable Markov chain version of the model is proposed, and the EM algorithm is used for maximization. In addition, a simulated annealing procedure is applied to maximize the profile likelihood over the discrete space of sequences of colors. We apply the methods to simulated data on the bacteriophage lambda genome.

scholarly journals Quantification of oxygen metabolic rates in Human brain with dynamic 17 O MRI: Profile likelihood analysis

2016 ◽  
Vol 78 (3) ◽  
pp. 1157-1167 ◽  
Author(s):  
Dmitry Kurzhunov ◽  
Robert Borowiak ◽  
Helge Hass ◽  
Philipp Wagner ◽  
Axel Joachim Krafft ◽  
...  
Keyword(s):  
Human Brain ◽  
Profile Likelihood ◽  
Metabolic Rates ◽  
Likelihood Analysis

Application of profile likelihood function to the uncertainty analysis of hydrometeorological extreme inference

2013 ◽  
Vol 56 (12) ◽  
pp. 3151-3160 ◽  
Author(s):  
Fan Lu ◽  
Hao Wang ◽  
DengHua Yan ◽  
DongDong Zhang ◽  
WeiHua Xiao
Keyword(s):  
Uncertainty Analysis ◽  
Likelihood Function ◽  
Profile Likelihood

On a criticism of the profile likelihood function

2007 ◽  
Vol 50 (1) ◽  
pp. 195-202 ◽  
Author(s):  
José A. Montoya ◽  
Eloísa Díaz-Francés ◽  
David A. Sprott
Keyword(s):  
Likelihood Function ◽  
Profile Likelihood

Statistical analysis of the item-count technique using Stata

2019 ◽  
Vol 19 (2) ◽  
pp. 390-434
Author(s):  
Chi-lin Tsai
Keyword(s):  
Data Analysis ◽  
Statistical Analysis ◽  
Count Data ◽  
Profile Likelihood ◽  
Likelihood Estimation ◽  
Great Flexibility ◽  
Recent Developments ◽  
List Experiment ◽  
Experiment Technique ◽  
Item Count Technique

In this article, I review recent developments of the item-count technique (also known as the unmatched-count or list-experiment technique) and introduce a new package, kict, for statistical analysis of the item-count data. This package contains four commands: kict deff performs a diagnostic test to detect the violation of an assumption underlying the item-count technique. kict ls and kict ml perform least-squares estimation and maximum likelihood estimation, respectively. Each encompasses a number of estimators, offering great flexibility for data analysis. kict pfci is a postestimation command for producing confidence intervals with better coverage based on profile likelihood. The development of the item-count technique is still ongoing. I will continue to update the kict package accordingly.

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