scholarly journals Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vector

2007 ◽  
pp. 127-141
Author(s):  
Sylvain Arlot ◽  
Gilles Blanchard ◽  
Étienne Roquain
Keyword(s):  
Random Vector ◽  
Confidence Regions ◽  
Multiple Tests

Bootstrap Analysis

2017 ◽  
Author(s):  
Russell Cheng
Keyword(s):  
Goodness Of Fit ◽  
Null Distribution ◽  
Real Data ◽  
Confidence Regions ◽  
Confidence Bands ◽  
Cumulative Distribution ◽  
Attractive Alternative ◽  
Bootstrap Analysis ◽  
Parametric Bootstrapping ◽  
Anderson Darling

Parametric bootstrapping (BS) provides an attractive alternative, both theoretically and numerically, to asymptotic theory for estimating sampling distributions. This chapter summarizes its use not only for calculating confidence intervals for estimated parameters and functions of parameters, but also to obtain log-likelihood-based confidence regions from which confidence bands for cumulative distribution and regression functions can be obtained. All such BS calculations are very easy to implement. Details are also given for calculating critical values of EDF statistics used in goodness-of-fit (GoF) tests, such as the Anderson-Darling A2 statistic whose null distribution is otherwise difficult to obtain, as it varies with different null hypotheses. A simple proof is given showing that the parametric BS is probabilistically exact for location-scale models. A formal regression lack-of-fit test employing parametric BS is given that can be used even when the regression data has no replications. Two real data examples are given.

scholarly journals Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hanji He ◽  
Guangming Deng
Keyword(s):  
Empirical Likelihood ◽  
Missing At Random ◽  
Confidence Regions ◽  
Likelihood Inference ◽  
High Dimensional ◽  
Finite Sample ◽  
The Mean ◽  
Data Missing ◽  
Consistency And Asymptotic Normality ◽  
The Impact

We extend the mean empirical likelihood inference for response mean with data missing at random. The empirical likelihood ratio confidence regions are poor when the response is missing at random, especially when the covariate is high-dimensional and the sample size is small. Hence, we develop three bias-corrected mean empirical likelihood approaches to obtain efficient inference for response mean. As to three bias-corrected estimating equations, we get a new set by producing a pairwise-mean dataset. The method can increase the size of the sample for estimation and reduce the impact of the dimensional curse. Consistency and asymptotic normality of the maximum mean empirical likelihood estimators are established. The finite sample performance of the proposed estimators is presented through simulation, and an application to the Boston Housing dataset is shown.

scholarly journals Experts’ opinion on the detection and management of prediabetes in Lebanon

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110294
Author(s):  
Emile Andari ◽  
Paola Atallah ◽  
Sami Azar ◽  
Akram Echtay ◽  
Selim Jambart ◽  
...  
Keyword(s):  
Risk Factors ◽  
Preliminary Report ◽  
Early Stage ◽  
National Guidelines ◽  
Lifestyle Modifications ◽  
Multiple Tests ◽  
Lebanese Population ◽  
To Come ◽  
Near Future

Given that the complications of type 2 diabetes can start at an early stage, early detection and appropriate management of prediabetes are essential. We aimed to develop an expert opinion on prediabetes in Lebanon to pave the way for national guidelines tailored for the Lebanese population in the near future. A panel of seven diabetes experts conducted a thorough literature review and discussed their opinions and experiences before coming up with a set of preliminary recommendations for the detection and management of prediabetes in Lebanon. Lebanese physicians employ multiple tests for the diagnosis of prediabetes and no national cut-off values exist. The panel agreed that prediabetes screening should be focused on patients exceeding 45 years of age with otherwise no risk factors and on adults with risk factors. The panel reached that fasting plasma glucose (FPG) and HbA1c should be used for prediabetes diagnosis in Lebanon. FPG values of 100–125 mg/dL or HbA1c values of 5.7%–6.4% were agreed upon as indicative of prediabetes. For the management of prediabetes, a three-step approach constituting lifestyle modifications, pharmacological treatment and bariatric surgery is recommended. There should be more focus on research on prediabetes in Lebanon. This preliminary report will be further discussed with the Lebanese Society of Endocrinology, Diabetes and Lipids in 2021 in order to come up with the first Lebanese national guidelines for the detection and management of prediabetes in Lebanon.

scholarly journals In search of a Goldilocks zone for credible AI

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kevin Allan ◽  
Nir Oren ◽  
Jacqui Hutchison ◽  
Douglas Martin
Keyword(s):  
Social Cognition ◽  
Single Source ◽  
Multiple Tests ◽  
The Social ◽  
New Directions ◽  
Global Problems ◽  
Scene Content ◽  
Different Sources ◽  
Source Accuracy

AbstractIf artificial intelligence (AI) is to help solve individual, societal and global problems, humans should neither underestimate nor overestimate its trustworthiness. Situated in-between these two extremes is an ideal ‘Goldilocks’ zone of credibility. But what will keep trust in this zone? We hypothesise that this role ultimately falls to the social cognition mechanisms which adaptively regulate conformity between humans. This novel hypothesis predicts that human-like functional biases in conformity should occur during interactions with AI. We examined multiple tests of this prediction using a collaborative remembering paradigm, where participants viewed household scenes for 30 s vs. 2 min, then saw 2-alternative forced-choice decisions about scene content originating either from AI- or human-sources. We manipulated the credibility of different sources (Experiment 1) and, from a single source, the estimated-likelihood (Experiment 2) and objective accuracy (Experiment 3) of specific decisions. As predicted, each manipulation produced functional biases for AI-sources mirroring those found for human-sources. Participants conformed more to higher credibility sources, and higher-likelihood or more objectively accurate decisions, becoming increasingly sensitive to source accuracy when their own capability was reduced. These findings support the hypothesised role of social cognition in regulating AI’s influence, raising important implications and new directions for research on human–AI interaction.

FAF-DRVFL: Fuzzy activation function based deep random vector functional links network for early diagnosis of Alzheimer disease

2021 ◽  
Vol 106 ◽  
pp. 107371
Author(s):  
Rahul Sharma ◽  
Tripti Goel ◽  
M. Tanveer ◽  
Shubham Dwivedi ◽  
R. Murugan
Keyword(s):  
Alzheimer Disease ◽  
Early Diagnosis ◽  
Random Vector ◽  
Activation Function

scholarly journals ID Learning Unit: Understanding and Interpreting Testing for Clostridium difficile

2014 ◽  
Vol 1 (1) ◽  
Author(s):  
Daniel A. Solomon ◽  
Danny A. Milner
Keyword(s):  
Clinical Practice ◽  
Clostridium Difficile ◽  
Basic Principles ◽  
Multiple Tests ◽  
Molecular Tests ◽  
Different Types

Abstract Understanding and interpreting the molecular tests for Clostridium difficile is challenging because there are several different types of assays and most laboratories combine multiple tests in order to assess for presence of disease. This learning unit demonstrates the basic principles of each test along with its strengths and weaknesses, and illustrates how the tests are used in clinical practice.

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