scholarly journals Asymptotic results for conditional measures of association of a random sum

2015 ◽  
Vol 60 ◽  
pp. 11-18 ◽  
Author(s):  
Alexandru V. Asimit ◽  
Yiqing Chen
Keyword(s):  
Random Sum ◽  
Asymptotic Results ◽  
Measures Of Association

scholarly journals Asymptotic Results for Conditional Measures of Association of a Random Sum

2014 ◽  
Author(s):  
Alexandru V. Asimit ◽  
Yiqing Chen
Keyword(s):  
Random Sum ◽  
Asymptotic Results ◽  
Measures Of Association

Asymptotic for LS estimators in the EV regression model for dependent errors

Filomat ◽  
2017 ◽  
Vol 31 (15) ◽  
pp. 4845-4856
Author(s):  
Konrad Furmańczyk
Keyword(s):  
Regression Model ◽  
Functional Dependence ◽  
Dependent Data ◽  
Errors In Variables ◽  
Mixing Conditions ◽  
Asymptotic Results ◽  
Dependence Measure ◽  
Wide Range ◽  
Ev Regression Model ◽  
Linear And Nonlinear

We study consistency and asymptotic normality of LS estimators in the EV (errors in variables) regression model under weak dependent errors that involve a wide range of linear and nonlinear time series. In our investigations we use a functional dependence measure of Wu [16]. Our results without mixing conditions complete the known asymptotic results for independent and dependent data obtained by Miao et al. [7]-[10].

Standard Asymptotic Theory

2017 ◽  
Author(s):  
Russell Cheng
Keyword(s):  
Asymptotic Theory ◽  
Asymptotic Variance ◽  
Statistical Significance ◽  
Regularity Conditions ◽  
Parameter Estimates ◽  
Asymptotic Results ◽  
Ml Estimation ◽  
True Parameter ◽  
Log Likelihood ◽  
Ml Estimator

This book relies on maximum likelihood (ML) estimation of parameters. Asymptotic theory assumes regularity conditions hold when the ML estimator is consistent. Typically an additional third derivative condition is assumed to ensure that the ML estimator is also asymptotically normally distributed. Standard asymptotic results that then hold are summarized in this chapter; for example, the asymptotic variance of the ML estimator is then given by the Fisher information formula, and the log-likelihood ratio, the Wald and the score statistics for testing the statistical significance of parameter estimates are all asymptotically equivalent. Also, the useful profile log-likelihood then behaves exactly as a standard log-likelihood only in a parameter space of just one dimension. Further, the model can be reparametrized to make it locally orthogonal in the neighbourhood of the true parameter value. The large exponential family of models is briefly reviewed where a unified set of regular conditions can be obtained.

Errors of Omission and Commission in "On the Validity of Measures of Association: The Nominal-Nominal, Two-by-Two Case"

1975 ◽  
Vol 80 (4) ◽  
pp. 975-981
Author(s):  
A. A. Hunter ◽  
Carlton A. Hornung
Keyword(s):  
Measures Of Association

scholarly journals Testing for treatment effect in covariate-adaptive randomized trials with generalized linear models and omitted covariates

2021 ◽  
pp. 096228022110082
Author(s):  
Yang Li ◽  
Wei Ma ◽  
Yichen Qin ◽  
Feifang Hu
Keyword(s):  
Generalized Linear Models ◽  
Treatment Effect ◽  
Linear Models ◽  
Type I Error ◽  
Type I ◽  
Test Statistic ◽  
Asymptotic Results ◽  
Adaptive Randomization ◽  
Adjustment Method ◽  
Inflated Type

Concerns have been expressed over the validity of statistical inference under covariate-adaptive randomization despite the extensive use in clinical trials. In the literature, the inferential properties under covariate-adaptive randomization have been mainly studied for continuous responses; in particular, it is well known that the usual two-sample t-test for treatment effect is typically conservative. This phenomenon of invalid tests has also been found for generalized linear models without adjusting for the covariates and are sometimes more worrisome due to inflated Type I error. The purpose of this study is to examine the unadjusted test for treatment effect under generalized linear models and covariate-adaptive randomization. For a large class of covariate-adaptive randomization methods, we obtain the asymptotic distribution of the test statistic under the null hypothesis and derive the conditions under which the test is conservative, valid, or anti-conservative. Several commonly used generalized linear models, such as logistic regression and Poisson regression, are discussed in detail. An adjustment method is also proposed to achieve a valid size based on the asymptotic results. Numerical studies confirm the theoretical findings and demonstrate the effectiveness of the proposed adjustment method.

From Measures of Association to Multilevel Models: Sociology Journals and the Quantitative Literacy Gap

2020 ◽  
pp. 0092055X2098042
Author(s):  
Thomas J. Linneman
Keyword(s):  
Multilevel Models ◽  
Ordinary Least Squares ◽  
Quantitative Literacy ◽  
Least Squares Regression ◽  
Journal Articles ◽  
Measures Of Association ◽  
Chi Square ◽  
Literacy Gap ◽  
Statistics Course ◽  
Sociology Journals

While most sociology majors must take a statistics course, the content of this course varies widely across departments. Starting from the assumption that sociology students should be able to engage effectively with the sociological literature, this article examines the statistical techniques used in 2,804 journal articles—from four generalist sociology journals from 1990 to 2019 and 11 additional sociology journals from 2019—in order to assess which techniques have risen or fallen in prevalence. Although stalwarts such as ordinary least squares regression, chi-square tests, and t tests maintain strong presences, the rise of logistic regression, interaction effects, and multilevel models has been dramatic. After assessing the proportion of articles students hypothetically could understand given various levels of statistical training, the article ends with suggestions for how to revamp the statistics course to help our students become more numerate citizens, both in their sociology courses and in the world at large.

scholarly journals Asymptotic results with estimating equations for time-evolving clustered data

2021 ◽  
Vol 214 ◽  
pp. 41-61
Author(s):  
Laura Dumitrescu ◽  
Ioana Schiopu-Kratina
Keyword(s):  
Clustered Data ◽  
Estimating Equations ◽  
Asymptotic Results

scholarly journals Weighted mixed-effects dose–response models for tables of correlated contrasts

2021 ◽  
Vol 21 (2) ◽  
pp. 320-347
Author(s):  
Nicola Orsini
Keyword(s):  
Dose Response ◽  
Response Pattern ◽  
Mixed Effects ◽  
Data Generation ◽  
Measures Of Association ◽  
Response Models ◽  
Response Data ◽  
Hazard Ratios ◽  
Health Related ◽  
Generation Mechanisms

Recognizing a dose–response pattern based on heterogeneous tables of contrasts is hard. Specification of a statistical model that can consider the possible dose–response data-generating mechanism, including its variation across studies, is crucial for statistical inference. The aim of this article is to increase the understanding of mixed-effects dose–response models suitable for tables of correlated estimates. One can use the command drmeta with additive (mean difference) and multiplicative (odds ratios, hazard ratios) measures of association. The postestimation command drmeta_graph greatly facilitates the visualization of predicted average and study-specific dose–response relationships. I illustrate applications of the drmeta command with regression splines in experimental and observational data based on nonlinear and random-effects data-generation mechanisms that can be encountered in health-related sciences.

scholarly journals Can Vaccination Trigger Autoimmune Disorders? A Meta-Analysis

Vaccines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 821
Author(s):  
Marek Petráš ◽  
Ivana Králová Lesná ◽  
Jana Dáňová ◽  
Alexander M. Čelko
Keyword(s):  
Meta Analysis ◽  
Autoimmune Disorders ◽  
Risk Of Bias ◽  
Extensive Literature ◽  
Primary Analysis ◽  
Measures Of Association ◽  
Assessment Development ◽  
Extensive Literature Search ◽  
The Stability

Vaccination as an important tool in the fight against infections has been suggested as a possible trigger of autoimmunity over the last decades. To confirm or refute this assumption, a Meta-analysis of Autoimmune Disorders Association With Immunization (MADAWI) was conducted. Included in the meta-analysis were a total of 144 studies published in 1968–2019 that were available in six databases and identified by an extensive literature search conducted on 30 November 2019. The risk of bias classification of the studies was performed using the Newcastle–Ottawa Quality Assessment Scale. The strength of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation. While our primary analysis was conducted in terms of measures of association employed in studies with a low risk of bias, the robustness of the MADAWI outcome was tested using measures independent of each study risk of bias. Additionally, subgroup analyses were performed to determine the stability of the outcome. The pooled association of 0.99 (95% confidence interval, 0.97–1.02), based on a total of 364 published estimates, confirmed an equivalent occurrence of autoimmune disorders in vaccinated and unvaccinated persons. The same level of association reported by studies independently of the risk of bias was supported by a sufficient number of studies, and no serious limitation, inconsistency, indirectness, imprecision, and publication bias. A sensitivity analysis did not reveal any discrepancy in the primary result. Current common vaccination is not the cause of any of the examined autoimmune disorders in the medium and long terms.

scholarly journals On nonparametric regression for bivariate circular long-memory time series

2021 ◽  
Author(s):  
Jan Beran ◽  
Britta Steffens ◽  
Sucharita Ghosh
Keyword(s):  
Time Series ◽  
Nonparametric Regression ◽  
Long Range ◽  
Long Memory ◽  
Regression Function ◽  
Confidence Bands ◽  
Long Range Dependence ◽  
Asymptotic Results ◽  
Memory Time ◽  
Circular Time Series

AbstractWe consider nonparametric regression for bivariate circular time series with long-range dependence. Asymptotic results for circular Nadaraya–Watson estimators are derived. Due to long-range dependence, a range of asymptotically optimal bandwidths can be found where the asymptotic rate of convergence does not depend on the bandwidth. The result can be used for obtaining simple confidence bands for the regression function. The method is illustrated by an application to wind direction data.

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