Chi-square type test statistics are widely used in assessing the goodness-of-fit of a theoretical model. The exact distributions of such statistics can be quite different from the nominal chi-square distribution due to violation of conditions encountered with real data. In such instances, the bootstrap or Monte Carlo methodology might be used to approximate the distribution of the statistic. However, the sample quantile may be a poor estimate of the population counterpart when either the sample size is small or the number of different values of the replicated statistic is limited. Using statistical learning, this article develops a method that yields more accurate quantiles for chi-square type test statistics. Formulas for smoothing the quantiles of chi-square type statistics are obtained. Combined with the bootstrap methodology, the smoothed quantiles are further used to conduct equivalence testing in mean and covariance structure analysis. Two real data examples illustrate the applications of the developed formulas in quantifying the size of model misspecification under equivalence testing. The idea developed in the article can also be used to develop formulas for smoothing the quantiles of other types of test statistics or parameter estimates.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1080/00273171.2020.1858018 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Outcomes of Surgical Interventions for Patellofemoral Instability in the Presence of Trochlear Dysplasia: A Systematic Review and Meta-analysis.
Am J Sports Med
January 2025
Department of Pharmacology and Biostatistics, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
Background: Patellar instability is frequently encountered by orthopaedic surgeons. One of the major risk factors of this condition is underlying trochlear dysplasia (TD). Recent trends have indicated the use of multiple procedures to correct patellar instability under these conditions.
View Article and Find Full Text PDFMachine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs.
Sci Rep
January 2025
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700 032, India.
We have adopted the classification Read-Across Structure-Activity Relationship (c-RASAR) approach in the present study for machine-learning (ML)-based model development from a recently reported curated dataset of nephrotoxicity potential of orally active drugs. We initially developed ML models using nine different algorithms separately on topological descriptors (referred to as simply "descriptors" in the subsequent sections of the manuscript) and MACCS fingerprints (referred to as "fingerprints" in the subsequent sections of the manuscript), thus generating 18 different ML QSAR models. Using the chemical spaces defined by the modeling descriptors and fingerprints, the similarity and error-based RASAR descriptors were computed, and the most discriminating RASAR descriptors were used to develop another set of 18 different ML c-RASAR models.
View Article and Find Full Text PDFPrevalence of Intestinal Parasitic Infections and Associated Factors Among Food Handlers in East Africa: A Systematic Review and Meta-analysis.
Acta Parasitol
January 2025
Department of Medical Laboratory Science, College of Health Sciences, Woldia University, P.O. box 400, Woldia, Ethiopia.
Background: Intestinal parasitic infections are a significant public health concern, especially among food handlers, who can transmit these infections to the public through food preparation and handling. This systematic review and meta-analysis aimed to determine the pooled prevalence and associated factors of intestinal parasitic infections among food handlers in the East African region.
Methods: A systematic review and meta-analysis on intestinal parasitic infections among food handlers involved a comprehensive search across various databases, including Scopus, PubMed, ScienceDirect, Google Scholar, and the institution's library registers.
Historical redlining and clustering of present-day breast cancer factors.
Cancer Causes Control
January 2025
Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, State University of New York at Buffalo, 265 Farber Hall, Buffalo, NY, 14214, USA.
Purpose: Historical redlining, a 1930s-era form of residential segregation and proxy of structural racism, has been associated with breast cancer risk, stage, and survival, but research is lacking on how known present-day breast cancer risk factors are related to historical redlining. We aimed to describe the clustering of present-day neighborhood-level breast cancer risk factors with historical redlining and evaluate geographic patterning across the US.
Methods: This ecologic study included US neighborhoods (census tracts) with Home Owners' Loan Corporation (HOLC) grades, defined as having a score in the Historic Redlining Score dataset; 2019 Population Level Analysis and Community EStimates (PLACES) data; and 2014-2016 Environmental Justice Index (EJI) data.
Conceptual foundations of a REFRAME-based approach to discriminate across total knee implant designs based on the positions of functional centres of rotation.
Sci Rep
January 2025
Department of Orthopaedic and Trauma Surgery, Musculoskeletal University Center Munich (MUM), Campus Grosshadern, Ludwig Maximilians University Munich, Munich, Germany.
In modern knee arthroplasty, surgeons increasingly aim for individualised implant selection based on data-driven decisions to improve patient satisfaction rates. The identification of an implant design that optimally fits to a patient's native kinematic patterns and functional requirements could provide a basis towards subject-specific phenotyping. The goal of this study was to achieve a first step towards identifying easily accessible and intuitive features that allow for discrimination between implant designs based on kinematic data.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!