The paper is concerned with inference for a parameter of interest in models that share a common interpretation for that parameter but that may differ appreciably in other respects. We study the general structure of models under which the maximum likelihood estimator of the parameter of interest is consistent under arbitrary misspecification of the nuisance part of the model. A specialization of the general results to matched-comparison and two-groups problems gives a more explicit and easily checkable condition in terms of a notion of symmetric parameterization, leading to a broadening and unification of existing results in those problems. The role of a generalized definition of parameter orthogonality is highlighted, as well as connections to Neyman orthogonality. The issues involved in obtaining inferential guarantees beyond consistency are briefly discussed.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11388321 | PMC |
| http://dx.doi.org/10.1073/pnas.2402736121 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Molecular mechanisms of cis-oxygen bridge neonicotinoids to Apis mellifera Linnaeus chemosensory protein: Surface plasmon resonance, multiple spectroscopy techniques, and molecular modeling.
Ecotoxicol Environ Saf
January 2025
State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China. Electronic address:
Honeybees, essential pollinators for maintaining biodiversity, are experiencing a sharp population decline, which has become a pressing environmental concern. Among the factors implicated in this decline, neonicotinoid pesticides, particularly those belonging to the fourth generation, have been the focus of extensive scrutiny due to their potential risks to honeybees. This study investigates the molecular basis of these risks by examining the binding interactions between Apis mellifera L.
View Article and Find Full Text PDFDeep Equilibrium Unfolding Learning for Noise Estimation and Removal in Optical Molecular Imaging.
Comput Med Imaging Graph
January 2025
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; National Key Laboratory of Kidney Diseases, Beijing 100853, China. Electronic address:
In clinical optical molecular imaging, the need for real-time high frame rates and low excitation doses to ensure patient safety inherently increases susceptibility to detection noise. Faced with the challenge of image degradation caused by severe noise, image denoising is essential for mitigating the trade-off between acquisition cost and image quality. However, prevailing deep learning methods exhibit uncontrollable and suboptimal performance with limited interpretability, primarily due to neglecting underlying physical model and frequency information.
View Article and Find Full Text PDFOptimization of HIV drugs through MCDM technique Analytic Hierarchy Process(AHP).
PLoS One
January 2025
Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia.
Topological indices are crucial tools for predicting the physicochemical and biological features of different drugs. They are numerical values obtained from the structure of chemical molecules. These indices, particularly the degree-based TIs are a useful tools for evaluating the connection between a compound's structure and its attributes.
View Article and Find Full Text PDFConvolutional neural network (CNN) configuration using a learning automaton model for neonatal brain image segmentation.
PLoS One
January 2025
Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran.
CNN is considered an efficient tool in brain image segmentation. However, neonatal brain images require specific methods due to their nature and structural differences from adult brain images. Hence, it is necessary to determine the optimal structure and parameters for these models to achieve the desired results.
View Article and Find Full Text PDFGlaucoma detection and staging from visual field images using machine learning techniques.
PLoS One
January 2025
School of Optometry and Vision Science, UNSW Sydney, Sydney, New South Wales, Australia.
Purpose: In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters.
Methods: A total of 265 PD plots and 265 numerical datasets of Humphrey 24-2 VF images were collected from 119 normal and 146 glaucomatous eyes to train the DL models to classify the images into four groups: normal, early glaucoma, moderate glaucoma, and advanced glaucoma.
Want 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!