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Estimation and Inference in Semi-Functional Partially Linear Measurement Error Models

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Journal of Systems science & Complexity 2020年第4期33卷 1179-1199页

作者： ZHU Hanbing ZHANG Riquan ZHU Gen School of Statistics East China Normal UniversityShanghai 200241China Department of Biostatistics and Data Science School of Public Healththe University of Texas Health Science Center at HoustonHouston 77030USA

This article studies the estimation and statistical inference problems of semi-functional partially linear regression models when the covariates in the linear part are measured with additive error. To obtain the estimation of the parametric component, a corrected profile least-squares based estimation procedure is developed. Asymptotic properties of the proposed estimators are established under some mild assumptions. To test hypothesis on the parametric part, the authors propose a novel test statistic based on the difference between the corrected residual sums of squares under the null and alternative hypotheses, and show that its limiting distribution is a weighted sum of independent standard χ12. Finally, the authors illustrate the finite sample performance of the methods with some simulation studies and a real data application.

关键词： estimation corrected parametric

Deep-AutoMO: Deep automated multiobjective neural network for trustworthy lesion malignancy diagnosis in the early stage via digital breast tomosynthesis

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Computers in Biology and Medicine 2024年 183卷 109299-109299页

作者： Chen, Xi Lv, Jiahuan Wang, Zeyu Qin, Genggeng Zhou, Zhiguo School of Information and Communications Engineering Xi'an Jiaotong University Xi'an China Department of Radiology Nanfang Hospital Southern Medical University Guangzhou China Department of Biostatistics & Data Science University of Kansas Medical Center and University of Kansas Cancer Center Kansas CityKS66160 United States

Breast cancer is the most prevalent cancer in women, and early diagnosis of malignant lesions is crucial for developing treatment plans. Digital breast tomosynthesis (DBT) has emerged as a valuable tool for early breast cancer detection, as it can identify more lesions and improve the early detection rate. Deep learning has shown great potential in medical image-based cancer diagnosis, including DBT. However, deploying these models in clinical practice may be challenging due to concerns about reliability and robustness. In this study, we developed a novel deep automated multiobjective neural network (Deep-AutoMO) to build a trustworthy model and achieve balance, safety and robustness in a unified way. During the training stage, we introduced a multiobjective immune neural architecture search (MINAS) that simultaneously considers sensitivity and specificity as objective functions, aiming to strike a balance between the two. Each neural network in Deep-AutoMO comprises a combination of a ResNet block, a DenseNet block and a pooling layer. We employ Bayesian optimization to optimize the hyperparameters in the MINAS, enhancing the efficiency of the model training process. In the testing stage, evidential reasoning based on entropy (ERE) approach is proposed to build a safe and robust model. The experimental study on DBT images demonstrated that Deep-AutoMO achieves promising performance with a well-balanced trade-off between sensitivity and specificity, outperforming currently available methods. Moreover, the model's safety is ensured through uncertainty estimation, and its robustness is improved, making it a trustworthy tool for breast cancer diagnosis in clinical settings. We have shared the code on GitHub for other researchers to use. The code can be found at https://***/ChaoyangZhang-XJTU/Deep-AutoMO. © 2024

关键词： Diseases

Root-n consistent semiparametric learning with high-dimensional nuisance parameters under minimal sparsity

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arXiv 2023年

作者： Liu, Lin Wang, Xinbo Wang, Yuhao Institute of Natural Sciences MOE-LSC School of Mathematical Sciences CMA-Shanghai SJTU-Yale Joint Center for Biostatistics and Data Science Shanghai Jiao Tong University China Department of Bioinformatics and Biostatistics School of Life Sciences SJTU-Yale Joint Center for Biostatistics and Data Science Shanghai Jiao Tong University China Institute for Interdisciplinary Information Sciences Tsinghua University China Shanghai Artificial Intelligence Laboratory Shanghai Qi Zhi Institute China

Treatment effect estimation under unconfoundedness is a fundamental task in causal inference. In response to the challenge of analyzing high-dimensional datasets collected in substantive fields such as epidemiology, genetics, economics, and social sciences, various methods for treatment effect estimation with high-dimensional nuisance parameters (the outcome regression and the propensity score) have been developed in recent years. However, it is still unclear what is the necessary and sufficient sparsity condition on the nuisance parameters such that we can estimate the treatment effect at 1/√n-rate. In this paper, we propose a new Double-Calibration strategy that corrects the estimation bias of the nuisance parameter estimates computed by regularized high-dimensional techniques and demonstrate that the corresponding Doubly-Calibrated estimator achieves 1/√n-rate as long as one of the nuisance parameters is sparse with sparsity below √n/log p, where p denotes the ambient dimension of the covariates, whereas the other nuisance parameter can be arbitrarily complex and completely misspecified. The Double-Calibration strategy can also be applied to settings other than treatment effect estimation, e.g. regression coefficient estimation in the presence of a diverging number of controls in a semiparametric partially linear model, and local average treatment effect estimation with instrumental variables. Copyright © 2023, The Authors. All rights reserved.

关键词： Regression analysis

Quantifying uncertainty of individualized treatment effects in right-censored survival data: A comparison of Bayesian additive regression trees and causal survival forest

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arXiv 2025年

作者： Kabata, Daijiro Henderson, Nicholas C. Varadhan, Ravi Center for Mathematical and Data Science Kobe University Hyogo Kobe Japan Department of Biostatistics University of Michigan Ann ArborMI United States Department of Oncology School of Medicine Johns Hopkins University BaltimoreMD United States Department of Biostatistics Johns Hopkins University BaltimoreMD United States

Estimation of individualized treatment effects (ITE), also known as conditional average treatment effects (CATE), is an active area of methodology development. However, much less attention has been paid to the quantification of uncertainty of ITE/CATE estimates in right-censored survival data. Here we undertake an extensive simulation study to examine the coverage of interval estimates from two popular estimation algorithms, Bayesian additive regression trees (BART) and causal survival forest (CSF). We conducted simulation designs from 3 different settings: first, in a setting where BART was developed for an accelerated failure time model;second, where CSF was developed;and finally, a "neutral" simulation taken from a setting where neither BART nor CSF was developed. BART outperformed CSF in all three simulation settings. Both the BART and CSF algorithms involve multiple hyperparameters, and BART credible intervals had better coverage than the CSF confidence intervals under the default values, as well as under optimized values, of these hyperparameters. Copyright © 2025, The Authors. All rights reserved.

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Auditory nerve phase-locked response recorded from normal hearing adults using electrocochleography

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International Audiology 2023年第2期62卷 172-181页

作者： Hana A. Almohammad Mark E. Chertoff John A. Ferraro Francisco J. Diaz a Department of Rehabilitation Sciences Faculty of Applied Medical Sciences Jordan University of Science and Technology Irbid Jordan b Department of Hearing and Speech University of Kansas Medical Center Kansas City KS USA c Department of Biostatistics and Data Science University of Kansas Medical Center Kansas City KS USA

ObjectiveThe auditory nerve overlapped waveform response (ANOW), a new measure that can be recorded non-invasively from humans, holds promise for providing more accurate assessment of low frequency hearing thresholds than currently used objective measures. This research aims to investigate the robustness and the nature of the ANOW response in *** within-session recordings of the ANOW response using low-frequency Tone Bursts (TBs) were obtained at multiple stimulus levels. ANOW’s absolute amplitude and phase locking value (PLV) measures were analysed to obtain normative data and to test the reliability of the ANOW *** sampleThirteen normal hearing adults within the age range of 25 to 40 *** response was obtained to both 250 Hz and 500 Hz TBs and was traced down to 30–40 dB nHL. ANOW response showed significantly higher amplitude and stronger phase locking using 250 Hz TB compared to 500 Hz TB. High degree of test retest reliability of the ANOW response was found using 250 Hz TB at presentation levels higher than 40 dB *** response is recordable noninvasively using low-frequency TBs and shows higher robustness as the stimulus frequency decreases.

关键词： Phase locking electrocochleography electrophysiology psychoacoustics/hearing science

Contrastive estimation reveals topic posterior information to linear models

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Journal of Machine Learning Research 2021年第1期22卷 1-31页

作者： Tosh, Christopher Krishnamurthy, Akshay Hsu, Daniel Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Microsoft Research New YorkNY10012 United States Department of Computer Science and Data Science Institute Columbia University New YorkNY10027 United States

Contrastive learning is an approach to representation learning that utilizes naturally occurring similar and dissimilar pairs of data points to find useful embeddings of data. In the context of document classification under topic modeling assumptions, we prove that contrastive learning is capable of recovering a representation of documents that reveals their underlying topic posterior information to linear models. We apply this procedure in a semi-supervised setup and demonstrate empirically that linear classifiers trained on these representations perform well in document classification tasks with very few training examples. ©2021 Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu.

关键词： Statistics

spaCI: deciphering spatial cellular communications through adaptive graph model

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Briefings in Bioinformatics 2023年第1期24卷 bbac563-bbac563页

作者： Tang, Ziyang Zhang, Tonglin Yang, Baijian Su, Jing Song, Qianqian Department of Computer and Information Technology Purdue University IN United States Department of Statistics Purdue University IN United States Department of Biostatistics and Health Data Science Indiana University School of Medicine IN United States Center for Cancer Genomics and Precision Oncology Wake Forest Baptist Comprehensive Cancer Center Atrium Health Wake Forest Baptist Winston Salem NC United States Department of Cancer Biology Wake Forest School of Medicine Winston Salem NC United States

Cell-cell communications are vital for biological signalling and play important roles in complex diseases. Recent advances in single-cell spatial transcriptomics (SCST) technologies allow examining the spatial cell communication landscapes and hold the promise for disentangling the complex ligand-receptor (L-R) interactions across cells. However, due to frequent dropout events and noisy signals in SCST data, it is challenging and lack of effective and tailored methods to accurately infer cellular communications. Herein, to decipher the cell-to-cell communications from SCST profiles, we propose a novel adaptive graph model with attention mechanisms named spaCI. spaCI incorporates both spatial locations and gene expression profiles of cells to identify the active L-R signalling axis across neighbouring cells. Through benchmarking with currently available methods, spaCI shows superior performance on both simulation data and real SCST datasets. Furthermore, spaCI is able to identify the upstream transcriptional factors mediating the active L-R interactions. For biological insights, we have applied spaCI to the seqFISH+ data of mouse cortex and the NanoString CosMx Spatial Molecular Imager (SMI) data of non-small cell lung cancer samples. spaCI reveals the hidden L-R interactions from the sparse seqFISH+ data, meanwhile identifies the inconspicuous L-R interactions including THBS1-ITGB1 between fibroblast and tumours in NanoString CosMx SMI data. spaCI further reveals that SMAD3 plays an important role in regulating the crosstalk between fibroblasts and tumours, which contributes to the prognosis of lung cancer patients. Collectively, spaCI addresses the challenges in interrogating SCST data for gaining insights into the underlying cellular communications, thus facilitates the discoveries of disease mechanisms, effective biomarkers and therapeutic targets. © 2022 The Author(s). Published by Oxford University Press.

关键词： adaptive graph model single-cell spatial transcriptomics spatial cell graph triplet loss

A categorization of performance measures for estimated non-linear associations between an outcome and continuous predictors

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arXiv 2025年

作者： Ullmann, Theresa Heinze, Georg Abrahamowicz, Michal Perperoglou, Aris Sauerbrei, Willi Schmid, Matthias Dunkler, Daniela Institute of Clinical Biometrics Center for Medical Data Science Medical University of Vienna Vienna Austria Department of Epidemiology & Biostatistics McGill University Montreal Canada Statistics and Data Science Innovation Hub GSK Stevenage United Kingdom Institute of Medical Biometry and Statistics Faculty of Medicine and Medical Center University of Freiburg Freiburg Germany Department of Medical Biometry Informatics and Epidemiology University Hospital Bonn Bonn Germany

In regression analysis, associations between continuous predictors and the outcome are often assumed to be linear. However, modeling the associations as non-linear can improve model fit. Many flexible modeling techniques, like (fractional) polynomials and spline-based approaches, are available. Such methods can be systematically compared in simulation studies, which require suitable performance measures to evaluate the accuracy of the estimated curves against the true data-generating functions. Although various measures have been proposed in the literature, no systematic overview exists so far. To fill this gap, we introduce a categorization of performance measures for evaluating estimated non-linear associations between an outcome and continuous predictors. This categorization includes many commonly used measures. The measures can not only be used in simulation studies, but also in application studies to compare different estimates to each other. We further illustrate and compare the behavior of different performance measures through some examples and a Shiny app. © 2025, CC BY.

关键词： Prediction models

Uniform Consistency of Generalized Fréchet Means

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arXiv 2024年

作者： Aveni, Andrea Mukherjee, Sayan Department of Statistical Science Duke University United States Center for Scalable Data Analytics and Artificial Intelligence Department of Computer Science Universität Liepzig Germany Max Planck Institute for Mathematics in the Natural Sciences Duke University Departments of Statistical Science Mathematics Computer Science and Biostatistics & Bioinformatics United States

We study a generalization of the Fréchet mean on metric spaces, which we call φ-means. Our generalization is indexed by a convex function φ. We find necessary and sufficient conditions for φ-means to be finite and provide a tight bound for the diameter of the intrinsic mean set. We also provide sufficient conditions under which all the φ-means coincide in a single point. Then, we prove the consistency of the sample φ-mean to its population analogue. We also find conditions under which classes of φ-means converge uniformly, providing a Glivenko-Cantelli result. Finally, we illustrate applications of our results and provide algorithms for the computation of φ-*** Codes 0F05, 60F15, 60D05, 62E20, 62F10, 58K05 © 2024, CC BY.

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