More Like this

1. Concurrent Functional Linear Regression Via Plug-in Empirical Likelihood

   https://doi.org/10.1007/s13171-024-00369-3  

   Chang, Hsin-wen; McKeague, Ian W (September 2024, Sankhya A)

   Abstract Functional data with non-smooth features (e.g., discontinuities in the functional mean and/or covariance) and monotonicity arise frequently in practice. This paper develops simultaneous inference for concurrent functional linear regression in this setting. We construct a simultaneous confidence band for a functional covariate effect of interest. Along with a Wald-type formulation, our approach is based on a powerful nonparametric likelihood ratio method. Our procedures are flexible enough to allow discontinuities in the coefficient functions and the covariance structure, while accounting for discretization of the observed trajectories under a fixed dense design. A simulation study shows that the proposed likelihood ratio-based procedure outperforms the Wald-type procedure in moderate sample sizes. We apply the proposed methods to studying the effect of age on the occupation time curve derived from wearable device data obtained in an NHANES study. 

   more » « less
2. Bayesian Sparse Mediation Analysis with Targeted Penalization of Natural Indirect Effects

   https://doi.org/10.1111/rssc.12518  

   Song, Yanyi; Zhou, Xiang; Kang, Jian; Aung, Max_T; Zhang, Min; Zhao, Wei; Needham, Belinda_L; Kardia, Sharon_L_R; Liu, Yongmei; Meeker, John_D; et al (November 2021, Journal of the Royal Statistical Society Series C: Applied Statistics)

   Abstract Causal mediation analysis aims to characterize an exposure's effect on an outcome and quantify the indirect effect that acts through a given mediator or a group of mediators of interest. With the increasing availability of measurements on a large number of potential mediators, like the epigenome or the microbiome, new statistical methods are needed to simultaneously accommodate high-dimensional mediators while directly target penalization of the natural indirect effect (NIE) for active mediator identification. Here, we develop two novel prior models for identification of active mediators in high-dimensional mediation analysis through penalizing NIEs in a Bayesian paradigm. Both methods specify a joint prior distribution on the exposure-mediator effect and mediator-outcome effect with either (a) a four-component Gaussian mixture prior or (b) a product threshold Gaussian prior. By jointly modelling the two parameters that contribute to the NIE, the proposed methods enable penalization on their product in a targeted way. Resultant inference can take into account the four-component composite structure underlying the NIE. We show through simulations that the proposed methods improve both selection and estimation accuracy compared to other competing methods. We applied our methods for an in-depth analysis of two ongoing epidemiologic studies: the Multi-Ethnic Study of Atherosclerosis (MESA) and the LIFECODES birth cohort. The identified active mediators in both studies reveal important biological pathways for understanding disease mechanisms. 

   more » « less
3. Orientation estimation for instrumented helmet using neural networks

   https://doi.org/10.1177/00202940221149062  

   Zaheer, Muhammad Hamad; Yoon, Se Young; Higginson, Brian K (September 2023, Measurement and Control)

   This work presents an integrated solution for head orientation estimation, which is a critical component for applications of virtual and augmented reality systems. The proposed solution builds upon the measurements from the inertial sensors and magnetometer added to an instrumented helmet, and an orientation estimation algorithm is developed to mitigate the effect of bias introduced by noise in the gyroscope signal. Convolutional Neural Network (CNN) techniques are introduced to develop a dynamic orientation estimation algorithm with a structure motivated by complementary filters and trained on data collected to represent a wide range of head motion profiles. The proposed orientation estimation method is evaluated experimentally and compared to both learning and non-learning-based orientation estimation algorithms found in the literature for comparable applications. Test results support the advantage of the proposed CNN-based solution, particularly for motion profiles with high acceleration disturbance that are characteristic of head motion. 

   more » « less
4. Decision tree boosted varying coefficient models

   https://doi.org/10.1007/s10618-022-00863-y  

   Zhou, Yichen; Hooker, Giles (September 2022, Data Mining and Knowledge Discovery)

   Abstract Varying coefficient models are a flexible extension of generic parametric models whose coefficients are functions of a set of effect-modifying covariates instead of fitted constants. They are capable of achieving higher model complexity while preserving the structure of the underlying parametric models, hence generating interpretable predictions. In this paper we study the use of gradient boosted decision trees as those coefficient-deciding functions in varying coefficient models with linearly structured outputs. In contrast to the traditional choices of splines or kernel smoothers, boosted trees are more flexible since they require no structural assumptions in the effect modifier space. We introduce our proposed method from the perspective of a localized version of gradient descent, prove its theoretical consistency under mild assumptions commonly adapted by decision tree research, and empirically demonstrate that the proposed tree boosted varying coefficient models achieve high performance qualified by their training speed, prediction accuracy and intelligibility as compared to several benchmark algorithms. 

   more » « less
5. Soft calibration for selection bias problems under mixed-effects models

   https://doi.org/10.1093/biomet/asad016  

   Gao, Chenyin; Yang, Shu; Kim, Jae Kwang (March 2023, Biometrika)

   Abstract Calibration weighting has been widely used to correct selection biases in nonprobability sampling, missing data and causal inference. The main idea is to calibrate the biased sample to the benchmark by adjusting the subject weights. However, hard calibration can produce enormous weights when an exact calibration is enforced on a large set of extraneous covariates. This article proposes a soft calibration scheme, where the outcome and the selection indicator follow mixed-effect models. The scheme imposes an exact calibration on the fixed effects and an approximate calibration on the random effects. On the one hand, our soft calibration has an intrinsic connection with best linear unbiased prediction, which results in a more efficient estimation compared to hard calibration. On the other hand, soft calibration weighting estimation can be envisioned as penalized propensity score weight estimation, with the penalty term motivated by the mixed-effect structure. The asymptotic distribution and a valid variance estimator are derived for soft calibration. We demonstrate the superiority of the proposed estimator over other competitors in simulation studies and using a real-world data application on the effect of BMI screening on childhood obesity. 

   more » « less