Reproducing kernel-based semiparametric functional smoothed score estimation with binary responses by Meichen Liu (8168148)

Accuracy of Fed-GLMM and meta-analysis estimates relative to the gold-standard pooled analysis. by Zhiyu Yan (74647)

Absolute relative bias of Fed-GLMM and meta-analysis estimates relative to gold-standard pooled analysis across simulation replicates. by Zhiyu Yan (74647)

Comparison of computation time and estimate accuracy for Fed-GLMM and meta-analysis relative to gold-standard pooled analysis with increasing computing nodes/EHR subsets. by Zhiyu Yan (74647)

Reverse engineering information flow using stimulus-encoding and stimulus-transfer estimation algorithms. by Gabriel Matías Lorenz (21094672)

Experimental setup and trajectory estimation methodology. by Nouman Javed (16624785)

Modeling Pregnancy Outcomes Through Sequentially Nested Regression Models by Xuan Bi (3096897)

“…Our approach explicitly bridges the connections across nested outcomes through computationally easy algorithms and enjoys theoretical guarantee of estimation and variable selection. …”

Solubility Prediction of Different Forms of Pharmaceuticals in Single and Mixed Solvents Using Symmetric Electrolyte Nonrandom Two-Liquid Segment Activity Coefficient Model by Getachew S. Molla (6416744)

“…The methodology incorporates key features of the symmetric eNRTL-SAC model structure to reduce the number of parameters and uses a hybrid of global search algorithms for parameter estimation. Moreover, a design of experiments is included in the methodology to generate and use experimental data appropriately for model parameter regression and model validation. …”

Distributed Estimation of Principal Support Vector Machines for Sufficient Dimension Reduction by Jun Jin (551362)

“…However, the computational burden of the principal support vector machines method constrains its use for massive data. To address this issue, we propose a naive and a refined distributed estimation algorithms for fast implementation when the sample size is large. …”

NuSeT efficiently addresses common segmentation challenges. by Linfeng Yang (516708)

Adaptive Inference for Change Points in High-Dimensional Data by Yangfan Zhang (6451946)

“…A simple combination of test statistics corresponding to several different <i>q</i>’s leads to a test with adaptive power property, that is, it can be powerful against both sparse and dense alternatives. On the estimation front, we obtain the convergence rate of the maximizer of our test statistic standardized by sample size when there is one change-point in mean and <i>q</i> = 2, and propose to combine our tests with a wild binary segmentation algorithm to estimate the change-point number and locations when there are multiple change-points. …”

Raw LC-MS/MS and RNA-Seq Mitochondria data by Stefano Martellucci (16284377)

“…The results were subsequently processed to filter out common contaminants, decoy hits from the reverse database, and protein groups identified by a single peptide. The data were filtered as follows: (a) binary expression of a protein (i.e., protein exclusively identified in either scLRP1+/+ or scLRP1-/-) was only considered relevant if all scLRP1+/+ samples or all scLRP1-/- samples expressed the protein. …”

Deep Discrete Encoders: Identifiable Deep Generative Models for Rich Data with Discrete Latent Layers by Seunghyun Lee (1372719)

“…Extensive simulation studies for high-dimensional data and deep architectures validate our theoretical results and demonstrate the excellent performance of our algorithms. …”

Table_1_Identification of sources of DIF using covariates in patient-reported outcome measures: a simulation study comparing two approaches based on Rasch family models.DOCX by Yseulys Dubuy (16809567)

“…The performance of the algorithms was assessed using: (i) the rates of false and correct detection of DIF, (ii) the DIF size and form recovery, and (iii) the bias in the latent variable level estimation. …”

GridScopeRodents: High-Resolution Global Typical Rodents Distribution Projections from 2021 to 2100 under Diverse SSP-RCP Scenarios by Yang Lan (20927512)

“…Here, we present GridScopeRodents, a high-resolution global dataset projecting the distribution of 10 rodent genera from 2021 to 2100 under four CMIP6-based Shared Socioeconomic Pathway–Representative Concentration Pathway (SSP–RCP) scenario combinations. Using occurrence data and environmental variable, we employ the Maximum Entropy (MaxEnt) algorithm within the species distribution modeling (SDM) framework to estimate occurrence probability at a spatial resolution of 1/12° (~10 km). …”