In this paper, we propose a new estimation method in estimating optimal dynamic treatment regimes. The quadratic inference functions in myopic regret-regression (QIF-MRr) can be used to estimate the parameters of the mean response at each visit, conditional on previous states and actions. Singularity issues may arise during computation when estimating the parameters in ODTR using QIF-MRr due to multicollinearity. Hence, the ridge penalty was introduced in rQIF-MRr to tackle the issues. A simulation study and an application to anticoagulation dataset were conducted to investigate the model's performance in parameter estimation. The results show that estimations using rQIF-MRr are more efficient than the QIF-MRr.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9302796 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0271542 | PLOS |
Publication Analysis
Top Keywords
Similar Publications
The role of a quadratic term in estimating the average treatment effect from longitudinal randomized controlled trials with missing data.
Psychol Methods
December 2024
Department of Psychology, University of Notre Dame.
Longitudinal randomized controlled trials (RCTs) have been commonly used in psychological studies to evaluate the effectiveness of treatment or intervention strategies. Outcomes in longitudinal RCTs may follow either straight-line or curvilinear change trajectories over time, and missing data are almost inevitable in such trials. The current study aims to investigate (a) whether the estimate of average treatment effect (ATE) would be biased if a straight-line growth (SLG) model is fit to longitudinal RCT data with quadratic growth and missing completely at random (MCAR) or missing at random (MAR) data, and (b) whether adding a quadratic term to an SLG model would improve the ATE estimation and inference.
View Article and Find Full Text PDFIntegration of Sense and Control for Uncertain Systems Based on Delayed Feedback Active Inference.
Entropy (Basel)
November 2024
School of Automation, Northwestern Polytechnical University, Xi'an 710129, China.
Asa result of the time lag in transmission, the data obtained by the sensor is delayed and does not reflect the state at the current moment. The effects of input delay are often overlooked in active inference (AIF), which may lead to significant deviations in state estimation and increased prediction errors, particularly when the system is subjected to a sudden external stimulus. In this paper, a theoretical framework of delayed feedback active inference (DAIF) is proposed to enhance the applicability of AIF to real systems.
View Article and Find Full Text PDFControl when confidence is costly.
ArXiv
October 2024
Departments of Electrical and Computer Engineering and Computer Science, Rice University, Houston, TX 77005.
We develop a version of stochastic control that accounts for computational costs of inference. Past studies identified efficient coding without control, or efficient control that neglects the cost of synthesizing information. Here we combine these concepts into a framework where agents rationally approximate inference for efficient control.
View Article and Find Full Text PDFGlobalSR: Global context network for single image super-resolution via deformable convolution attention and fast Fourier convolution.
Neural Netw
December 2024
School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China. Electronic address:
Vision Transformer have achieved impressive performance in image super-resolution. However, they suffer from low inference speed mainly because of the quadratic complexity of multi-head self-attention (MHSA), which is the key to learning long-range dependencies. On the contrary, most CNN-based methods neglect the important effect of global contextual information, resulting in inaccurate and blurring details.
View Article and Find Full Text PDFProtein domain embeddings for fast and accurate similarity search.
Genome Res
October 2024
Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana 47408, USA
Recently developed protein language models have enabled a variety of applications with the protein contextual embeddings they produce. Per-protein representations (each protein is represented as a vector of fixed dimension) can be derived via averaging the embeddings of individual residues, or applying matrix transformation techniques such as the discrete cosine transformation (DCT) to matrices of residue embeddings. Such protein-level embeddings have been applied to enable fast searches of similar proteins; however, limitations have been found; for example, PROST is good at detecting global homologs but not local homologs, and knnProtT5 excels for proteins with single domains but not multidomain proteins.
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!