AIMC Topic: Models, Statistical

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Contextually enhanced ES-dRNN with dynamic attention for short-term load forecasting.

Neural networks : the official journal of the International Neural Network Society
In this paper, we propose a new short-term load forecasting (STLF) model based on contextually enhanced hybrid and hierarchical architecture combining exponential smoothing (ES) and a recurrent neural network (RNN). The model is composed of two simul...

A support vector machine-based cure rate model for interval censored data.

Statistical methods in medical research
The mixture cure rate model is the most commonly used cure rate model in the literature. In the context of mixture cure rate model, the standard approach to model the effect of covariates on the cured or uncured probability is to use a logistic funct...

Unsupervised learning for medical data: A review of probabilistic factorization methods.

Statistics in medicine
We review popular unsupervised learning methods for the analysis of high-dimensional data encountered in, for example, genomics, medical imaging, cohort studies, and biobanks. We show that four commonly used methods, principal component analysis, K-m...

Calibrating machine learning approaches for probability estimation: A comprehensive comparison.

Statistics in medicine
Statistical prediction models have gained popularity in applied research. One challenge is the transfer of the prediction model to a different population which may be structurally different from the model for which it has been developed. An adaptatio...

An improved multiply robust estimator for the average treatment effect.

BMC medical research methodology
BACKGROUND: In observational studies, double robust or multiply robust (MR) approaches provide more protection from model misspecification than the inverse probability weighting and g-computation for estimating the average treatment effect (ATE). How...

IoT-Based Reinforcement Learning Using Probabilistic Model for Determining Extensive Exploration through Computational Intelligence for Next-Generation Techniques.

Computational intelligence and neuroscience
Computing intelligence is built on several learning and optimization techniques. Incorporating cutting-edge learning techniques to balance the interaction between exploitation and exploration is therefore an inspiring field, especially when it is com...

Probabilistic Modeling for Image Registration Using Radial Basis Functions: Application to Cardiac Motion Estimation.

IEEE transactions on neural networks and learning systems
Cardiovascular diseases (CVDs) are the leading cause of death, affecting the cardiac dynamics over the cardiac cycle. Estimation of cardiac motion plays an essential role in many medical clinical tasks. This article proposes a probabilistic framework...

Exploring examinees' responses to constructed response items with a supervised topic model.

The British journal of mathematical and statistical psychology
Textual data are increasingly common in test data as many assessments include constructed response (CR) items as indicators of participants' understanding. The development of techniques based on natural language processing has made it possible for re...

Machine learning and statistical models for analyzing multilevel patent data.

Scientific reports
A recent surge of patent applications among public hospitals in China has aroused significant research interest. A country's healthcare innovation capacity can be measured by its number of patents. This paper explores the link between the number of p...

Stability of clinical prediction models developed using statistical or machine learning methods.

Biometrical journal. Biometrische Zeitschrift
Clinical prediction models estimate an individual's risk of a particular health outcome. A developed model is a consequence of the development dataset and model-building strategy, including the sample size, number of predictors, and analysis method (...