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Multivariate Varying Coefficient Spatiotemporal Model
As of 2020, 807,920 individuals in the U.S. had end-stage kidney disease (ESKD) with about 70% of patients on dialysis, a life-sustaining treatment....
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Statistical Inferences for Multivariate Generalized Gamma Regression Model
Generalized gamma (GG) distribution serves as a widely applied statistical tool, particularly suitable for scenarios where data distribution skews...
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Multivariate Beta Mixture Model: Probabilistic Clustering with Flexible Cluster Shapes
This paper introduces the multivariate beta mixture model (MBMM), a new probabilistic model for soft clustering. MBMM adapts to diverse cluster...
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Aquaculture 4.0: hybrid neural network multivariate water quality parameters forecasting model
This study examined the efficiency of hybrid deep neural network and multivariate water quality forecasting model in aquaculture ecosystem. Accurate...
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Knowledge-Data Fusion Model for Multivariate Load Short-Term Forecasting of Integrated Energy System
The short-term forecasting of multiple loads is crucial for the optimization and scheduling of integrated energy system (IES). However, the load...
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Multivariate Distributions
Chapter 15 discusses bivariate and multivariate probability distributions. In particular, it discusses the...
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Multivariate Discrete q-Distributions
This book is devoted to the study of multivariate discrete q-distributions, which is greatly facilitated by existing multivariate q-sequences and...
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A Versatile Model for Clustered and Highly Correlated Multivariate Data
For the analysis of multivariate data with an approximately one-dimensional latent structure, it is suggested to model this latent variable by a...
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Multivariate Model Predictive Control for High Permeability Photovoltaic Microgrid
In order to improve the efficiency of photovoltaic (PV) grid-connected response and reduce the total harmonic distortion rate, a multivariate model...
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Model-based clustering using a new multivariate skew distribution
Quite often real data exhibit non-normal features, such as asymmetry and heavy tails, and present a latent group structure. In this paper, we first...
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Bounded multivariate generalized Gaussian mixture model using ICA and IVA
A bounded multivariate generalized Gaussian mixture model with a full covariance matrix is proposed for modeling data in a bounded support region....
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A graph attention network-based model for anomaly detection in multivariate time series
Anomaly detection of multivariate time series plays a growingly crucial role in intelligent operation and maintenance. Most existing anomaly...
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Multivariate Regression Analysis
This final chapter introduces multivariate regression modelling. We cover the logic behind multiple regression modelling and explain the...
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A Comprehensive Multivariate Wind Speed Forecasting Model Utilizing Deep Learning Neural Networks
Predicting wind speed accurately is essential for the efficient generation of wind power. To enhance the precision of wind speed forecasting, this...
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DQN-PACG: load regulation method based on DQN and multivariate prediction model
Demand response plays a pivotal role in modern smart grid systems, aiding in balancing energy consumption. However, the increasing energy demands of...
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The orthogonal skew model: computationally efficient multivariate skew-normal and skew-t distributions with applications to model-based clustering
We introduce a parameterization for the multivariate skew normal and skew- t distributions, which enforces an orthogonal structure on the skewness...
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Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data
Alzheimer’s disease (AD) is the most common form of dementia. Early and accurate detection of AD is crucial to plan for disease modifying therapies...
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Introduction to Multivariate Calibration A Practical Approach
This book contains several new sections that provide even more in-depth knowledge on the topics. New content on the classical least-squares model,...
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Censored Multivariate Linear Regression Model
Often, real-life problems require modelling several response variables together. This work analyses a multivariate linear regression model when the...
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The research on a novel multivariate grey model and its application in carbon dioxide emissions prediction
Accurate small-sample prediction is an urgent, very difficult, and challenging task due to the quality of data storage restricted in most realistic...
