Generalized <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e4213" altimg="si40.svg"><mml:mi>k</mml:mi></mml:math>-means in GLMs with applications to the outbreak of COVID-19 in the United States

Zhang, Tonglin; Lin, Ge

doi:10.1016/j.csda.2021.107217

Cited by 12 publications

(4 citation statements)

References 32 publications

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“…If mj < m j , then we can find at least one pair of γ i and γ i , such that they are not in the same cluster but they are grouped to the same cluster. By the Lemma 1 of Zhang and Lin (2021), for…”

Section: Appendixmentioning

confidence: 99%

Subgroup Analysis for Longitudinal data via Semiparametric Additive Mixed Effect Model

Bo¹,

Zhang²

2021

Preprint

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In this paper, we propose a general subgroup analysis framework based on semiparametric additive mixed effect models in longitudinal analysis, which can identify subgroups on each covariate and estimate the corresponding regression functions simultaneously. In addition, the proposed procedure is applicable for both balanced and unbalanced longitudinal data. A backfitting combined with k-means algorithm is developed to estimate each semiparametric additive component across subgroups and detect subgroup structure on each covariate respectively. The actual number of groups is estimated by minimizing a Bayesian information criteria. The numerical studies demonstrate the efficacy and accuracy of the proposed procedure in identifying the subgroups and estimating the regression functions. In addition, we illustrate the usefulness of our method with an application to PBC data and provide a meaningful partition of the population.

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Section: Appendixmentioning

confidence: 99%

Subgroup Analysis for Longitudinal data via Semiparametric Additive Mixed Effect Model

Bo¹,

Zhang²

2021

Preprint

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“…Projection of the number of reported COVID-19 cases using k-means was discussed in (Vadyala et al, 2020). The prediction of COVID-19 cases using the K-Means algorithm in the United States of America was discussed in (Zhang & Lin, 2021).…”

Section: Unsupervised Learningmentioning

confidence: 99%

“…The environment and the reinforcement agent continually interact with each other, the agent selects the action and the response to these actions are given by the environment, which in turn presents new scenarios to the agent. The epidemiological Markov model was used to analyse the COVID-19 pandemic and clinical risk factors of patients in (W. Zhang et al, 2021).…”

Section: Reinforcement Learningmentioning

confidence: 99%

Battling COVID-19 using machine learning: A review

Chadaga

Prabhu

et al. 2021

Cogent Engineering

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Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2) known as Coronavirus surfaced in late 2019. It turned out to be a life-threatening disease and is causing chaos all around the world. The World Health Organisation (WHO) declared it a pandemic in March 2020. To handle COVID-19 related problems, research in many areas of science was introduced. Machine learning (ML), being one of the most successful stories in recent times is widely used to solve a variety of problems in our everyday life. Here, an overview of machine learning that tackles the pandemic is discussed in the beginning. Various datasets related to COVID-19 are also explored. Diagnosis of this viral disease using CT-Scans, X-ray images, sound analysis and blood tests using machine learning are presented in-depth. Drug and vaccine development using machine learning for COVID-19 are also discussed. Pandemic management and control were also examined. The main objective of this paper is to conduct a systematic review of machine learning applications that fight the deadly virus. This paper helps the researchers to understand and analyse the data trends related to COVID-19 and also

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“…The virus of COVID-19 primarily spreads between people via respiratory droplets from breathing, coughing, and sneezing, which can cause cluster infections in society. To avoid cluster infections, many countries have imposed travel restrictions in Spring 2020 [42]. According to the website of WHO, until August 2 2021, the outbreak has affected over 200 countries and territories with more than 200 million confirmed cases and 4 million deaths in the entire world.…”

Section: Dimension Reduction For Countmentioning

confidence: 99%

Flexible Principal Component Analysis for Exponential Family Distributions

Zhang

Yang

Song

et al. 2021

Preprint

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Traditional principal component analysis (PCA) is well known in high-dimensional data analysis, but it requires to express data by a matrix with observations to be continuous. To overcome the limitations, a new method called flexible PCA (FPCA) for exponential family distributions is proposed. The goal is to ensure that it can be implemented to arbitrary shaped region for either count or continuous observations. The methodology of FPCA is developed under the framework of generalized linear models. It provides statistical models for FPCA not limited to matrix expressions of the data. A maximum likelihood approach is proposed to derive the decomposition when the number of principal components (PCs) is known. This naturally induces a penalized likelihood approach to determine the number of PCs when it is unknown. By modifying it for missing data problems, the proposed method is compared with previous PCA methods for missing data. The simulation study shows that the performance of FPCA is always better than its competitors. The application uses the proposed method to reduce the dimensionality of arbitrary shaped sub-regions of images and the global spread patterns of COVID-19 under normal and Poisson distributions, respectively.

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Generalized k-means in GLMs with applications to the outbreak of COVID-19 in the United States

Cited by 12 publications

References 32 publications

Subgroup Analysis for Longitudinal data via Semiparametric Additive Mixed Effect Model

Subgroup Analysis for Longitudinal data via Semiparametric Additive Mixed Effect Model

Battling COVID-19 using machine learning: A review

Flexible Principal Component Analysis for Exponential Family Distributions

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