Critical points on growth curves in autoregressive and mixed models

Pinho, Sheila Zambello de; Carvalho, Lídia Raquel de; Mischan, Martha Maria; Passos, José Raimundo de Souza

doi:10.1590/s0103-90162014000100004

Cited by 15 publications

(11 citation statements)

References 15 publications

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“…45 Mixed effect model gives better fit for model with independent errors, whereas the auto-regressive model performs better in correcting residual auto-correlation effect. 46 There are good scopes for further improvements that can be carried out on the proposed procedure. The proposed "auto-regressive type" joint modeling can be refined more efficiently.…”

Section: Discussionmentioning

confidence: 99%

“…Both approach could help to get insight on disease progression and comparisons on these approaches depend on the scenario in which they can be effectively applied 45 . Mixed effect model gives better fit for model with independent errors, whereas the auto‐regressive model performs better in correcting residual auto‐correlation effect 46 …”

Section: Discussionmentioning

confidence: 99%

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Classification algorithm for high‐dimensional protein markers in time‐course data

Vishwakarma

Bhattacharjee

Banerjee

et al. 2020

Statistics in Medicine

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Identification of biomarkers is an emerging area in oncology. In this article, we develop an efficient statistical procedure for the classification of protein markers according to their effect on cancer progression. A high-dimensional time-course dataset of protein markers for 80 patients motivates us for developing the model. The threshold value is formulated as a level of a marker having maximum impact on cancer progression. The classification algorithm technique for high-dimensional time-course data is developed and the algorithm is validated by comparing random components using both proportional hazard and accelerated failure time frailty models. The study elucidates the application of two separate joint modeling techniques using auto regressive-type model and mixed effect model for time-course data and proportional hazard model for survival data with proper utilization of Bayesian methodology. Also, a prognostic score is developed on the basis of few selected genes with application on patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Classification algorithm for high‐dimensional protein markers in time‐course data

Vishwakarma

Bhattacharjee

Banerjee

et al. 2020

Statistics in Medicine

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“…In each of the models estimated before and after the peak of the pandemic, at the second stage of modeling we added an autoregressive component of order one (AR(1)) to compare the results to the models that do not account for the violation of independence of residuals. This approach has been successfully used to model plant and animal growth where measuring the same unit can lead to violation of independence of residuals [15] [16]. Next, we will select the most useful model to modeling the pandemic by using Vuong's test [23].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Thus, there is a dire need for research on modeling the outbreak of COVID-19 to help officials in their decision-making processes regarding interventions and allocation of resources [22]. At the moment this manuscript was being written, the pandemic was ongoing and most of the epidemiological models developed focused on short-term predictions, identifying the daily peak of COVID-19 cases, predicting the duration of the pandemic, and estimating the possible impact of the measures implemented for minimizing exposure to the virus and decrease the fatality rate [1] [3] [11] [12][14] [15][17] [25].…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Polynomial and Nonlinear Growth Approach for Modeling the COVID-19 Outbreak in Mexico

R¹,

Estrada²,

de-la-Torre-Gutiérrez³

2021

Preprint

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Since December 2019, the coronavirus disease (COVID-19) has rapidly spread worldwide. The Mexican government has implemented public safety measures to minimize the spread of the virus. In this paper, the authors use statistical models in two stages to estimate the total number of coronavirus (COVID-19) cases per day at the state and national level in Mexico. Two types of models are proposed: first, a polynomial model of the growth for the first part of the outbreak until the inflection point of the pandemic curve and then a second nonlinear growth model is used to estimate the middle and the end of the outbreak. Model selection will be performed using Vuong’s test. The proposed models show overall fit similar to predictive models (e.g. time series, and machine learning); however, the interpretation of parameters is less complex for decision-makers and the residuals follow the expected distribution when fitting the models without autocorrelation being an issue.

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“…Para calcular o intervalo de confiança da abcissa do ponto de inflexão usaremos a expressão (Pinho et al, 2014), dada por:…”

Section: Intervalo De Confiança Do Ponto De Inflexãounclassified

Avaliação da qualidade de ajuste e predição de modelos não lineares: uma aplicação em dados de crescimento de frutos de cacaueiro

Eloy¹

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Avaliação da qualidade de ajuste e predição de modelos não lineares: uma aplicação em dados de crescimento de frutos de cacaueiro / Raquel Aline Oliveira Eloy.-versão revisada de acordo com a resolução CoPGr 6018 de 2011.-Piracicaba, 2018. 48 p. Dissertação (Mestrado)-USP / Escola Superior de Agricultura "Luiz de Queiroz". 1.Modelo logístico 2.Modelo Gompertz 3.Modelo Von Bertalanffy 4.Qualidade de ajuste de predição 5.Cacau. I. Título. AGRADECIMENTOS Agradeço em primeiro lugar a Deus, pelo dom da vida e por me sustentar nos momentos mais difíceis, aos meus pais, Geciro Soares (in memorian) e Raquel Souza, por tudo que fizeram por mim. Agradeço a minha família por ter me ajudado principalmente nesses dois anos fora de casa. Obrigada <3. Ao departamento de Estatística da Universidade Estatual da Paraíba, em especial a Ana Patrícia Bastos Peixoto e Tiago Almeida de Oliveira, pelo incentivo e apoio. Agradecer a minha orientadora e amiga Taciana Villela Savian, alguém que tem amor pelo que faz, mulher forte e amorosa e que no meio de tantas obrigações não esquece das coisas essenciais da vida <3. Ao Departamento de Ciências Exatas-LCE, em especial aos professores Sônia Stefano, Roseli Leandro, Cristian Villegas e César Lima, obrigada por todo conhecimento, agradecer aos funcionários do departamento, em especial a Solange, por toda ajuda e informação. Ao apoio financeiro da CAPES para o desenvolvimento e realização desse trabalho. Os meus vizinhos amigos, que me acompanham e me incentivam desde a época da alfabetização até a festa de comemoração por ter sido aprovada no mestrado.

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Critical points on growth curves in autoregressive and mixed models

Cited by 15 publications

References 15 publications

Classification algorithm for high‐dimensional protein markers in time‐course data

Classification algorithm for high‐dimensional protein markers in time‐course data

A Two-Stage Polynomial and Nonlinear Growth Approach for Modeling the COVID-19 Outbreak in Mexico

Avaliação da qualidade de ajuste e predição de modelos não lineares: uma aplicação em dados de crescimento de frutos de cacaueiro

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