Brief Review of Functional Data Analysis: A Case Study on Regional Demographic and Economic Data

Padilla, Adrian; González-Villacorte, Mabel; Amaro, Isidro R.; Infante, Saba

doi:10.1007/978-3-030-62833-8_14

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…In the future researches will be valuable to perform a forecast study of confirmed cases and the influence of political responses in the growth curve of new cases per day using other FDA techniques that allow us to get a broad analysis of the COVID-19 pandemic in more Latin America countries. Therefore, we encourage to continue this study employing more FDA procedures, for instance, those mentioned in [13].…”

Section: Conclusion and Further Studiesmentioning

confidence: 99%

“…Over the years various studies regarding functional principal component analysis (FPCA) have been carried out, beginning from [3], where a technique for principal components analysis of data consisting of functions observed at a determined number of argument values is described, and [15] that showed how the theory of L-splines can support generalizations of linear modeling and principal components analysis to samples drawn from random functions. More recent researches include: [6] a study of Sleep Heart Health Study (SHHS) using multilevel functional principal component analysis (MFPCA), [13] an exploratory analysis employing FPCA, functional clustering and principal component analysis on fertility, infant mortality, life expectancy, Multidimensional Poverty Index (MPI), Human Development Index (HDI) and Gross Domestic Product (GDP) growth indexes data sets from twenty Latin American countries during time frames around 1960-2018, [1] an evaluation of Spatial functional data analysis (sFDA) as a tool to regionalize seasonality and intensity precipitation patterns in Ecuador, [5] an analysis to verify the potential utility, as well as the theoretical and practical consistency of the results of functional data analysis applied to the financial risk of credit unions, subject to the control of the Superintendency of Banks of Ecuador. In particular, concerning COVID-19 in [20] an exploration of the modes of variation of the data through a FPCA was done, and a study of the canonical correlation between confirmed and death cases, together with cluster analysis and forecasting based on the dynamic FPCA.…”

Section: Introductionmentioning

confidence: 99%

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An Exploratory Analysis of COVID-19 in Latin America Using Functional Principal Component Analysis

Chaglla

Amaro

Infante

2021

Communications in Computer and Information Science

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Even though there already exists a wide variety of epidemiological models, it's worthwhile to apply Functional Data Analysis (FDA) techniques to study the shapes of the COVID-19 pandemic in Latin America. In the present work we use Functional Principal Component Analysis (FPCA) to make an exploratory study on a dataset formed by the total cases per million, new cases, new tests, and stringency index of 6 Latin American countries, namely: Mexico, Ecuador, Chile, Peru, Cuba, and Colombia; obtained from the first confirmed case reported to January 2021, measured daily. We identify an increasing pattern in all of the variables and the interesting case of Cuba concerning the management of the pandemic, as well as the influence of stringency index over the growth curve of positive cases, and the mean perturbations with functional principal components (FPC) of the variables. Finally, we suggest more FDA techniques to carry out further studies to get a broad perspective of COVID-19 in Latin America.

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Section: Conclusion and Further Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Exploratory Analysis of COVID-19 in Latin America Using Functional Principal Component Analysis

Chaglla

Amaro

Infante

2021

Communications in Computer and Information Science

Self Cite

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“…The essential idea behind FDA is to transform data from repeated measurements over time into a function for individuals and then analyze a set of functions. Recently, FDA has been widely applied to analyze time-series data to represent complex time-dependent phenomena in various fields, including public health [ 16 ], life science [ 17 ], and socioeconomics [ 18 ]. Other applications of FDA to several fields are summarized in Ullah and Finch [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Functional data analysis-based yield modeling in year-round crop cultivation

Matsui,

Mochida

2024

Horticulture Research

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Crop yield prediction is essential for effective agricultural management. We introduce a methodology for modeling the relationship between environmental parameters and crop yield in longitudinal crop cultivation, exemplified by strawberry and tomato production based on year-round cultivation. Employing functional data analysis (FDA), we developed a model to assess the impact of these factors on crop yield, particularly in the face of environmental fluctuation. Specifically, we demonstrated that a varying-coefficient functional regression model (VCFRM) is utilized to analyze time-series data, enabling to visualize seasonal shifts and the dynamic interplay between environmental conditions such as solar radiation and temperature and crop yield. The interpretability of our FDA-based model yields insights for optimizing growth parameters, thereby augmenting resource efficiency and sustainability. Our results demonstrate the feasibility of VCFRM-based yield modeling, offering strategies for stable, efficient crop production, pivotal in addressing the challenges of climate adaptability in plant factory-based horticulture.

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