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Regional and temporal patterns of influenza: Application of functional data analysis

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Infectious Disease Modelling 2021年第1期6卷 1061-1072页

作者： Azizur Rahman Depeng Jiang Department of Community Health Sciences University of ManitobaWinnipegManitobaCanada Department of Statistics Jahangirnagar UniversitySavarDhakaBangladesh School of Sciences Nanjing Forest UniversityNanjingJiangsuChina

Background:The accurate estimation of temporal patterns of influenza may help in utilizing hospital resources and guiding influenza *** paper proposes functional data analysis(FDA)to improve the prediction of temporal patterns of ***:We illustrate FDA methods using the weekly Influenza-like Illness(ILI)activity level data from the *** propose to use the Fourier basis function for transforming discrete weekly data to the smoothed functional ILI *** analysis of variance(FANOVA)is used to examine the regional differences in temporal patterns and the impact of state's political ***:The ILI activity has a very distinct peak at the beginning and end of the *** are significant differences in average level of ILI activities among geographic ***,the temporal patterns in terms of the peak and flat time are quite consistent across *** geographic and temporal patterns of ILI activities also depend on the political make-up of *** states affiliated with Republicans had higher ILI activities than those affiliated with Democrats across the whole *** influence of political party affiliation on temporal pattern is quite different among geographic ***:functional data analysis can help us to reveal the temporal variability in average ILI levels,rate of change in ILI levels,and the effect of geographical *** should be given to wider application of FDA to generate more accurate estimates in public health and biomedical research.

关键词： functional data analysis functional ANOVA Influenza-like illness Temporal patterns Political orientation

Curve Classification Based onMean-Variance Feature Weighting and Its Application

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Computers, Materials & Continua 2024年第5期79卷 2465-2480页

作者： Zewen Zhang Sheng Zhou Chunzheng Cao School of Mathematics and Statistics Nanjing University of Information Science and TechnologyNanjing210044China Center for Applied Mathematics of Jiangsu Province Nanjing University of Information Science and TechnologyNanjing210044China

The classification of functional data has drawn much attention in recent *** main challenge is representing infinite-dimensional functional data by finite-dimensional features while utilizing those features to achieve better classification *** this paper,we propose a mean-variance-based(MV)feature weighting method for classifying functional data or functional *** the feature extraction stage,each sample curve is approximated by B-splines to transfer features to the coefficients of the spline *** that,a feature weighting approach based on statistical principles is introduced by comprehensively considering the between-class differences and within-class variations of the *** also introduce a scaling parameter to adjust the gap between the weights of *** new feature weighting approach can adaptively enhance noteworthy local features while mitigating the impact of confusing *** algorithms for feature weighted K-nearest neighbor and support vector machine classifiers are both ***,the new approach can be well integrated into existing functional data classifiers,such as the generalized functional linear model and functional linear discriminant analysis,resulting in a more accurate *** performance of the mean-variance-based classifiers is evaluated by simulation studies and real *** results show that the newfeatureweighting approach significantly improves the classification accuracy for complex functional data.

关键词： functional data analysis classification feature weighting B-splines

Estimation in Partially Observed functional Linear Quantile Regression

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Journal of Systems Science & Complexity 2022年第1期35卷 313-341页

作者： XIAO Juxia XIE Tianfa ZHANG Zhongzhan Faculty of Science Beijing University of TechnologyBeijing 100124China School of Mathematics and Computer Science Shanxi Normal UniversityLinfen 041000China Faculty of Science Beijing University of TechnologBeijing 100124China.

Currently,working with partially observed functional data has attracted a greatly increasing attention,since there are many applications in which each functional curve may be observed only on a subset of a common domain,and the incompleteness makes most existing methods for functional data analysis *** this paper,motivated by the appealing characteristics of conditional quantile regression,the authors consider the functional linear quantile regression,assuming the explanatory functions are observed partially on dense but discrete point grids of some random subintervals of the domain.A functional principal component analysis(FPCA)based estimator is proposed for the slope function,and the convergence rate of the estimator is *** addition,the finite sample performance of the proposed estimator is evaluated through simulation studies and a real data application.

关键词： Conditional quantile regression functional data analysis functional principal component analysis incomplete curves

Industrial Food Quality analysis Using New k-Nearest-Neighbour methods

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Computers, Materials & Continua 2021年第5期67卷 2681-2694页

作者： Omar Fetitah Ibrahim M.Almanjahie Mohammed Kadi Attouch Salah Khardani Laboratory of Statistics and Stochastic Processes University of Djillali LiabesSidi Bel Abbes22000Algeria Department of Mathematics College of ScienceKing Khalid UniversityAbha62529Saudi Arabia Statistical Research and Studies Support Unit King Khalid UniversityAbha62529Saudi Arabia Facultédes sciences de Tunis Laboratoire des Réseaux Intelligents et NanotechnologieTunisTunisia

The problem of predicting continuous scalar outcomes from functional predictors has received high levels of interest in recent years in many fields,especially in the food *** k-nearest neighbor(k-NN)method of Near-Infrared Reflectance(NIR)analysis is practical,relatively easy to implement,and becoming one of the most popular methods for conducting food quality based on NIR *** k-NN is often named k nearest neighbor classifier when it is used for classifying categorical variables,while it is called k-nearest neighbor regression when it is applied for predicting noncategorical *** objective of this paper is to use the functional Near-Infrared Reflectance(NIR)spectroscopy approach to predict some chemical components with some modern statistical models based on the kernel and k-Nearest Neighbour *** this paper,three NIR spectroscopy datasets are used as examples,namely Cookie dough,sugar,and tecator ***,we propose three models for this kind of data which are functional Nonparametric Regression,functional Robust Regression,and functional Relative Error Regression,with both kernel and k-NN approaches to compare between *** experimental result shows the higher efficiency of k-NN predictor over the kernel *** predictive power of the k-NN method was compared with that of the kernel method,and several real data sets were used to determine the predictive power of both methods.

关键词： functional data analysis classical regression robust regression relative error regression kernel method k-NN method near-infrared spectroscopy

Robust Estimation for Partial functional Linear Regression Model Based on Modal Regression

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Journal of Systems Science & Complexity 2020年第2期33卷 527-544页

作者： YU Ping ZHU Zhongyi SHI Jianhong AI Xikai School of Mathematics and Computer Science Shanxi Normal UniversityLinfen 041000China Department of Statistics Fudan UniversityShanghai 200433China

This paper presents a robust estimation procedure by using modal regression for the partial functional linear regression,which combines the common linear model with the functional linear regression *** outstanding merit of the new method is that it is robust against outliers or heavy-tail error distributions while performs no worse than the least-square-based estimation method for normal error *** slope function is fitted by *** suitable conditions,the authors obtain the convergence rates and asymptotic normality of the ***,simulation studies and a real data example are conducted to examine the finite sample performance of the proposed *** the simulation results and the real data analysis confirm that the newly proposed method works very well.

关键词： B-spline functional data analysis functional linear model modal regression

Composite Quantile Estimation in Partial functional Linear Regression Model Based on Polynomial Spline

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Acta Mathematica Sinica,English Series 2021年第10期37卷 1627-1644页

作者： Ping YU Ting LI Zhong Yi ZHU Jian Hong SHI School of Mathematics and Computer Science Shanxi Normal UniversityLinfen 041000P.R.China School of Statistics and Management Shanghai University of Finance and EconomicsShanghai 200433P.R.China Department of Statistics Fudan UniversityShanghai 200433P.R.China

In this paper,we consider composite quantile regression for partial functional linear regression model with polynomial spline *** some mild conditions,the convergence rates of the estimators and mean squared prediction error,and asymptotic normality of parameter vector are *** studies demonstrate that the proposed new estimation method is robust and works much better than the least-squares based method when there are outliers in the dataset or the random error follows heavy-tailed ***,we apply the proposed methodology to a spectroscopic data sets to illustrate its usefulness in practice.

关键词： Asymptotic normality composite quantile regression functional data analysis polynomial spline rates of convergence

The k Nearest Neighbors Estimator of the M-Regression in functional Statistics

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Computers, Materials & Continua 2020年第12期65卷 2049-2064页

作者： Ahmed Bachir Ibrahim Mufrah Almanjahie Mohammed Kadi Attouch Department of Mathematics College of ScienceKing Khalid UniversityAbha62529Saudi Arabia Statistical Research and Studies Support Unit King Khalid UniversityAbha62529Saudi Arabia Djillali Liabes University Sidi Belabbes22000Algeria

It is well known that the nonparametric estimation of the regression function is highly sensitive to the presence of even a small proportion of outliers in the *** solve the problem of typical observations when the covariates of the nonparametric component are functional,the robust estimates for the regression parameter and regression operator are *** main propose of the paper is to consider data-driven methods of selecting the number of neighbors in order to make the proposed processes fully *** use thek Nearest Neighbors procedure(kNN)to construct the kernel estimator of the proposed robust *** some regularity conditions,we state consistency results for kNN functional estimators,which are uniform in the number of neighbors(UINN).Furthermore,a simulation study and an empirical application to a real data analysis of octane gasoline predictions are carried out to illustrate the higher predictive performances and the usefulness of the kNN approach.

关键词： functional data analysis quantile regression kNN method uniform nearest neighbor(UNN)consistency functional nonparametric statistics almost complete convergence rate

A Study of Detection of Outliers for Working and Non-Working Days Air Quality in Kolkata, India: A Case Study

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Journal of Environmental Protection 2023年第8期14卷 685-709页

作者： Mohammad Ahmad Weihu Cheng Zhao Xu Abdul Kalam Faculty of Science Beijing University of Technology Beijing China

A variety of factors affect air quality, making it a difficult issue. The level of clean air in a certain area is referred to as air quality. It is challenging for conventional approaches to correctly discover aberrant values or outliers due to the significant fluctuation of this sort of data, which is influenced by Climate change and the environment. With accelerating industrial expansion and rising population density in Kolkata City, air pollution is continuously rising. This study involves two phases, in the first phase imputation of missing values and second detection of outliers using Statistical Process Control (SPC), and functional data analysis (FDA), studies to achieve the efficacy of the outlier identification methodology proposed with working days and Nonworking days of the variables NO2, SO2, and O3, which were used for a year in a row in Kolkata, India. The results show how the functional data approach outshines traditional outlier detection methods. The outcomes show that functional data analysis vibrates more than the other two approaches after imputation, and the suggested outlier detector is absolutely appropriate for the precise detection of outliers in highly variable data.

关键词： Statistical Process Control functional data analysis Fuzzy C Means Outliers Air Quality

Linear mixed-effects model for longitudinal complex data with diversified characteristics

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Journal of Management Science and Engineering 2020年第2期5卷 105-124页

作者： Zhichao Wang Huiwen Wang Shanshan Wang Shan Lu Gilbert Saporta School of Economics and Management Beihang UniversityBeijingChina Beijing Advanced Innovation Center for Big data and Brain Computing BeijingChina School of Statistics and Mathematics Central University of Finance and EconomicsBeijingChina Applied Statistics Conservatoire National des Arts et MetiersParis75141France

The increasing richness of data encourages a comprehensive understanding of economic and financial activities,where variables of interest may include not only scalar(point-like)indicators,but also functional(curve-like)and compositional(pie-like)*** many research topics,the variables are also chronologically collected across individuals,which falls into the paradigm of longitudinal *** complicated nature of data,however,increases the difficulty of modeling these variables under the classic longitudinal *** this study,we investigate the linear mixed-effects model(LMM)for such complex *** types of variables arefirst consistently represented using the corresponding basis expansions so that the classic LMM can then be conducted on them,which gener-alizes the theoretical framework of LMM to complex data analysis.A number of simulation studies indicate the feasibility and effectiveness of the proposed *** further illustrate its practical utility in a real data study on Chinese stock market and show that the proposed method can enhance the performance and interpretability of the regression for complex data with diversified characteristics.

关键词： Longitudinal complex data Linear mixed-effects model Compositional data analysis functional data analysis Chinese stock market Online investors'sentiment