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Mathematics
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Advances in Statistical Modeling
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Advances in Statistical Modeling

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 5345

Special Issue Editor

Dr. Ranjit Kumar Paul

E-Mail Website
Guest Editor
ICAR - Indian Agricultural Statistics Research Institute, New Delhi, New Delhi 110 012, India
Interests: statistical modelling; linear and nonlinear time series analysis; wavelet analysis; long memory time series; climate change

Special Issue Information

Dear Colleagues,

The deep learning (DL), machine learning (ML) and various stochastic models have been developed to solve many practical problems in the real world. In recent times, ensembles of models have been developed to deal with the complexity of emerging datasets, which cannot be captured by traditional statistical models. A large amount of data is being generated every year in many fields including ecology and environment. These data offer many challenges to researchers, such as robustness, big data, outliers, low prediction accuracy, data dimensionality, etc. There is a need to develop advanced statistical models/algorithms to capture their complex behavior. 

Keeping these in mind, I am pleased to announce the Special Issue "Advances in Statistical Modeling" and would like to invite authors to submit their novel findings. The recommended topics include, but are not limited to, the following:

  • Machine learning and deep learning techniques;
  • Market intelligence;
  • Big data modeling;
  • Application of remote sensing;
  • Time series analysis;
  • Environmental pollution;
  • Meteorological prediction;
  • Advances in optimization technique.

Dr. Ranjit Kumar Paul
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • environment
  • machine learning
  • statistical modeling
  • time series analysis

Published Papers (4 papers)

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Research

18 pages, 583 KiB  
Article
Adaptive Regression Analysis of Heterogeneous Data Streams via Models with Dynamic Effects
by Jianfeng Wei, Jian Yang, Xuewen Cheng, Jie Ding and Shengquan Li
Mathematics 2023, 11(24), 4899; https://doi.org/10.3390/math11244899 - 7 Dec 2023
Viewed by 852
Abstract
Streaming data sequences arise from various areas in the era of big data, and it is challenging to explore efficient online models that adapt to them. To address the potential heterogeneity, we introduce a new online estimation procedure to analyze the constantly incoming [...] Read more.
Streaming data sequences arise from various areas in the era of big data, and it is challenging to explore efficient online models that adapt to them. To address the potential heterogeneity, we introduce a new online estimation procedure to analyze the constantly incoming streaming datasets. The underlying model structures are assumed to be the generalized linear models with dynamic regression coefficients. Our key idea lies in introducing a vector of unknown parameters to measure the differences between batch-specific regression coefficients from adjacent data blocks. This is followed by the usage of the adaptive lasso penalization methodology to accurately select nonzero components, which indicates the existence of dynamic coefficients. We provide detailed derivations to demonstrate how our proposed method not only fits within the online updating framework in which the old estimator is recursively replaced with a new one based solely on the current individual-level samples and historical summary statistics but also adaptively avoids undesirable estimation biases coming from the potential changes in model parameters of interest. Computational issues are also discussed in detail to facilitate implementation. Its practical performance is demonstrated through both extensive simulations and a real case study. In summary, we contribute to a novel online method that efficiently adapts to streaming data environment, addresses potential heterogeneity, and mitigates estimation biases from changes in coefficients. Full article
(This article belongs to the Special Issue Advances in Statistical Modeling)
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23 pages, 1279 KiB  
Article
Prediction and Analysis of Container Terminal Logistics Arrival Time Based on Simulation Interactive Modeling: A Case Study of Ningbo Port
by Ruoqi Wang, Jiawei Li and Ruibin Bai
Mathematics 2023, 11(15), 3271; https://doi.org/10.3390/math11153271 - 25 Jul 2023
Cited by 3 | Viewed by 1631
Abstract
This study is a driving analysis of the transfer data of container terminals based on simulation interactive modeling technology. In the context of a container yard, a model was established to analyze and predict the arrival time and influencing factors of container transportation [...] Read more.
This study is a driving analysis of the transfer data of container terminals based on simulation interactive modeling technology. In the context of a container yard, a model was established to analyze and predict the arrival time and influencing factors of container transportation through the data from the control center of the yard. The economic benefit index in the index system was determined through expert consultation, the automatic terminal can be obtained by acquiring the actual operating parameters of the terminal, and the terminal to be built can be acquired mainly through simulation modeling. Therefore, when determining the design scheme before constructing the automated container terminal, a terminal simulation model needs to be established that meets the requirements of loading and unloading operations and terminal production operations. In addition, an automated container terminal simulation model needs to be implemented to verify the feasibility of the evaluation model. The results reveal that the accuracy of the current prediction model is still limited—the highest accuracy is only 72%, whether there are continuous or discrete variables, traffic or weather variables. Moreover, the study denotes that the relationship between weather and specific time factors and the arrival time of containers is weak, even negligible. This study provides guidance and decision-making support for the construction of automated terminals. Full article
(This article belongs to the Special Issue Advances in Statistical Modeling)
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18 pages, 1507 KiB  
Article
Wavelets in Combination with Stochastic and Machine Learning Models to Predict Agricultural Prices
by Sandip Garai, Ranjit Kumar Paul, Debopam Rakshit, Md Yeasin, Walid Emam, Yusra Tashkandy and Christophe Chesneau
Mathematics 2023, 11(13), 2896; https://doi.org/10.3390/math11132896 - 28 Jun 2023
Cited by 8 | Viewed by 1137
Abstract
Wavelet decomposition in signal processing has been widely used in the literature. The popularity of machine learning (ML) algorithms is increasing day by day in agriculture, from irrigation scheduling and yield prediction to price prediction. It is quite interesting to study wavelet-based stochastic [...] Read more.
Wavelet decomposition in signal processing has been widely used in the literature. The popularity of machine learning (ML) algorithms is increasing day by day in agriculture, from irrigation scheduling and yield prediction to price prediction. It is quite interesting to study wavelet-based stochastic and ML models to appropriately choose the most suitable wavelet filters to predict agricultural commodity prices. In the present study, some popular wavelet filters, such as Haar, Daubechies (D4), Coiflet (C6), best localized (BL14), and least asymmetric (LA8), were considered. Daily wholesale price data of onions from three major Indian markets, namely Bengaluru, Delhi, and Lasalgaon, were used to illustrate the potential of different wavelet filters. The performance of wavelet-based models was compared with that of benchmark models. It was observed that, in general, the wavelet-based combination models outperformed other models. Moreover, wavelet decomposition with the Haar filter followed by application of the random forest (RF) model gave better prediction accuracy than other combinations as well as other individual models. Full article
(This article belongs to the Special Issue Advances in Statistical Modeling)
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20 pages, 1714 KiB  
Article
Dynamic Modeling for Metro Passenger Flows on Congested Transfer Routes
by Weiyan Mu, Xin Wang, Chunya Li and Shifeng Xiong
Mathematics 2023, 11(6), 1427; https://doi.org/10.3390/math11061427 - 15 Mar 2023
Cited by 1 | Viewed by 1029
Abstract
With the rapid development of urbanization, the metro becomes more and more important for people’s travel in big cities. To quantitatively describe metro passenger flows on congested transfer routes, this paper introduces a dynamic model based on automated data from the automatic fare [...] Read more.
With the rapid development of urbanization, the metro becomes more and more important for people’s travel in big cities. To quantitatively describe metro passenger flows on congested transfer routes, this paper introduces a dynamic model based on automated data from the automatic fare collection (AFC) and automatic vehicle location (AVL) systems. An expectation maximization (EM) algorithm is proposed to compute the maximum likelihood estimates of unknown parameters in our model. Our model can yield a systematic analysis of one-transfer passenger flows on both population and individual aspects. Important characteristics, including transfer time, boarding probabilities, walking time, passenger-to-train assignment probabilities, and total travel time, can be inferred using only the AFC and AVL data. We provide a case study on the Beijing metro. Detailed analysis results based on our model are given. We also present a cross-validation method to validate our model with real data. Full article
(This article belongs to the Special Issue Advances in Statistical Modeling)
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