Stats

25 pages, 1520 KiB

Open AccessCommunication

An Integrated Hybrid-Stochastic Framework for Agro-Meteorological Prediction Under Environmental Uncertainty

by Mohsen Pourmohammad Shahvar, Davide Valenti, Alfonso Collura, Salvatore Micciche, Vittorio Farina and Giovanni Marsella

Stats 2025, 8(2), 30; https://doi.org/10.3390/stats8020030 - 25 Apr 2025

Abstract

This study presents a comprehensive framework for agro-meteorological prediction, combining stochastic modeling, machine learning techniques, and environmental feature engineering to address challenges in yield prediction and wind behavior modeling. Focused on mango cultivation in the Mediterranean region, the workflow integrates diverse datasets, including [...] Read more.

This study presents a comprehensive framework for agro-meteorological prediction, combining stochastic modeling, machine learning techniques, and environmental feature engineering to address challenges in yield prediction and wind behavior modeling. Focused on mango cultivation in the Mediterranean region, the workflow integrates diverse datasets, including satellite-derived variables such as NDVI, soil moisture, and land surface temperature (LST), along with meteorological features like wind speed and direction. Stochastic modeling was employed to capture environmental variability, while a proxy yield was defined using key environmental factors in the absence of direct field yield measurements. Machine learning models, including random forest and multi-layer perceptron (MLP), were hybridized to improve the prediction accuracy for both proxy yield and wind components (U and V that represent the east–west and north–south wind movement). The hybrid model achieved mean squared error (MSE) values of 0.333 for U and 0.181 for V, with corresponding R² values of 0.8939 and 0.9339, respectively, outperforming the individual models and demonstrating reliable generalization in the 2022 test set. Additionally, although NDVI is traditionally important in crop monitoring, its low temporal variability across the observation period resulted in minimal contribution to the final prediction, as confirmed by feature importance analysis. Furthermore, the analysis revealed the significant influence of environmental factors such as LST, precipitable water, and soil moisture on yield dynamics, while wind visualization over digital elevation models (DEMs) highlighted the impact of terrain features on the wind patterns. The results demonstrate the effectiveness of combining stochastic and machine learning approaches in agricultural modeling, offering valuable insights for crop management and climate adaptation strategies. Full article

(This article belongs to the Section Applied Statistics and Machine Learning Methods)

29 pages, 572 KiB

Open AccessArticle

Bias-Corrected Fixed Item Parameter Calibration, with an Application to PISA Data

by Alexander Robitzsch

Stats 2025, 8(2), 29; https://doi.org/10.3390/stats8020029 - 24 Apr 2025

Abstract

Fixed item parameter calibration (FIPC) is commonly used to compare groups or countries using an item response theory model with a common set of fixed item parameters. However, FIPC has been shown to produce biased estimates of group means and standard deviations in [...] Read more.

Fixed item parameter calibration (FIPC) is commonly used to compare groups or countries using an item response theory model with a common set of fixed item parameters. However, FIPC has been shown to produce biased estimates of group means and standard deviations in the presence of random differential item functioning (DIF). To address this, a bias-corrected variant of FIPC, called BCFIPC, is introduced in this article. BCFIPC eliminated the bias of FIPC with only minor efficiency losses in certain simulation conditions, but substantial precision gains in many others, particularly for estimating group standard deviations. Finally, a comparison of both methods using the PISA 2009 dataset revealed relatively large differences in country means and standard deviations. Full article

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34 pages, 2528 KiB

Open AccessFeature PaperArticle

Inferences About Two-Parameter Multicollinear Gaussian Linear Regression Models: An Empirical Type I Error and Power Comparison

by Md Ariful Hoque, Zoran Bursac and B. M. Golam Kibria

Stats 2025, 8(2), 28; https://doi.org/10.3390/stats8020028 - 23 Apr 2025

Abstract

In linear regression analysis, the independence assumption is crucial and the ordinary least square (OLS) estimator generally regarded as the Best Linear Unbiased Estimator (BLUE) is applied. However, multicollinearity can complicate the estimation of the effect of individual variables, leading to potential inaccurate [...] Read more.

In linear regression analysis, the independence assumption is crucial and the ordinary least square (OLS) estimator generally regarded as the Best Linear Unbiased Estimator (BLUE) is applied. However, multicollinearity can complicate the estimation of the effect of individual variables, leading to potential inaccurate statistical inferences. Because of this issue, different types of two-parameter estimators have been explored. This paper compares t-tests for assessing the significance of regression coefficients, including several two-parameter estimators. We conduct a Monte Carlo study to evaluate these methods by examining their empirical type I error and power characteristics, based on established protocols. The simulation results indicate that some two-parameter estimators achieve better power gains while preserving the nominal size at 5%. Real-life data are analyzed to illustrate the findings of this paper. Full article

(This article belongs to the Section Statistical Methods)

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25 pages, 2840 KiB

Open AccessArticle

Detailed Command vs. Mission Command: A Cancer-Stage Model of Institutional Decision-Making

by Rodrick Wallace

Stats 2025, 8(2), 27; https://doi.org/10.3390/stats8020027 - 19 Apr 2025

Abstract

Those accustomed to acting within ‘normal’ bureaucracies will have experienced the degradation, distortion, and stunting imposed by inordinate levels of hierarchical ‘decision structure’, particularly under the critical time constraints so fondly exploited by John Boyd and his followers. Here, via an approach based [...] Read more.

Those accustomed to acting within ‘normal’ bureaucracies will have experienced the degradation, distortion, and stunting imposed by inordinate levels of hierarchical ‘decision structure’, particularly under the critical time constraints so fondly exploited by John Boyd and his followers. Here, via an approach based on the asymptotic limit theorems of information and control theories, we explore this dynamic in detail, abducting ideas from the theory of carcinogenesis. The resulting probability models can, with some effort, be converted into new statistical tools for analysis of real time, real world data involving cognitive phenomena and their dysfunctions across a considerable range of scales and levels of organization. Full article

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38 pages, 844 KiB

Open AccessArticle

The New Marshall–Olkin–Type II Exponentiated Half-Logistic–Odd Burr X-G Family of Distributions with Properties and Applications

by Broderick Oluyede, Thatayaone Moakofi and Gomolemo Lekono

Stats 2025, 8(2), 26; https://doi.org/10.3390/stats8020026 - 4 Apr 2025

Abstract

We develop a novel family of distributions named the Marshall–Olkin type II exponentiated half-logistic–odd Burr X-G distribution. Several mathematical properties including linear representation of the density function, Rényi entropy, probability-weighted moments, and distribution of order statistics are obtained. Different estimation methods are employed [...] Read more.

We develop a novel family of distributions named the Marshall–Olkin type II exponentiated half-logistic–odd Burr X-G distribution. Several mathematical properties including linear representation of the density function, Rényi entropy, probability-weighted moments, and distribution of order statistics are obtained. Different estimation methods are employed to estimate the unknown parameters of the new distribution. A simulation study is conducted to assess the effectiveness of the estimation methods. A special model of the new distribution is used to show its usefulness in various disciplines. Full article

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19 pages, 296 KiB

Open AccessArticle

Affine Calculus for Constrained Minima of the Kullback–Leibler Divergence

by Giovanni Pistone

Stats 2025, 8(2), 25; https://doi.org/10.3390/stats8020025 - 21 Mar 2025

Abstract

The non-parametric version of Amari’s dually affine Information Geometry provides a practical calculus to perform computations of interest in statistical machine learning. The method uses the notion of a statistical bundle, a mathematical structure that includes both probability densities and random variables to [...] Read more.

The non-parametric version of Amari’s dually affine Information Geometry provides a practical calculus to perform computations of interest in statistical machine learning. The method uses the notion of a statistical bundle, a mathematical structure that includes both probability densities and random variables to capture the spirit of Fisherian statistics. We focus on computations involving a constrained minimization of the Kullback–Leibler divergence. We show how to obtain neat and principled versions of known computations in applications such as mean-field approximation, adversarial generative models, and variational Bayes. Full article

27 pages, 677 KiB

Open AccessArticle

Optimal ANOVA-Based Emulators of Models With(out) Derivatives

by Matieyendou Lamboni

Stats 2025, 8(1), 24; https://doi.org/10.3390/stats8010024 - 17 Mar 2025

Abstract

This paper proposes new ANOVA-based approximations of functions and emulators of high-dimensional models using either available derivatives or local stochastic evaluations of such models. Our approach makes use of sensitivity indices to design adequate structures of emulators. For high-dimensional models with available derivatives, [...] Read more.

This paper proposes new ANOVA-based approximations of functions and emulators of high-dimensional models using either available derivatives or local stochastic evaluations of such models. Our approach makes use of sensitivity indices to design adequate structures of emulators. For high-dimensional models with available derivatives, our derivative-based emulators reach dimension-free mean squared errors (MSEs) and a parametric rate of convergence (i.e.,

O (N^{- 1})

). This approach is extended to cope with every model (without available derivatives) by deriving global emulators that account for the local properties of models or simulators. Such generic emulators enjoy dimension-free biases, parametric rates of convergence, and MSEs that depend on the dimensionality. Dimension-free MSEs are obtained for high-dimensional models with particular distributions from the input. Our emulators are also competitive in dealing with different distributions of the input variables and selecting inputs and interactions. Simulations show the efficiency of our approach. Full article

(This article belongs to the Section Statistical Methods)

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7 pages, 207 KiB

Open AccessArticle

Statistical Gravity and Entropy of Spacetime

by Riccardo Fantoni

Stats 2025, 8(1), 23; https://doi.org/10.3390/stats8010023 - 13 Mar 2025

Abstract

We discuss the foundations of the statistical gravity theory we proposed in a recent publication [Riccardo Fantoni, Quantum Reports, 6, 706 (2024)]. Full article

25 pages, 400 KiB

Open AccessArticle

A Flexible Bivariate Integer-Valued Autoregressive of Order (1) Model for Over- and Under-Dispersed Time Series Applications

by Naushad Mamode Khan and Yuvraj Sunecher

Stats 2025, 8(1), 22; https://doi.org/10.3390/stats8010022 - 12 Mar 2025

Abstract

In real-life inter-related time series, the counting responses of different entities are commonly influenced by some time-dependent covariates, while the individual counting series may exhibit different levels of mutual over- or under-dispersion or mixed levels of over- and under-dispersion. In the current literature, [...] Read more.

In real-life inter-related time series, the counting responses of different entities are commonly influenced by some time-dependent covariates, while the individual counting series may exhibit different levels of mutual over- or under-dispersion or mixed levels of over- and under-dispersion. In the current literature, there is still no flexible bivariate time series process that can model series of data of such types. This paper introduces a bivariate integer-valued autoregressive of order 1 (BINAR(1)) model with COM-Poisson innovations under time-dependent moments that can accommodate different levels of over- and under-dispersion. Another particularity of the proposed model is that the cross-correlation between the series is induced locally by relating the current observation of one series with the previous-lagged observation of the other series. The estimation of the model parameters is conducted via a Generalized Quasi-Likelihood (GQL) approach. The proposed model is applied to different real-life series problems in Mauritius, including transport, finance, and socio-economic sectors. Full article

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9 pages, 965 KiB

Open AccessCommunication

The Gamma-G Family: Brief Survey and COVID-19 Application

by Gauss M. Cordeiro and Alexsandro A. Ferreira

Stats 2025, 8(1), 21; https://doi.org/10.3390/stats8010021 - 5 Mar 2025

Abstract

In recent years, the gamma-G family has gained significant popularity for generating new distributions by incorporating an additional shape parameter into the baseline model. This article compiles and documents the existing gamma-G distributions and demonstrates their application to real COVID-19 data. Full article

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21 pages, 326 KiB

Open AccessArticle

Quantum-Inspired Latent Variable Modeling in Multivariate Analysis

by Theodoros Kyriazos and Mary Poga

Stats 2025, 8(1), 20; https://doi.org/10.3390/stats8010020 - 28 Feb 2025

Cited by 1

Abstract

Latent variables play a crucial role in psychometric research, yet traditional models often struggle to address context-dependent effects, ambivalent states, and non-commutative measurement processes. This study proposes a quantum-inspired framework for latent variable modeling that employs Hilbert space representations, allowing questionnaire items to [...] Read more.

Latent variables play a crucial role in psychometric research, yet traditional models often struggle to address context-dependent effects, ambivalent states, and non-commutative measurement processes. This study proposes a quantum-inspired framework for latent variable modeling that employs Hilbert space representations, allowing questionnaire items to be treated as pure or mixed quantum states. By integrating concepts such as superposition, interference, and non-commutative probabilities, the framework captures cognitive and behavioral phenomena that extend beyond the capabilities of classical methods. To illustrate its potential, we introduce quantum-specific metrics—fidelity, overlap, and von Neumann entropy—as complements to correlation-based measures. We also outline a machine-learning pipeline using complex and real-valued neural networks to handle amplitude and phase information. Results highlight the capacity of quantum-inspired models to reveal order effects, ambivalent responses, and multimodal distributions that remain elusive in standard psychometric approaches. This framework broadens the multivariate analysis theoretical and methodological toolkit, offering a dynamic and context-sensitive perspective on latent constructs while inviting further empirical validation in diverse research settings. Full article

(This article belongs to the Section Multivariate Analysis)

37 pages, 14387 KiB

Open AccessArticle

Deviations from Normality in Autocorrelation Functions and Their Implications for MA(q) Modeling

by Manuela Royer-Carenzi and Hossein Hassani

Stats 2025, 8(1), 19; https://doi.org/10.3390/stats8010019 - 20 Feb 2025

Abstract

The identification of the orders of time series models plays a crucial role in their accurate specification and forecasting. The Autocorrelation Function (ACF) is commonly used to identify the order q of Moving Average (MA(q)) models, as it theoretically vanishes for [...] Read more.

The identification of the orders of time series models plays a crucial role in their accurate specification and forecasting. The Autocorrelation Function (ACF) is commonly used to identify the order q of Moving Average (MA(q)) models, as it theoretically vanishes for lags beyond q. This property is widely used in model selection, assuming the sample ACF follows an asymptotic normal distribution for robustness. However, our examination of the sum of the sample ACF reveals inconsistencies with these theoretical properties, highlighting a deviation from normality in the sample ACF for MA(q) processes. As a natural extension of the ACF, the Extended Autocorrelation Function (EACF) provides additional insights by facilitating the simultaneous identification of both autoregressive and moving average components. Using simulations, we evaluate the performance of q-order identification in MA(q) models, which is based on the properties of ACF. Similarly, for ARMA(

p, q

) models, we assess the (

p, q

)-order identification relying on EACF. Our findings indicate that both methods are effective for sufficiently long time series but may incorrectly favor an ARMA(

p, q - 1

) model when the

a_{q}

coefficient approaches zero. Additionally, if the cumulative sums of ACF (SACF) behave consistently and the Ljung–Box test validates the proposed model, it can serve as a strong candidate. The proposed models should then be compared based on their predictive performance. We illustrate our methodology with an application to wind speed data and sea surface temperature anomalies, providing practical insights into the relevance of our findings. Full article

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9 pages, 965 KiB

Open AccessCommunication

STATom@ic: R Package for Automated Statistical Analysis of Omic Datasets

by Rui S. Treves, Tyler C. Gripshover and Josiah E. Hardesty

Stats 2025, 8(1), 18; https://doi.org/10.3390/stats8010018 - 11 Feb 2025

Abstract

Background: The evolution of “omic” technologies, which measure all biological molecules of a specific type (e.g., genomics), has enabled rapid and cost-effective data acquisition, depending on the technique and sample size. This, however, generates new hurdles that need to be addressed and should [...] Read more.

Background: The evolution of “omic” technologies, which measure all biological molecules of a specific type (e.g., genomics), has enabled rapid and cost-effective data acquisition, depending on the technique and sample size. This, however, generates new hurdles that need to be addressed and should be improved upon. This includes selecting the appropriate statistical test based on study design in a high-throughput manner. Methods: An automated statistical analysis pipeline for omic datasets that we coined STATom@ic (pronounced stat-o-matic) was developed in R programming language. Results: We developed an R package that enables statisticians, bioinformaticians, and scientists to perform assumption tests (e.g., normality and variance homogeneity) before selecting appropriate statistical tests. This analysis package can handle two-group and multiple-group comparisons. In addition, this R package can be used for many data formats including normalized counts (RNASeq) and spectral abundance (proteomics and metabolomics). STATom@ic has high precision but lower recall compared to DeSeq2. Conclusions: The STATom@ic R Package is a user-friendly stand-alone or add-on to current bioinformatic workflows that automatically performs appropriate statistical analysis based on the characteristics of the data. Full article

(This article belongs to the Section Biostatistics)

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13 pages, 489 KiB

Open AccessArticle

Limits of Agreement Based on Transformed Measurements

by Erik Thorlund Parner

Stats 2025, 8(1), 17; https://doi.org/10.3390/stats8010017 - 10 Feb 2025

Abstract

Method comparison studies are typically analyzed using limits of agreement (LoAs). The standard Bland–Altman approach estimates LoAs under the assumption that the differences between methods follow a normal distribution. However, many types of measurements, such as volume, concentration, and percentage values, often deviate [...] Read more.

Method comparison studies are typically analyzed using limits of agreement (LoAs). The standard Bland–Altman approach estimates LoAs under the assumption that the differences between methods follow a normal distribution. However, many types of measurements, such as volume, concentration, and percentage values, often deviate from normality. This study explores LoAs for the difference between two clinical measurements and prediction intervals for one measurement given the other, using a transformation of the data. After back-transforming, the resulting LoA for the original measurements depends on the subject level, represented by the average of the measurements. A simulation study evaluates the statistical properties of these LoAs and their confidence limits, demonstrating strong performance for small-to-medium sample sizes. LoA derived from transformed measurements are also compared with those obtained using a regression-based method proposed by Bland and Altman. Two applications demonstrate the approach using logarithmic and cube root transformations. This transformation-based method offers a straightforward way to obtain LoAs that depends on the subject level. Full article

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33 pages, 878 KiB

Open AccessArticle

An Unbiased Convex Estimator Depending on Prior Information for the Classical Linear Regression Model

by Mustafa I. Alheety, HM Nayem and B. M. Golam Kibria

Stats 2025, 8(1), 16; https://doi.org/10.3390/stats8010016 - 9 Feb 2025

Abstract

We propose an unbiased restricted estimator that leverages prior information to enhance estimation efficiency for the linear regression model. The statistical properties of the proposed estimator are rigorously examined, highlighting its superiority over several existing methods. A simulation study is conducted to evaluate [...] Read more.

We propose an unbiased restricted estimator that leverages prior information to enhance estimation efficiency for the linear regression model. The statistical properties of the proposed estimator are rigorously examined, highlighting its superiority over several existing methods. A simulation study is conducted to evaluate the performance of the estimators, and real-world data on total national research and development expenditures by country are analyzed to illustrate the findings. Both the simulation results and real-data analysis demonstrate that the proposed estimator consistently outperforms the alternatives considered in this study. Full article

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17 pages, 564 KiB

Open AccessCommunication

Note on Pre-Taxation Data Reported by UK FTSE-Listed Companies: Search for Compatibility with Benford’s Laws

by Marcel Ausloos, Probowo Erawan Sastroredjo and Polina Khrennikova

Stats 2025, 8(1), 15; https://doi.org/10.3390/stats8010015 - 7 Feb 2025

Abstract

Pre-taxation analysis plays a crucial role in ensuring the fairness of public revenue collection. It can also serve as a tool to reduce the risk of tax avoidance, one of the UK government’s concerns. Our report utilises pre-tax income (

P I

) [...] Read more.

Pre-taxation analysis plays a crucial role in ensuring the fairness of public revenue collection. It can also serve as a tool to reduce the risk of tax avoidance, one of the UK government’s concerns. Our report utilises pre-tax income (

P I

) and total assets (

T A

) data from 567 companies listed on the FTSE All-Share index, gathered from the Refinitiv EIKON database, covering 14 years, i.e., the period from 2009 to 2022. We also derive the

P I / T A

ratio, and distinguish between positive and negative

P I

cases. We test the conformity of such data to Benford’s Laws, specifically studying the first significant digit (

F d

), the second significant digit (

S d

), and the first and second significant digits (

F S d

). We use and justify two pertinent tests, the

χ^{2}

and the Mean Absolute Deviation (MAD). We find that both tests do not lead to conclusions in complete agreement with each other—in particular, the MAD test entirely rejects the Benford’s Laws conformity of the reported financial data. From the mere accounting point of view, we conclude that the findings not only cast some doubt on the reported financial data, but also suggest that many more investigations should be considered on closely related matters. On the other hand, the study of a ratio, like

P I / T A

, of variables that are (or are not) Benford’s Laws-compliant adds to the literature concerning whether such indirect variables should (or should not) be Benford’s Laws-compliant. Full article

(This article belongs to the Section Financial Statistics)

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23 pages, 1253 KiB

Open AccessArticle

EEG Signal Analysis for Numerical Digit Classification: Methodologies and Challenges

by Augoustos Tsamourgelis and Adam Adamopoulos

Stats 2025, 8(1), 14; https://doi.org/10.3390/stats8010014 - 5 Feb 2025

Abstract

Electroencephalography (EEG) has existed since the early 20th century. It has proven to be a vital tool for electrophysiological studies of conditions like epilepsy. Recently, it has been revitalized as the field of machine learning has been developing, widening its usefulness among a [...] Read more.

Electroencephalography (EEG) has existed since the early 20th century. It has proven to be a vital tool for electrophysiological studies of conditions like epilepsy. Recently, it has been revitalized as the field of machine learning has been developing, widening its usefulness among a plethora of neurological conditions and in brain–computer interface (BCI) applications. This study delves into the intricate process of classifying EEG signals elicited by the visual stimuli of subjects viewing the digits 0 and 1 and a blank screen. We focus on developing a comprehensive workflow for EEG preprocessing, as well as feature extraction and signal classification. We achieve strong differentiation capabilities between digit and non-digit values in all classification algorithms. However, our study also highlights the profound neurological challenges encountered in distinguishing between the digit values, as our model, inspired by the related bibliography, was unable to differentiate between digit values 0 and 1. These findings underscore the complexity of numerical processing in the brain, revealing critical insights into the limitations and potential of EEG-based digit classification and the need for clarity in the bioinformatics community. Full article

(This article belongs to the Section Time Series Analysis)

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26 pages, 12220 KiB

Open AccessArticle

Measurement and Decomposition Analysis of Occupational Income Inequality in China

by Jing Yuan, Teng Ma, Yinghui Wang and Zongwu Cai

Stats 2025, 8(1), 13; https://doi.org/10.3390/stats8010013 - 2 Feb 2025

Abstract

Using the China CFPS database, this paper measures the degree of intra-occupational inequality in China with the Pareto coefficient and uses the generalized entropy index to decompose the top income gap by region as well as by industry. The empirical results show that, [...] Read more.

Using the China CFPS database, this paper measures the degree of intra-occupational inequality in China with the Pareto coefficient and uses the generalized entropy index to decompose the top income gap by region as well as by industry. The empirical results show that, firstly, the degree of income inequality between occupations in China has increased significantly in recent years. The provinces with a higher degree of income inequality between occupations are mostly located in the more economically developed regions in the central and eastern parts of the country, while the degree of inequality between occupations in the western part is lower. Secondly, the highest-income occupations are mainly in the manufacturing industry, with relatively high levels in the construction industry, the education sector, the wholesale and retail trade, and public administration and social organizations, while the levels in other occupations are relatively low. Lastly, the top income gap primarily originates from within industries. However, the contribution rate of the top income gap between industries is gradually increasing, while the contribution rate of the top income gap within industries is gradually decreasing. Full article

(This article belongs to the Section Financial Statistics)

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17 pages, 1319 KiB

Open AccessCommunication

Smart Renting: Harnessing Urban Data with Statistical and Machine Learning Methods for Predicting Property Rental Prices from a Tenant’s Perspective

by Francisco Louzada, Kleython José Coriolano Cavalcanti de Lacerda, Paulo Henrique Ferreira and Naomy Duarte Gomes

Stats 2025, 8(1), 12; https://doi.org/10.3390/stats8010012 - 27 Jan 2025

Abstract

The real estate market plays a pivotal role in most nations’ economy, showcasing continuous growth. Particularly noteworthy is the rapid expansion of the digital real estate sector, marked by innovations like 3D visualization and streamlined online contractual processes, a momentum further accelerated by [...] Read more.

The real estate market plays a pivotal role in most nations’ economy, showcasing continuous growth. Particularly noteworthy is the rapid expansion of the digital real estate sector, marked by innovations like 3D visualization and streamlined online contractual processes, a momentum further accelerated by the aftermath of the Coronavirus Disease 2019 (COVID-19) pandemic. Amidst this transformative landscape, artificial intelligence emerges as a vital force, addressing consumer needs by harnessing data analytics for predicting and monitoring rental prices. While studies have demonstrated the efficacy of machine learning (ML) algorithms such as decision trees and neural networks in predicting house prices, there is a lack of research specifically focused on rental property prices, a significant sector in Brazil due to the prohibitive costs associated with property acquisition. This study fills this crucial gap by delving into the intricacies of rental pricing, using data from the city of São Carlos-SP, Brazil. The research aims to analyze, model, and predict rental prices, employing an approach that incorporates diverse ML models. Through this analysis, our work showcases the potential of ML algorithms in accurately predicting rental house prices. Moreover, it envisions the practical application of this research with the development of a user-friendly website. This platform could revolutionize the renting experience, empowering both tenants and real estate agencies with the ability to estimate rental values based on specific property attributes and have access to its statistics. Full article

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