Statistical Model Research Articles

Screening p-hackers: Dissemination noise as bait.

We show that adding noise before publishing data effectively screens [Formula: see text]-hacked findings: spurious explanations produced by fitting many statistical models (data mining). Noise creates "baits" that affect two types of researchers differently. Uninformed [Formula: see text]-hackers, who are fully ignorant of the true mechanism and engage in data mining, often fall for baits. Informed researchers, who start with an ex ante hypothesis, are minimally affected. We show that as the number of observations grows large, dissemination noise asymptotically achieves optimal screening. In a tractable special case where the informed researchers' theory can identify the true causal mechanism with very few data, we characterize the optimal level of dissemination noise and highlight the relevant trade-offs. Dissemination noise is a tool that statistical agencies currently use to protect privacy. We argue this existing practice can be repurposed to screen [Formula: see text]-hackers and thus improve research credibility.

Cracking Chicken-Egg Conundrums: Juxtaposing Contemporaneous and Lagged Reciprocal Effects Models of Academic Self-Concept and Achievement’s Directional Ordering

Multi-wave-cross-lagged-panel models (CLPMs) of directional ordering are a focus of much controversy in educational psychology and more generally. Extending traditional analyses, methodologists have recently argued for including random intercepts and lag2 effects between non-adjacent waves and giving more attention to controlling covariates. However, the related issues of appropriate time intervals between waves (lag1 intervals across waves) and the possibility of contemporaneous (lag0) effects within each wave are largely unresolved. Although philosophers, theologians, and scientists widely debate sequential (lagged) and simultaneous (lag0) theories of causality, CLPM researchers have mostly ignored contemporaneous effects, arguing causes must precede effects. In a substantive-methodological synergy, we integrated these issues and designed new structural equation models to reanalyze one of the strongest CLPM studies of academic self-concept (ASC) and achievement (five annuals of mathematics data; 3527 secondary school students). A taxonomy of models incorporating various combinations of lag0, lag1, and lag2 effects, random intercepts, and covariates consistently supported a priori reciprocal effect model (REM) predictions—medium or large reciprocal effects of ASC and achievement on each other. Consistent with self-concept theory, effects of ASC on achievement evolved over time (lag1, not lag0 effects), whereas effects of achievement on ASC effects were more contemporaneous (lag0, not lag1 effects). We argue that lag0 effects reflect proximal events occurring subsequent to the previous data wave, suggesting the need for shorter intervals but also leaving open the possibility of contemporaneous effects that are truly instantaneous. We discuss limitations and future directions but also note the broad applicability of our statistical models.

Treatment outcomes of digital nasoalveolar moulding in infants with cleft lip and palate: A systematic review with meta-analysis.

The aim of this systematic review was to compare the treatment outcomes of digital nasoalveolar moulding (dNAM) technique with conventional nasoalveolar moulding (cNAM) or non-presurgical intervention protocol in infants with unilateral (UCLP) or bilateral (BCLP) cleft lip and palate. A bibliometric search by MEDLINE (via Ovid), Embase, Cochrane Library, grey literature and manual method was conducted without language restriction until November 2023. Literature screening and data extraction were undertaken in Covidence. The risk of bias was evaluated using the Newcastle-Ottawa Scale and RoB-2. Pooled effect sizes were determined through random-effects statistical model using R-Software, and the certainty of evidence was assessed using the GRADE approach. Among 775 retrieved articles, nine studies were included for qualitative synthesis (6-UCLP, 3-BCLP), with only three eligible UCLP studies for meta-analysis. In the UCLP group, very low certainty of evidence indicated no difference in alveolar cleft width (SMD, 0.13 mm; 95% CI, -0.31 to 0.57; I2, 0%), soft tissue (lip) cleft gap, nasal width, nasal height, and columellar deviation angle changes between dNAM and cNAM. In the BCLP group, qualitative synthesis suggested similar changes in alveolar, lip, and nasal dimensions with dNAM and cNAM. In both cleft groups (UCLP, BCLP), reduced alveolar cleft width was observed in the dNAM group compared to the non-presurgical intervention protocol, along with fewer clinical visits and reduced chairside time for dNAM compared to cNAM. It can be concluded that the treatment outcomes with dNAM were comparable to cNAM in reducing malformation severity and were advantageous in terms of chairside time and clinical visit frequency. However, the overall quality of evidence is very low and standardization is needed for the virtual workflow regarding the alveolar movements and growth factor algorithms. Registration: PROSPERO-database (CRD42020186452).

Estimation of return levels with long return periods for extreme sea levels in a time-varying framework

AbstractAt nuclear power plants, risk analysis concerning environmental extremes is crucial. Based on historical data, estimation of return levels is usually performed. For long return periods, a problem is that the related uncertainties of the return levels often get large. Moreover, models need to take into account possible effects of climate change. In this paper, extreme sea levels close to Swedish nuclear power plants are considered. Non-stationary statistical models and the related results of conditional prediction during a typical time horizon of an infrastructure are studied. The influences of parameters in extreme-value distributions and the lengths of observation records are discussed. The effect of land uplift in parts of the Baltic Sea is seen.

The snow cover is more important than other climatic variables on the prediction of vegetation dynamics in the Pyrenees (1981-2014)

Abstract The dynamics of the mountain vegetation is governed by multiple climatic drivers including temperature, precipitation, radiation and snow cover variability. However, in the Mediterranean environment, little is known about the relative importance of each variable. In this study we assess how different snowpack indices (the maximum annual accumulation, the length of the snow season, and the melt-out date) and key climate variables (precipitation, temperature and shortwave solar radiation) control the interannual variability of the maximum Normalized Difference Vegetation Index (peak NDVI) in the Pyrenees. We use a 33 year long remote sensing dataset (1981-2014) to build a statistical model relating the annual peak NDVI with snow and climate variables. 
In elevated areas characterized by a well developed seasonal snowpack the melt-out date was the most important climatic variable for predicting the annual peak NDVI. However, at lower elevations where snow presence is ephemeral, shortwave solar radiation was the most important variable. This change in the relative importance of climatic variables occurs around 1300 m a.s.l. The results do not show a significant contribution of maximum snow accumulation, suggesting that indicators of snow presence (i.e. melt-out date or snow season duration), which are significantly easier to obtain than snow mass indicators from remote sensing, could be used to model the influence of the snowpack on peak NDVI at regional scale. 

Prospective validation of clinical deterioration predictive models prior to intensive care unit transfer among patients admitted to acute care cardiology wards.

Very few predictive models have been externally validated in a prospective cohort following the implementation of an artificial intelligence analytic system. This type of real-world validation is critically important due to the risk of data drift, or changes in data definitions or clinical practices over time, that could impact model performance in contemporaneous real-world cohorts. In this work, we report the model performance of a predictive analytics tool developed before COVID-19 and demonstrate model performance during the COVID-19 pandemic. The analytic system (CoMETⓇ, Nihon Kohden Digital Health Solutions LLC, Irvine, CA) was implemented in a randomized controlled trial that enrolled 10,422 patient visits in a 1:1 display-on display-off design. The CoMET scores were calculated for all patients but only displayed in the display-on arm. Only the control/display-off group is reported here because the scores could not alter care patterns. Of the 5184 visits in the display-off arm, 311 experienced clinical deterioration and care escalation, resulting in transfer to the intensive care unit (ICU), primarily due to respiratory distress. The model performance of CoMET was assessed based on areas under the receiver operating characteristic curve, which ranged from 0.725 to 0.737. The models were well-calibrated, and there were dynamic increases in the model scores in the hours preceding the clinical deterioration events. A hypothetical alerting strategy based on a rise in score and duration of the rise would have had good performance, with a positive predictive value more than 10-fold the event rate. We conclude that predictive statistical models developed five years before study initiation had good model performance despite the passage of time and the impact of the COVID-19 pandemic. &#xD.

Nonlinear association of fibrinogen levels with functional prognosis in patients with acute ischemic stroke: a prospective cohort study

ObjectiveFibrinogen, essential in primary hemostasis, platelet aggregation, and leukocyte-endothelial interactions, is also associated with a heightened risk of acute ischemic stroke (AIS). However, its influence on AIS patient outcomes is unclear. This study examines the correlation between fibrinogen levels and the risk of unfavorable outcomes three months post-AIS.MethodsThis is a secondary analysis of a prospective cohort study conducted in Korea. The sample consisted of 1851 AIS patients who received treatment at a Korean hospital between January 2010 and December 2016. Statistical models were established to understand the relationship between fibrinogen levels(mg/dL) and unfavorable outcomes(mRs ≥ 3), including logistic regression models, Generalized Additive Models (GAM), and smooth curve fitting (penalized splines). The log-likelihood ratio test has been utilized to evaluate the best fit. To ensure the robustness of the results, sensitivity analyses were conducted by reanalyzing the relationship after excluding participants with TG > 200 mg/dl and BMI > 25 kg/m2. Subgroup analyses were also performed to assess whether influencing factors modify the association between fibrinogen levels and unfavorable outcomes.ResultsAfter adjusting for multiple covariates including age, BMI, sex, LDL-c, TG, HGB, HDL-c, BUN, FPG, ALB, PLT, AF, hypertension, smoking, DM, mRs score at admission, the binary logistic regression model demonstrated revealed a significant positive association between fibrinogen levels and the risk of unfavorable outcomes in AIS patients (OR = 1.215, 95% CI: 1.032–1.429, p = 0.019). Sensitivity analyses supported these findings, with similar ORs observed in subsets of patients with TG < 200 mg/dL (OR = 1.221, 95% CI: 1.036–1.440) and BMI < 25 kg/m2 (OR = 1.259, 95% CI: 1.051–1.509). Additionally, the relationship between fibrinogen levels and outcomes was nonlinear, with a critical threshold of 2.74 g/L. Below the inflection point, the OR for unfavorable outcomes was 0.666 ((95% CI: 0.360, 1.233, p = 0.196), whereas above it, the OR increased to 1.374 (95% CI: 1.138, 1.659).ConclusionsThis study has provided evidence of a positive and nonlinear correlation between fibrinogen levels and 3-month poor functional outcomes in patients with AIS. When fibrinogen levels exceeded 2.74 g/L, a significant and positive association was observed with the risk of poor outcomes. This study provides a further reference for optimizing rehabilitation exercises and facilitating clinical counseling in patients with acute ischemic stroke.

Prediction of response to promising first-line chemotherapy in ovarian cancer patients with residual peritoneal tumors: practical biomarkers and robust multiplex models.

Platinum/taxane (TC) chemotherapy with debulking surgery stays the mainstay of the treatment in ovarian cancer patients with peritoneal metastasis, and recently its novel modality, intraperitoneal carboplatin with dose-dense paclitaxel (ddTCip), was shown to have greater therapeutic impact. Nevertheless, the response varies among patients and consequent recurrence, or relapse often occurs. Discovery of therapeutic response predictor to ddTCip and/or TC therapy is eagerly awaited to improve the treatment outcome. Using datasets in 76 participants in our ddTCip study and published databases on patients received TC therapy, we first validated a total of 75 previously suggested markers, sought out more active biomarkers through the association analyses of genome-wide transcriptome and genotyping data with progression-free survival (PFS) and adverse events, and then developed multiplex statistical prediction models for PFS and toxicity by mainly using multiple regression analysis and the classification and regression tree (CART) algorithm. The association analyses revealed that SPINK1 could be a possible biomarker of ddTCip efficacy, while ABCB1 rs1045642 and ERCC1 rs11615 would be a predictor of hematologic toxicity and peripheral neuropathy, respectively. Multiple regression analyses and CART algorithm finally provided a potent efficacy prediction model using 5 gene expression data and robust multiplex toxicity prediction models-CART models using a total of 4 genotype combinations and multiple regression models using 15 polymorphisms on 12 genes. Biomarkers and multiplex models composed here could work well in the response prediction of ddTCip and/or TC therapy, which might contribute to realize optimal selection of the key therapy.

Hydrogeochemical evolution and processes based on multivariable statistical and inverse simulation modeling: A coal mine in the Northern Coalfield, China

ABSTRACT Considering groundwater from the aquifers overlying the bedrock is an important water source for drinking purposes. As such, the investigation of its property is essential. Based on the spatial structure of aquifers in the study area, the aquifers in the Cenozoic strata are divided into three groups. The multivariate statistical approaches are employed to identify the hydrogeochemical processes and hydro-chemical types, and hydrogeochemical inverse modeling is applied to further validate and elucidate the hydrogeochemical process and water–rock interaction. The results are as follows: (1) The hydro-chemical type of the upper aquifer is dominated by the K + Na-HCO3 type, while others have similar water quality types, which are dominated by the K + Na-Cl type and the K + Na-SO4 type. (2) The saturation index of anhydrite, gypsum, halite, and CO2(g) is below zero in three aquifers, indicating that they are unsaturated. While aragonite, calcite, and dolomite in the middle aquifer remain in the unsaturated–saturated state. (3) The cation exchange process accelerating the reduction of Ca2+ concentration and the increase of SO42− concentration occurs in three aquifers, and the dissolution of calcite and dolomite minerals occurs in most cases. This study supports the fundamental evidence for the hydrogeochemical processes and water resource utilization and has a certain practical significance.

Kinetics of the one-dimensional voter model with long-range interactions

Abstract The voter model is an extremely simple yet nontrivial prototypical model of ordering dynamics, which has been studied in great detail. Recently, a great deal of activity has focused on long-range statistical physics models, where interactions take place among faraway sites, with a probability slowly decaying with distance. In this paper, we study analytically the one-dimensional long-range voter model, where an agent takes the opinion of another at distance r with probability ∝ r−α. The model displays rich and diverse features as α is changed. For α &gt; 3 the behavior is similar to the one of the nearest-neighbor version, with the formation of ordered domains whose typical size grows as R(t) ∝ t1/2 until consensus (a fully ordered configuration) is reached. The correlation function C(r,t) between two agents at distance r obeys dynamical scaling with sizeable corrections at large distances r &gt; r∗(t), slowly fading away in time. For 2 &lt; α ≤ 3 violations of scaling appear, due to the simultaneous presence of two lengh-scales, the size of domains growing as t(α−2)/(α−1), and the distance L(t) ∝ t1/(α−1) over which correlations extend. For α ≤ 2 the system reaches a partially ordered stationary state, characterised by an algebraic correlator, whose lifetime diverges in the thermodynamic limit of infinitely many agents, so that consensus is not reached. For a finite system escape towards the fully ordered configuration is finally promoted by development of large distance correlations. In a system of N sites, global consensus is achieved after a time T ∝ N2 for α&gt;3,T ∝Nα−1 for2&lt;α≤3,andT ∝N forα≤2.

A bilingual speech neuroprosthesis driven by cortical articulatory representations shared between languages.

Advancements in decoding speech from brain activity have focused on decoding a single language. Hence, the extent to which bilingual speech production relies on unique or shared cortical activity across languages has remained unclear. Here, we leveraged electrocorticography, along with deep-learning and statistical natural-language models of English and Spanish, to record and decode activity from speech-motor cortex of a Spanish-English bilingual with vocal-tract and limb paralysis into sentences in either language. This was achieved without requiring the participant to manually specify the target language. Decoding models relied on shared vocal-tract articulatory representations across languages, which allowed us to build a syllable classifier that generalized across a shared set of English and Spanish syllables. Transfer learning expedited training of the bilingual decoder by enabling neural data recorded in one language to improve decoding in the other language. Overall, our findings suggest shared cortical articulatory representations that persist after paralysis and enable the decoding of multiple languages without the need to train separate language-specific decoders.

Empirical meropenem versus piperacillin/tazobactam for adult patients with sepsis (EMPRESS) trial: Protocol.

Piperacillin/tazobactam may be associated with less favourable outcomes than carbapenems in patients with severe bacterial infections, but the certainty of evidence is low. The Empirical Meropenem versus Piperacillin/Tazobactam for Adult Patients with Sepsis (EMPRESS) trial is an investigator-initiated, international, parallel-group, randomised, open-label, adaptive clinical trial with an integrated feasibility phase. We will randomise adult, critically ill patients with sepsis to empirical treatment with meropenem or piperacillin/tazobactam for up to 30 days. The primary outcome is 30-day all-cause mortality. The secondary outcomes are serious adverse reactions within 30 days; isolation precautions due to resistant bacteria within 30 days; days alive without life support and days alive and out of hospital within 30 and 90 days; 90- and 180-day all-cause mortality and 180-day health-related quality of life. EMPRESS will use Bayesian statistical models with weak to somewhat sceptical neutral priors. Adaptive analyses will be conducted after follow-up of the primary outcome for the first 400 participants concludes and after every 300 subsequent participants, with adaptive stopping for superiority/inferiority and practical equivalence (absolute risk difference <2.5%-points) and response-adaptive randomisation. The expected sample sizes in scenarios with no, small or large differences are 5189, 5859 and 2570 participants, with maximum 14,000 participants and ≥99% probability of conclusiveness across all scenarios. EMPRESS will compare the effects of empirical meropenem against piperacillin/tazobactam in adult, critically ill patients with sepsis. Due to the pragmatic, adaptive design with high probability of conclusiveness, the trial results are expected to directly inform clinical practice.

A Study on the Interaction Between Population Mobility and High-Quality Development of Manufacturing Industry in China —Based on the Data of Fujian Province

This paper uses relevant mathematical statistical models to verify the interactive effect between population mobility and the high-quality development of the manufacturing industry based on the panel data of 43 counties in the Fujian province from 2007 to 2021. The results show that (1) the inflow areas of population flow in the Fujian province are mainly concentrated in the southeast region; (2) there is mutual promotion between population mobility and the high-quality development of the manufacturing industry in the Fujian province, and the population mobility in adjacent areas has a significant promotion effect on the high-quality development of the local manufacturing industry; and (3) heterogeneity results show that after 2011, the interaction effect between population mobility and the high-quality development of the manufacturing industry has declined, and population inflow has a stronger role in promoting the high-quality development of the manufacturing industry than population outflow. The conclusions of this study are of great significance to the Fujian province for implementing a reasonable population policy and promoting the coordinated development of manufacturing enterprises.

Numerical modelling of dispersal of Ampelisca (Amphipoda Gammaridae) during their diel migration

The aim of this study is to analyse and model the dispersal of Ampelisca, a benthic genus of amphipod crustaceans, in the fine sand community of the Bay of Morlaix (western English Channel), during their nocturnal migration in the water column. Based on sampling of the suprabenthos (in the water column adjacent to the bottom) and surface plankton on 17 June 1994, Dauvin and Zouhiri (1996) studied the vertical distribution of Ampelisca during a spring swarming reproductive period. Statistical modelling techniques and specific numerical models were combined to simulate the effects of tidal currents, wind direction and speed on Ampelisca dispersal. As an illustration to constant pluriannual seasonal pattern, the dispersion of Ampelisca individuals considered as passive particles was studied at two reproductive periods, on a short night (17th-18th June 1994) during a neap tide, and on long night (7th-8th September 1994) during a spring tide. These models are used to investigate whether Ampelisca can be dispersed beyond the fine sand community in the immediate area of their small-scale benthic habitat (only 6 km²) during their diel vertical migration. According to the results, the speed and direction of currents will determine the trajectory of Ampelisca and the degree of dispersal outside their benthic habitat during a nighttime pelagic sortie. These simulations, particularly during spring tides, are incompatible with population stability, as they appear to be excessively dispersive. Short-term dispersal simulations for twenty minutes in the plankton allow the optimal return of individuals to the fine sand benthic habitats. We hypothesize there is an adaptative advantage for Ampelisca over time to short diel migrations. However, we currently do not know whether each individual makes a single migration, or several migrations in the water column during a night.

How does institutional theory illuminate the influence of the digital economy on R&amp;D networks?

PurposeThe purpose of this research is to explore how an innovation organization's orientation toward the digital economy influences its position within R&amp;D networks. By using institutional theory, the study aims to forecast market changes and understand how organizations can navigate the digital economy to secure essential resources and minimize dependencies.Design/methodology/approachThis study employs a longitudinal panel dataset with 11,763 entries from 1995 to 2018, covering strategic emerging industries in China to analyze the impact of digital economy orientation on R&amp;D networks. Utilizing advanced statistical models, it assesses the role of the legal environment as a moderator. This methodological approach facilitates a robust examination of the nexus between digital orientation and network dynamics within the context of institutional theory.FindingsThe study reveals that an organization's digital economy orientation enhances its centrality in R&amp;D networks but reduces its control over structural holes. The legal environment negatively moderates the impact of digital economy orientation on network centrality, while positively influencing the relationship with network structural holes. These findings offer new insights into how institutional forces shape the strategic positioning of organizations in R&amp;D collaborations.Originality/valueThis research offers a fresh perspective on the digital economy's impact on R&amp;D networks, particularly in the Industry-University-Research (IUR) context. It extends the discourse by integrating institutional theory to elucidate the adaptation of R&amp;D networks in the digital era. By identifying the legal environment as a moderator, the study provides a nuanced understanding of the strategic alignment within networks influenced by digital advancements. The unique focus on China's R&amp;D networks presents a valuable contribution to the global discussion on digital integration and innovation ecosystems, highlighting the intersection of policy, academia, and industry in shaping research and development trajectories.

High-resolution ghost imaging through dynamic and complex scattering media with adaptive moving average correction

Optical imaging through dynamic and complex scattering media has attracted various applications, e.g., ranging from scene imaging to cell imaging. Nevertheless, imaging through complex media is full of challenges attributed to the inhomogeneous scattering, leading to nonlinear effects. Although ghost imaging (GI) has proven effective in solving some scattering problems, dynamic and complex scattering still requires an efficient solution. In this Letter, we report a model based on adaptive moving average (AMA) to correct the influence of dynamic scattering media from a statistical perspective for high-resolution GI. The developed AMA correction method selects an appropriate time window based on the changing trend of measured single-pixel light intensities to accurately correct a series of dynamic scaling factors. Then, the corrected single-pixel light intensities are used for ghost reconstruction using a second-order correlation algorithm. A series of optical experiments are conducted to verify superiority of the proposed method. Moreover, the proposed method can be applied with other algorithms to enhance the quality of the reconstructed ghost images. By leveraging a statistical model based on the measured data, the proposed scheme offers an enhanced solution to solving dynamic and complex scattering problems in GI.

Data-driven analysis of annual rain distributions

Rainfall is an important component of the climate system, and its statistical properties are vital for prediction purposes. In this study, we have developed a statistical method for constructing the distribution of annual precipitation. The method is based on the convolution of the measured monthly rainfall distributions and does not depend on any presumed annual rainfall distribution. Using a simple statistical model, we demonstrate that our approach allows for a better prediction of extremely dry or wet years with a recurrence time several times longer than the original time series. The method that has been proposed can be utilized for other climate variables as well. Published by the American Physical Society 2024

Optimizing recombinant antibody fragment production: A comparison of artificial intelligence and statistical modeling.

Maximizing the recombinant protein yield necessitates optimizing the production medium. This can be done using a variety of methods, including the conventional "one-factor-at-a-time" approach and more recent statistical and mathematical methods such as artificial neural network (ANN), genetic algorithm, etc. Every approach has advantages and disadvantages of its own, yet even when a technique has flaws, it is nevertheless used to get the best results. Here, one categorical variable and four numerical parameters, including post-induction time, inducer concentration, post-induction temperature, and pre-induction cell density, were optimized using the 232 experimental assays of the central composite design. The direct and indirect effects of factors on the yield of anti-epithelial cell adhesion molecule extracellular domain fragment antibody were examined using statistical methods. The analysis of variance results indicate that the response surface methodology (RSM) model is effective in predicting the amount of produced single-chain fragment variable (p-value = 0.0001 and R2 = 0.905). For ANN modeling, the evaluation using normalized root mean square error (NRMSE) and R2 values shows a good fit (R2 = 0.942) and accurate predictions (NRMSE = 0.145). The analysis of error parameters and R2 of a dataset, which contained 30 data points randomly selected from the complete dataset, showed that the ANN model had a higher R2 value (0.968) compared to the RSM model (0.932). Furthermore, the ANN model demonstrated stronger predictive ability with a lower NRMSE (0.048vs. 0.064). Induction at the cell density of 0.7 and an isopropyl β-D-1-thiogalactopyranoside concentration of 0.6mM for 32 h at 30°C in BW25113 was the ideal culture condition leading to the protein yield of 259.51mg/L. Under the optimum conditions, the output values predicted by the ANN model (259.83mg/L) were more in line with the experimental data (259.51mg/L) than the RSM (276.13mg/L) expected value. This outcome demonstrated that the ANN model outperforms the RSM in terms of prediction accuracy.

The Lomax-Exponentiated Odds Ratio–G Distribution and Its Applications

This paper introduces the Lomax-exponentiated odds ratio–G (L-EOR–G) distribution, a novel framework designed to adeptly navigate the complexities of modern datasets. It blends theoretical rigor with practical application to surpass the limitations of traditional models in capturing complex data attributes such as heavy tails, shaped curves, and multimodality. Through a comprehensive examination of its theoretical foundations and empirical data analysis, this study lays down a systematic theoretical framework by detailing its statistical properties and validates the distribution’s efficacy and robustness in parameter estimation via Monte Carlo simulations. Empirical evidence from real-world datasets further demonstrates the distribution’s superior modeling capabilities, supported by compelling various goodness-of-fit tests. The convergence of theoretical precision and practical utility heralds the L-EOR–G distribution as a groundbreaking advancement in statistical modeling, significantly enhancing precision and adaptability. The new model not only addresses a critical need within statistical modeling but also opens avenues for future research, including the development of more sophisticated estimation methods and the adaptation of the model for various data types, thereby promising to refine statistical analysis and interpretation across a wide array of disciplines.

Examining the relationship between atmospheric pollutants and meteorological factors in Asansol city, West Bengal, India, using statistical modelling.

Meteorological conditions significantly impact ambient air quality in urban environments. This study focuses on Asansol, known as the "Coal City" and the "Industrial Heart of West Bengal," a notable hotspot for air pollution. Despite its significance, limited research has addressed the influence of meteorological factors on key air pollutants in this urban area. From January 2019 to December 2023, this investigation explores the relationships between meteorological parameters (including atmospheric temperature, relative humidity, rainfall, wind speed) and the concentrations of crucial air pollutants (PM2.5, PM10, NO2, SO2). Temporal trends in air pollutant concentrations are also analysed. The Spearman correlation method is used to establish associations between pollutant concentrations and meteorological variables, while multiple linear regression (MLR) models are employed to assess meteorological factors and potential impact on pollutant concentrations. The analysis reveals a decreasing trend in pollutant concentrations in Asansol. Temperature exhibits negative correlations with all pollutants in all seasons except for a positive correlation during the monsoon. Rainfall consistently displays significant negative correlations with pollutants in all seasons. Relative humidity is negatively correlated with pollutants in all seasons, and wind speed, except during the post-monsoon season, shows negative correlations with all pollutants. Linear models excel in predicting particulate matter concentrations but perform poorly in predicting gaseous contaminants. Accounting for seasonal fluctuations and meteorological parameters, this research enhances the accuracy of air pollution forecasting, contributing to a better understanding of air quality dynamics in Asansol and similar urban areas.