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Journal articles on the topic 'M-quantile'

Author: Grafiati

Published: 9 March 2023

Last updated: 19 July 2025

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Borgoni, Riccardo, Paola Del Bianco, Nicola Salvati, Timo Schmid, and Nikos Tzavidis. "Modelling the distribution of health-related quality of life of advanced melanoma patients in a longitudinal multi-centre clinical trial using M-quantile random effects regression." Statistical Methods in Medical Research 27, no. 2 (2016): 549–63. http://dx.doi.org/10.1177/0962280216636651.

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Health-related quality of life assessment is important in the clinical evaluation of patients with metastatic disease that may offer useful information in understanding the clinical effectiveness of a treatment. To assess if a set of explicative variables impacts on the health-related quality of life, regression models are routinely adopted. However, the interest of researchers may be focussed on modelling other parts (e.g. quantiles) of this conditional distribution. In this paper, we present an approach based on quantile and M-quantile regression to achieve this goal. We applied the methodologies to a prospective, randomized, multi-centre clinical trial. In order to take into account the hierarchical nature of the data we extended the M-quantile regression model to a three-level random effects specification and estimated it by maximum likelihood.

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Alfò, Marco, Nicola Salvati, and M. Giovanna Ranallli. "Finite mixtures of quantile and M-quantile regression models." Statistics and Computing 27, no. 2 (2016): 547–70. http://dx.doi.org/10.1007/s11222-016-9638-1.

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Komunjer, Ivana, and Quang Vuong. "SEMIPARAMETRIC EFFICIENCY BOUND IN TIME-SERIES MODELS FOR CONDITIONAL QUANTILES." Econometric Theory 26, no. 2 (2009): 383–405. http://dx.doi.org/10.1017/s0266466609100038.

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We derive the semiparametric efficiency bound in dynamic models of conditional quantiles under a sole strong mixing assumption. We also provide an expression of Stein’s (1956) least favorable parametric submodel. Our approach is as follows: First, we construct a fully parametric submodel of the semiparametric model defined by the conditional quantile restriction that contains the data generating process. We then compare the asymptotic covariance matrix of the MLE obtained in this submodel with those of the M-estimators for the conditional quantile parameter that are consistent and asymptotically normal. Finally, we show that the minimum asymptotic covariance matrix of this class of M-estimators equals the asymptotic covariance matrix of the parametric submodel MLE. Thus, (i) this parametric submodel is a least favorable one, and (ii) the expression of the semiparametric efficiency bound for the conditional quantile parameter follows.

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Otto-Sobotka, Fabian, Nicola Salvati, Maria Giovanna Ranalli, and Thomas Kneib. "Adaptive semiparametric M-quantile regression." Econometrics and Statistics 11 (July 2019): 116–29. http://dx.doi.org/10.1016/j.ecosta.2019.03.001.

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Maiboroda, Rostyslav, Vitaliy Miroshnychenko, and Olena Sugakova. "Quantile estimators for regression errors in mixture models with varying concentrations." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 1 (2024): 45–50. http://dx.doi.org/10.17721/1812-5409.2024/1.8.

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In this paper we consider data obtained from a mixture of M different sub-populations (mixture components). Dependencies between the observed variables are described by nonlinear regression models with unknown regression parameters and error terms distributions different for different components. The mixing probabilities (concentrations of the components in the mixture) vary from observation to observation. Estimators for quantiles of error terms distributions are considered based on weighted empirical distribution functions of the regression models residuals. Consistency of these estimators is demonstrated. The results can be applied to the construction of quantile vs. quantile plots for visual comparison and analysis of error terms distributions.

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Moreno, Justo De Jorge, and Oscar Rojas Carrasco. "EVOLUTION OF EFFICIENCY AND ITS DETERMINANTS IN THE RETAIL SECTOR IN SPAIN: NEW EVIDENCE." Journal of Business Economics and Management 16, no. 1 (2014): 244–60. http://dx.doi.org/10.3846/16111699.2012.732958.

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The purpose of this work is twofold: on the one hand, recent methodologies will be used to estimate technical efficiency and its determinants factors in Spain's retail sector. In particular, the order-m approach, which is based on the concept of expected minimum input function and quantile regression, for the analysis of the factors determinants of efficiency is used. On the other hand, the results obtained applying the methods mentioned in the Spanish retail sector can contribute to opening up a new field of analysis since the results may be compared by means of the methodologies proposed as well as those which already exist in the literature. The paper used data envelopment analysis stochastic (order-m) to measure efficiency and quantile regression analysis for the second stage in Spanish retail. For the second stage of analysis relative of the factors determinants of efficiency, we use quantile regression. We take account of heterogeneity between the different characteristics of firms, using quantile regression techniques. We find that firm size, age and market concentration are positively related to the efficiency along the quantiles considered in the analysis. The relationship between intensity of capital and better trained employees in the efficiency shows a curvilinear behavior. Also, there are significant differences by region to which the firm belongs. The main contribution of this paper is to provide an efficiency analysis for Spanish retail sector using a non parametric approach with a robust estimator and quantile regression analysis for second stage. This methodology allows for a more careful analysis of what happens at firm level.

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Nulkarim, Aldi Rochman, and Ika Yuni Wulansari. "M-quantile Chambers-Dunstan Untuk Pendugaan Area Kecil." Seminar Nasional Official Statistics 2021, no. 1 (2021): 80–89. http://dx.doi.org/10.34123/semnasoffstat.v2021i1.1065.

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Metode Small Area Estimations (SAE) digunakan sebagai pendekatan yang reliabel dalam mengatasi kendala ketidakcukupan sampel pada survei sampel. BPS memproduksi statistik area kecil menggunakan metode SAE popular seperti Empirical Best Linear Unbiased Prediction dalam model Fay-Herriot (EBLUP-FH). Metode EBLUP-FH sebagai pendekatan parametrik memerlukan asumsi normalitas dan terbebas dari outliers pada kedua komponen random effect-nya. Namun, hal tersebut sulit dipenuhi karena seringkali data di lapangan berperilaku ekstrim. Metode SAE M-quantile Chambers-Dunstan (CD) merelaksasi asumsi parametrik dan robust dalam inferensi terhadap outliers. Penelitian ini mengkaji metode M-quantile CD dalam meningkatkan robustness pendugaan area kecil melalui penerapannya pada data riil untuk estimasi rata-rata pengeluaran rumah tangga per kapita tingkat kecamatan di DI Yogyakarta tahun 2018. Penelitian ini menggunakan data Susenas 2018 dan Podes 2018. Hasil implementasi pada data riil menunjukkan model M-quantile CD berhasil memperbaiki presisi EBLUP-FH. Dengan mengimplementasikan M-quantile CD diharapkan estimasi data berperilaku ekstrim lebih akurat untuk pengambilan kebijakan di daerah.

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Al-Sabri, Haithm Mohammed Hamood, Norhafiza Nordin, and Hanita Kadir Shahar. "The impact of chief executive officer (CEO) and deal characteristics on mergers and acquisitions (M&A) duration: A quantile regression evidence from an emerging market." Asian Academy of Management Journal of Accounting and Finance 18, no. 1 (2022): 101–32. http://dx.doi.org/10.21315/aamjaf2022.18.1.5.

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This paper examines the impact of chief executive officer (CEO) and deal characteristics on mergers and acquisitions (M&A) duration in Malaysia. Univariate analysis and quantile regression (QR) are performed on 556 completed M&As transactions undertaken by Malaysian public firms from 2001 to 2019. In line with the upper echelons theory, which states that organizational outcomes can be predicted by looking at the characteristics of top-level executives, the findings from QR show that CEO characteristics significantly affect acquisition duration. This effect is conditional on the duration quantiles for CEO tenure and CEO duality but non-conditional for foreign CEO. Specifically, the findings reveal that the degree of influence by CEO characteristics gets stronger when the transactions are longer and complicated. CEO tenure can decrease M&A duration when a transaction falls in longer duration quantile. M&A transactions tend to take a longer duration when there is CEO duality. Foreign CEOs show more ability to execute transactions in a short duration compared to local CEOs. Deal characteristics such as deal size, merger transaction, hiring a financial advisor and conducting multiple acquisitions are main factors that prolong duration. The findings of this study may benefit policymakers, managers, and investors who involve directly and indirectly in an M&A process.

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Dreassi, Emanuela, M. Giovanna Ranalli, and Nicola Salvati. "Semiparametric M-quantile regression for count data." Statistical Methods in Medical Research 23, no. 6 (2014): 591–610. http://dx.doi.org/10.1177/0962280214536636.

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A.A.Aly, Eman-Eldin. "On quantile processes for m-dependent Rv's." Statistics 18, no. 3 (1987): 423–35. http://dx.doi.org/10.1080/02331888708802039.

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Chambers, Ray, and Nikos Tzavidis. "M-quantile models for small area estimation." Biometrika 93, no. 2 (2006): 255–68. http://dx.doi.org/10.1093/biomet/93.2.255.

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Salvati, Nicola, Monica Pratesi, Nikos Tzavidis, and Ray Chambers. "SPATIAL M-QUANTILE MODELS FOR SMALL AREA ESTIMATION." Statistics in Transition new series 10, no. 2 (2009): 251–67. http://dx.doi.org/10.59170/stattrans-2009-019.

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In small area estimation direct survey estimates that rely only on area-specific data can exhibit large sampling variability due to small sample sizes at the small area level. Efficient small area estimates can be constructed using explicit linking models that borrow information from related areas. The most popular class of models for this purpose are models that include random area effects. Estimation for these models typically assumes that the random area effects are uncorrelated. In many situations, however, it is reasonable to assume that the effects of neighbouring areas are correlated. Models that extend conventional random effects models to account for spatial correlation between the small areas have been recently proposed in literature. A new semi-parametric approach to small area estimation is based on the use of M-quantile models. Unlike traditional random effects models, M-quantile models do not depend on strong distributional assumptions and are robust to the presence of outliers. In its current form, however, the M-quantile approach to small area estimation does not allow for spatially correlated area effects. The aim of this paper is to extend the M-quantile approach to account for such spatial correlation between small areas.

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Ku, Yu-Yen, and Tze-Yu Yen. "Heterogeneous Effect of Financial Leverage on Corporate Performance: A Quantile Regression Analysis of Taiwanese Companies." Review of Pacific Basin Financial Markets and Policies 19, no. 03 (2016): 1650015. http://dx.doi.org/10.1142/s0219091516500156.

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The effect of financial leverage on corporate performance has been debated. We reexamine the effect by using a sample of 6,630 observations from nonfinancial Taiwanese publicly traded companies during the 2008–2012 period, employing the quantile regression approach and comparing its results with the ones provided by conventional models (least squares and fixed effects). Our empirical results show that the effect of financial leverage on the corporate performance is not homogeneous among various quantile levels: the financial leverage destroys (enhances) companies with low (high) return on equity quantiles. Moreover, the association between leverage and corporate performance is trivial when the mid-range performance quantiles are considered. Our findings are consistent with the results provided by Lee and Li [Lee, BS and M-YL Li (2012). Journal of Banking and Finance, 36, 2157–2173] for U.S. firms. The asymmetric relationship between financial leverage and the corporate performance identified in this study can adequately clarify the debated link between financial leverage and the corporate performance reported in previous empirical studies.

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Shim, Joo-Yong, and Chang-Ha Hwang. "M-quantile kernel regression for small area estimation." Journal of the Korean Data and Information Science Society 23, no. 4 (2012): 749–56. http://dx.doi.org/10.7465/jkdi.2012.23.4.749.

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Merlo, Luca, Lea Petrella, Nicola Salvati, and Nikos Tzavidis. "Marginal M-quantile regression for multivariate dependent data." Computational Statistics & Data Analysis 173 (September 2022): 107500. http://dx.doi.org/10.1016/j.csda.2022.107500.

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Tzavidis, Nikos, Nicola Salvati, Monica Pratesi, and Ray Chambers. "M-quantile models with application to poverty mapping." Statistical Methods and Applications 17, no. 3 (2007): 393–411. http://dx.doi.org/10.1007/s10260-007-0070-8.

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Xin, Hua, Jianping Zhu, Junge Sun, Chenlu Zheng, and Tzong-Ru Tsai. "Reliability Inference Based on the Three-Parameter Burr Type XII Distribution with Type II Censoring." International Journal of Reliability, Quality and Safety Engineering 25, no. 02 (2018): 1850010. http://dx.doi.org/10.1142/s0218539318500109.

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The three-parameter Burr type XII distribution (3pBXIID) is quite flexible and contains a wide range of distribution shapes for fitting lifetime data. However, it is difficult to obtain reliable estimates of the 3pBXIID quantiles from censored samples for evaluating the reliability of lifetime data. In this work, a Metropolis–Hastings Markov chain Monte Carlo (M-H MCMC) procedure is proposed to obtain reliable maximum likelihood estimates (MLEs) of the 3pBXIID quantiles from a type II censored sample. Moreover, the parametric bootstrap percentile procedure is used to obtain the confidence interval of the quantile of the 3pBXIID. The performance of the proposed M-H MCMC method is evaluated in view of Monte Carlo simulations. Two examples, regarding the survival lifetimes of breast cancer patients and the reliability inference on the lifetimes of oil-well pumps for sucker-rod oil pumping systems, are applied to illustrate the applications of the proposed M-H MCMC method and bootstrap procedure.

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Ben-Salha, Ousama, Mourad Zmami, Sami Sobhi Waked, Bechir Raggad, Faouzi Najjar, and Yazeed Mohammad Alenazi. "Assessing the Impacts of Transition and Physical Climate Risks on Industrial Metal Markets: Evidence from the Novel Multivariate Quantile-on-Quantile Regression." Atmosphere 16, no. 2 (2025): 233. https://doi.org/10.3390/atmos16020233.

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Climate change and global warming have been shown to increase the frequency and intensity of extreme weather events. Concurrently, substantial efforts are being directed toward fostering the transition to a low-carbon economy. These concurrent trends result in the emergence of both physical and transition climate risks. This study investigates the impacts of climate risks, both physical and transition, on the return of major industrial metals (aluminum, copper, iron, lead, tin, nickel, and zinc) between January 2005 and December 2023. Employing the novel multivariate quantile-on-quantile regression (m-QQR) approach, this study examines how climate risks affect metal markets under different market conditions and risk levels. The results reveal that transition risks exert a more significant adverse impact on metal returns during bearish markets conditions, particularly for metals linked to high-emission industries, while physical risks affect metal returns across a wider range of quantiles, often increasing volatility during extreme market conditions. Furthermore, copper and nickel, both of which are crucial for renewable energy development, demonstrate resilience at higher quantiles, highlighting their role in the transition to a low-carbon economy. Finally, these two metals may serve as effective hedges against losses in other metals that are more vulnerable to transition risks, like aluminum and lead.

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Yanuar, Ferra, Athifa Salsabila Deva, and Maiyastri Maiyastri. "Modeling Length of Hospital Stay for Patients With COVID-19 in West Sumatra Using Quantile Regression Approach." CAUCHY 7, no. 1 (2021): 118–28. http://dx.doi.org/10.18860/ca.v7i1.12995.

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This study aims to construct the model for the length of hospital stay for patients with COVID-19 using quantile regression and Bayesian quantile approaches. The quantile regression models the relationship at any point of the conditional distribution of the dependent variable on several independent variables. The Bayesian quantile regression combines the concept of quantile analysis into the Bayesian approach. In the Bayesian approach, the Asymmetric Laplace Distribution (ALD) distribution is used to form the likelihood function as the basis for formulating the posterior distribution. All 688 patients with COVID-19 treated in M. Djamil Hospital and Universitas Andalas Hospital in Padang City between March-July 2020 were used in this study. This study found that the Bayesian quantile regression method results in a smaller 95% confidence interval and higher value than the quantile regression method. It is concluded that the Bayesian quantile regression method tends to yield a better model than the quantile method. Based on the Bayesian quantile regression method, it investigates that the length of hospital stay for patients with COVID-19 in West Sumatra is significantly influenced by Age, Diagnoses status, and Discharge status.

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Hall, Peter, and Joel L. Horowitz. "Bandwidth Selection in Semiparametric Estimation of Censored Linear Regression Models." Econometric Theory 6, no. 2 (1990): 123–50. http://dx.doi.org/10.1017/s0266466600005089.

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Quantile and semiparametric M estimation are methods for estimating a censored linear regression model without assuming that the distribution of the random component of the model belongs to a known parametric family. Both methods require estimating derivatives of the unknown cumulative distribution function of the random component. The derivatives can be estimated consistently using kernel estimators in the case of quantile estimation and finite difference quotients in the case of semiparametric M estimation. However, the resulting estimates of derivatives, as well as parameter estimates and inferences that depend on the derivatives, can be highly sensitive to the choice of the kernel and finite difference bandwidths. This paper discusses the theory of asymptotically optimal bandwidths for kernel and difference quotient estimation of the derivatives required for quantile and semiparametric M estimation, respectively. We do not present a fully automatic method for bandwidth selection.

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Wu, T. S., Y. J. Chen, W. N. Huang, Y. H. Chen, and Y. M. Chen. "POS0326 POLYGENIC RISK SCORE OF RHEUMATOID ARTHRITIS PREDICTS BONY EROSION." Annals of the Rheumatic Diseases 82, Suppl 1 (2023): 408.1–409. http://dx.doi.org/10.1136/annrheumdis-2023-eular.1204.

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BackgroundPolygenic risk scores (PRS) are widely used to estimate disease risks and predict clinical outcomes. However, differences exist among PRS derived from genome-wide association study (GWAS) of ethnicities across the globe.ObjectivesWe aimed to build up PRS for predicting the development of rheumatoid arthritis (RA) in a Taiwanese population and investigate whether PRS of RA may be associated with bone erosion.MethodsWe constructed PRS using GWAS data from a hospital-based cohort of 2044 RA cases and 7950 non-RA controls participating in the Taiwan Precision Medicine Initiative. LDpred2, PLINK and PRsice2 models were used to evaluate the area under curve (AUC) of RA susceptibility in both training and testing datasets (1022 RA cases and 3975 controls). Demographic data, seropositivity of rheumatoid factor (RF), anti-citrullinated protein antibody (ACPA), treatment with biological and targeted synthetic disease modifying anti-rheumatic drugs (bDMARDs and tsDMARDs) and bone erosions by either plain x ray or ultrasonography were compared among quantiles of PRS.ResultsPRS of 97,396 single nucleotide polymorphisms derived from LDpred2 exhibited the highest AUC for predicting the development of RA in the overall dataset as compared with the remaining algorithms. Participants with the top RA-PRS quantile have the highest proportion of RF and ACPA positivity (74.8% & 65.0%, respectively), bone erosion (86.4%) and higher chance to receive bDMARDs or tsDMARDs (42.3%) as compared to the counterparts. In addition, RA-PRS quantile was a significant risk for bone erosion in the multivariate regression model with the adjustment of RF, ACPA positivity and therapeutic medication, specifically in the group of age < 60 years.ConclusionPRS is associated with seropositivity, erosive bone disease, need for advanced therapy in Taiwanese patients with RA.Reference[1]Honda S, Ikari K, Yano K, Terao C, Tanaka E, Harigai M, Kochi Y. Association of Polygenic Risk Scores With Radiographic Progression in Patients With Rheumatoid Arthritis. Arthritis Rheumatol. 2022 May;74(5):791-800.Table 1.Basic demographics of 2042 participants with RA by RA-PRS quantiles1st quantile(n=511)2nd quantile(n=510)3rd quantile(n=510)4th quantile(n=511)pvalueN%N%N%N%RA diagnosis age < 60 years36270.8438976.2738675.6940078.280.04Female gender40479.0641481.1840278.8239176.520.34BMI23.74.1024.34.4123.94.1423.64.060.04Smoking407.83407.84428.24428.220.99RF positivity24957.3727460.6230365.1634574.84<0.0001ACPA positivity15338.8320148.3225660.5228264.98<0.0001ESR (mm/hr)31.3826.1832.5629.2433.6127.8235.9329.050.07CRP (mg/L)0.771.800.891.900.792.210.972.210.39DAS284.291.444.271.444.121.354.421.470.10Periodontitis367.05326.27377.25305.870.79Boen erosion15877.0719378.7822785.3425586.440.01Glucocorticoid40278.6742182.5543284.7144687.280.002Methotrexate28054.7932062.7535369.2239076.32<0.0001Hydroxychloroquine41280.6343284.7144487.0645288.450.003Sulfasalazine15129.5519538.2418836.8622343.64<0.0001Target therapies13526.4216231.7619438.0421642.27<0.0001TNFi8817.221022013025.4915329.94<0.0001Non-TNFi5610.968115.889218.0410520.550.0003JAKi316.07418.04489.41418.020.26By ANOVA test.RA: rheumatoid arthritis; PRS: polygenic risk score; BMI: body mass index; RF: rheumatoid factor; ACPA: anti-citrullinated protein antibody; ESR: erythrocyte sedimentation rate; CRP: C reactive protein; DAS28: disease activity score by 28 joints; TNFi: tumor necrosis factor inhibitor; JAKi: Janus kinase inhibitorFigure 1.Risk of (A) bone erosion (B) RF positivity (C) ACPA positivity (D) targeted therapy in second, third and top quantiles as compared to the first quantile in participants aged < 60 years. RF: rheumatoid factor; ACPA: anti-citrullinated protein antibody. *p< 0.05Acknowledgements:NIL.Disclosure of InterestsNone Declared.

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Vinciotti, Veronica, and Keming Yu. "M-quantile Regression Analysis of Temporal Gene Expression Data." Statistical Applications in Genetics and Molecular Biology 8, no. 1 (2009): 1–20. http://dx.doi.org/10.2202/1544-6115.1452.

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Salvati, N., N. Tzavidis, M. Pratesi, and R. Chambers. "Small area estimation via M-quantile geographically weighted regression." TEST 21, no. 1 (2010): 1–28. http://dx.doi.org/10.1007/s11749-010-0231-1.

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Jallad, Ra’fat, Ahmad Tina, and Antonios Persakis. "Mergers and Acquisitions’ Moderating Effect on the Relationship Between Credit Risk and Bank Value: A Quantile Regression Approach." Journal of Risk and Financial Management 18, no. 2 (2025): 100. https://doi.org/10.3390/jrfm18020100.

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This research explores the relationship between credit risk and bank value within the framework of horizontal mergers and acquisitions (M&A), employing a quantile regression approach to analyze how horizontal M&A activities moderate this relationship across 110 operational Bank Holding Companies (BHCs) over 23 years. This paper stands out from previous studies by extending the scope beyond linear approaches and using the Quantiles via Moments estimator to address potential endogeneity concerns. The results demonstrate a significant negative link between credit risk and bank value, which decreases in magnitude as moving higher in the value distribution. Conversely, there is a consistent positive connection between M&A activities and bank value that is stable across different quantiles of value. Mergers and acquisitions worsen the negative impact of credit risk on bank value, affecting banks with both low and high values similarly. The findings provide useful information for investors, practitioners, and policymakers in the banking industry. Investors may use credit risk and value proposition assessments to make well-informed investment decisions, or to construct well-diversified portfolios, and identify appropriate institutions for mergers and acquisitions to enhance value. It is recommended that practitioners prioritize efficient credit risk management, especially before engaging in M&A activities and aligning them with the bank’s value proposition. Policymakers should develop guidelines to regulate M&A transactions, using established dynamic credit risk standards that correspond to banks’ value propositions, to promote financial stability and drive industry expansion.

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Khan, Kiren S., Jessica Logan, Laura M. Justice, Ryan P. Bowles, and Shayne B. Piasta. "The Contribution of Vocabulary, Grammar, and Phonological Awareness Across a Continuum of Narrative Ability Levels in Young Children." Journal of Speech, Language, and Hearing Research 64, no. 9 (2021): 3489–503. http://dx.doi.org/10.1044/2021_jslhr-20-00403.

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Purpose Narrative skill represents a higher-level linguistic skill that shows incremental development in the preschool years. During these years, there are considerable individual differences in this skill, with some children being highly skilled narrators (i.e., precocious) relative to peers of their age. In this study, we explored the contribution of three lower-level language skills to a range of narrative abilities, from children performing below expected levels for their age to those performing much higher than the expected levels for their age. We speculated that individual differences in lower-level skills would contribute meaningfully to variability in narrative skills. Method Using a sample of 336 children between 3 and 6 years of age ( M = 4.27 years, SD = 0.65), both multiple regression and quantile regression approaches were used to explore how vocabulary, grammar, and phonological awareness account for variance in children's “narrative ability index” (NAI), an index of how children scored on the Narrative Assessment Protocol–Second Edition relative to the expected performance for their age. Results Multiple regression results indicated that lower-level language skills explained a significant amount of variance (approximately 13%) in children's NAI scores. Quantile regression results indicated that phonological awareness and vocabulary accounted for significant variance in children's NAI scores at lower quantiles. At the median quantile, vocabulary and grammar accounted for significant variance in children's NAI scores. For precocious narrators, only vocabulary accounted for a significant amount of variance in children's NAI scores. Conclusion Results indicate that lower-level language skills work in conjunction to support narrative skills at different ability levels, improving understanding of how lower-level language skills contribute across a spectrum of higher-level linguistic abilities.

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Zhang, Biao. "M-estimation and quantile estimation in the presence of auxiliary information." Journal of Statistical Planning and Inference 44, no. 1 (1995): 77–94. http://dx.doi.org/10.1016/0378-3758(94)00040-3.

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Zhang, Yongxia, Qi Wang, and Maozai Tian. "Smoothed Quantile Regression with Factor-Augmented Regularized Variable Selection for High Correlated Data." Mathematics 10, no. 16 (2022): 2935. http://dx.doi.org/10.3390/math10162935.

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This paper studies variable selection for the data set, which has heavy-tailed distribution and high correlations within blocks of covariates. Motivated by econometric and financial studies, we consider using quantile regression to model the heavy-tailed distribution data. Considering the case where the covariates are high dimensional and there are high correlations within blocks, we use the latent factor model to reduce the correlations between the covariates and use the conquer to obtain the estimators of quantile regression coefficients, and we propose a consistency strategy named factor-augmented regularized variable selection for quantile regression (Farvsqr). By principal component analysis, we can obtain the latent factors and idiosyncratic components; then, we use both as predictors instead of the covariates with high correlations. Farvsqr transforms the problem from variable selection with highly correlated covariates to that with weakly correlated ones for quantile regression. Variable selection consistency is obtained under mild conditions. Simulation study and real data application demonstrate that our method is better than the common regularized M-estimation LASSO.

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Gao, Na, Yi Ma, Mingli Zhao, et al. "Quantile Analysis of Long-Term Trends of Near-Surface Chlorophyll-a in the Pearl River Plume." Water 12, no. 6 (2020): 1662. http://dx.doi.org/10.3390/w12061662.

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The concentration of chlorophyll-a (CHL) is an important proxy for the amount of phytoplankton biomass in the ocean. Characterizing the variability of CHL in the Pearl River Plume (PRP) is therefore of great importance for the understanding of the changes in oceanic productivity in the coastal region. By applying quantile regression analysis on 21-year (1998–2018) near-surface CHL data from satellite observations, this study investigated the long-term trend of CHL in the PRP. The results show decreasing trends (at an order of 10−2 mg m−3 year−1) for all percentiles of the CHL in the PRP, suggesting a decrease in productivity in the past two decades. The trends differ fundamentally from those in the open regions of the northern South China Sea with mixed signs and small magnitudes (10−4 mg m−3 year−1). The magnitudes of the trends in high quantiles (>80th) are larger than those in low quantiles (<50th) in the PRP, indicative of a decrease in the variance of the CHL. The area with apparent decreasing trends is restricted to the PRP in summer and extends to the entire coastal region in winter. This decrease in CHL is possibly attributed to the decrease in nutrient input from the river runoff and the weakening of wind-forced mixing rather than the changes in sea surface temperature. This study extends our knowledge on the variability of CHL in the PRP and provides references to the investigation of the changes of the coastal ecological environment.

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Cade, Brian S., and Pamela R. Johnson. "Quantile Equivalence to Evaluate Compliance With Habitat Management Objectives." Journal of Fish and Wildlife Management 2, no. 2 (2011): 169–82. http://dx.doi.org/10.3996/052011-jfwm-032.

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Abstract Equivalence estimated with linear quantile regression was used to evaluate compliance with habitat management objectives at Arapaho National Wildlife Refuge based on monitoring data collected in upland (5,781 ha; n = 511 transects) and riparian and meadow (2,856 ha, n = 389 transects) habitats from 2005 to 2008. Quantiles were used because the management objectives specified proportions of the habitat area that needed to comply with vegetation criteria. The linear model was used to obtain estimates that were averaged across 4 y. The equivalence testing framework allowed us to interpret confidence intervals for estimated proportions with respect to intervals of vegetative criteria (equivalence regions) in either a liberal, benefit-of-doubt or conservative, fail-safe approach associated with minimizing alternative risks. Simple Boolean conditional arguments were used to combine the quantile equivalence results for individual vegetation components into a joint statement for the multivariable management objectives. For example, management objective 2A required at least 809 ha of upland habitat with a shrub composition ≥0.70 sagebrush (Artemisia spp.), 20–30% canopy cover of sagebrush ≥25 cm in height, ≥20% canopy cover of grasses, and ≥10% canopy cover of forbs on average over 4 y. Shrub composition and canopy cover of grass each were readily met on &gt;3,000 ha under either conservative or liberal interpretations of sampling variability. However, there were only 809–1,214 ha (conservative to liberal) with ≥10% forb canopy cover and 405–1,098 ha with 20–30% canopy cover of sagebrush ≥25 cm in height. Only 91–180 ha of uplands simultaneously met criteria for all four components, primarily because canopy cover of sagebrush and forbs was inversely related when considered at the spatial scale (30 m) of a sample transect. We demonstrate how the quantile equivalence analyses also can help refine the numerical specification of habitat objectives and explore specification of spatial scales for objectives with respect to sampling scales used to evaluate those objectives.

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Dehling, H., R. Fried, and M. Wendler. "A robust method for shift detection in time series." Biometrika 107, no. 3 (2020): 647–60. http://dx.doi.org/10.1093/biomet/asaa004.

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Summary We present a robust and nonparametric test for the presence of a changepoint in a time series, based on the two-sample Hodges–Lehmann estimator. We develop new limit theory for a class of statistics based on two-sample U-quantile processes in the case of short-range dependent observations. Using this theory, we derive the asymptotic distribution of our test statistic under the null hypothesis of a constant level. The proposed test shows better overall performance under normal, heavy-tailed and skewed distributions than several other modifications of the popular cumulative sums test based on U-statistics, one-sample U-quantiles or M-estimation. The new theory does not involve moment conditions, so any transform of the observed process can be used to test the stability of higher-order characteristics such as variability, skewness and kurtosis.

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Bianchi, Annamaria, and Nicola Salvati. "Asymptotic Properties and Variance Estimators of the M-quantile Regression Coefficients Estimators." Communications in Statistics - Theory and Methods 44, no. 11 (2014): 2416–29. http://dx.doi.org/10.1080/03610926.2013.791375.

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Zhou, Xingcai, and Fangxia Zhu. "Wavelet-M-Estimation for Time-Varying Coefficient Time Series Models." Discrete Dynamics in Nature and Society 2020 (September 3, 2020): 1–11. http://dx.doi.org/10.1155/2020/1025452.

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This paper proposes wavelet-M-estimation for time-varying coefficient time series models by using a robust-type wavelet technique, which can adapt to local features of the time-varying coefficients and does not require the smoothness of the unknown time-varying coefficient. The wavelet-M-estimation has the desired asymptotic properties and can be used to estimate conditional quantile and to robustify the usual mean regression. Under mild assumptions, the Bahadur representation and the asymptotic normality of wavelet-M-estimation are established.

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Hundecha, Y., A. St-Hilaire, T. B. M. J. Ouarda, S. El Adlouni, and P. Gachon. "A Nonstationary Extreme Value Analysis for the Assessment of Changes in Extreme Annual Wind Speed over the Gulf of St. Lawrence, Canada." Journal of Applied Meteorology and Climatology 47, no. 11 (2008): 2745–59. http://dx.doi.org/10.1175/2008jamc1665.1.

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Abstract Changes in the extreme annual wind speed in and around the Gulf of St. Lawrence (Canada) were investigated through a nonstationary extreme value analysis of the annual maximum 10-m wind speed obtained from the North American Regional Reanalysis (NARR) dataset as well as observed data from selected stations of Environment Canada. A generalized extreme value distribution with time-dependent location and scale parameters was used to estimate quantiles of interest as functions of time at locations where significant trend was detected. A Bayesian method, the generalized maximum likelihood approach, is implemented to estimate the parameters. The analysis yielded shape parameters very close to 0, suggesting that the distribution can be modeled using the Gumbel distribution. A similar analysis using a nonstationary Gumbel model yielded similar quantiles with narrower credibility intervals. Overall, little change was detected over the period 1979–2004. Only 7% of the investigated grids exhibited trends at the 5% significant level, and the analysis performed on the reanalysis data at locations of significant trend indicated a rise in the median extreme annual wind speed by up to 2 m s−1 per decade in the southern coastal areas with a corresponding increase in the 90% and 99% quantiles of the extreme annual wind speeds by up to 5 m s−1 per decade. Also in the northern part of the gulf and some offshore areas in the south, the 50%, 90%, and 99% quantile values of the extreme annual wind speeds are noted to drop by up to 1.5, 3, and 5 m s−1, respectively. While the directions of the changes in the annual extremes at the selected stations are similar to those of the reanalysis data at nearby grid cells, the magnitudes and significance levels of the changes are generally inconsistent. Change at the same significance level over the same period of the NARR dataset was noted only at 2 stations out of 13.

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Sukhonos, P. A., V. V. Ivanov, and N. A. Diansky. "Long–period trends in water temperature changes in the northern part of the Atlantic Ocean from ocean reanalysis data." Doklady Rossijskoj akademii nauk. Nauki o Zemle 515, no. 2 (2024): 289–95. http://dx.doi.org/10.31857/s2686739724040145.

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The results of assessing long–period changes in water temperature in the North Atlantic Ocean (0°–70° N, 8°–80° W) based on data from ocean reanalyses and objective analyses for the periods 1961– 2011 and 1980–2011 are presented. The obtained estimates are based on the application of a nonparametric method of regression analysis (quantile regression) to the monthly ocean temperature for a quantile value of 0.5. During the period 1961–2011 warming was mainly observed in the upper 400 m layer in the region from the equator to 70° N. Over this 51-year period, the increase in the median monthly ocean temperature averaged over the analyzed water area ~0.5°C, and in the Gulf Stream–North Atlantic Current system ~1°C. During the period 1980–2011 warming in the North Atlantic Ocean mainly occurred in the upper 1 km layer at high latitudes (50°–65° N). Over this 32-year period, the increase in the median monthly ocean temperature in the subpolar gyre in the upper 400 m layer was ~1°C.

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Hu, Jie, Yu Chen, Weiping Zhang, and Xiao Guo. "Penalized high‐dimensional M‐quantile regression: From L 1 to L p optimization." Canadian Journal of Statistics 49, no. 3 (2021): 875–905. http://dx.doi.org/10.1002/cjs.11597.

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Costanzo, Antonella. "The Effect of M@tabel on Italian Students’ Performances: A Quantile Regression Approach." Procedia - Social and Behavioral Sciences 197 (July 2015): 236–44. http://dx.doi.org/10.1016/j.sbspro.2015.07.130.

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Borgoni, R., A. Carcagní, N. Salvati, and T. Schmid. "Analysing radon accumulation in the home by flexible M-quantile mixed effect regression." Stochastic Environmental Research and Risk Assessment 33, no. 2 (2019): 375–94. http://dx.doi.org/10.1007/s00477-018-01643-1.

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Bianchi, Annamaria, Enrico Fabrizi, Nicola Salvati, and Nikos Tzavidis. "Estimation and Testing in M-quantile Regression with Applications to Small Area Estimation." International Statistical Review 86, no. 3 (2018): 541–70. http://dx.doi.org/10.1111/insr.12267.

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Giusti, C., N. Tzavidis, M. Pratesi, and N. Salvati. "Resistance to Outliers of M-Quantile and Robust Random Effects Small Area Models." Communications in Statistics - Simulation and Computation 43, no. 3 (2013): 549–68. http://dx.doi.org/10.1080/03610918.2012.707724.

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Willey, Joshua Z., Yeseon P. Moon, Erin R. Kulick, et al. "Physical Inactivity Predicts Slow Gait Speed in an Elderly Multi-Ethnic Cohort Study: The Northern Manhattan Study." Neuroepidemiology 49, no. 1-2 (2017): 24–30. http://dx.doi.org/10.1159/000479695.

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Introduction: Gait speed is associated with multiple adverse outcomes of aging. We hypothesized that physical inactivity would be prospectively inversely associated with gait speed independently of white matter hyperintensity volume and silent brain infarcts on MRI. Methods: Participants in the Northern Manhattan Study MRI sub-study had physical activity assessed when they were enrolled into the study. A mean of 5 years after the MRI, participants had gait speed measured via a timed 5-meter walk test. Physical inactivity was defined as reporting no leisure-time physical activity. Multi-variable logistic and quantile regression was performed to examine the associations between physical inactivity and future gait speed adjusted for confounders. Results: Among 711 participants with MRI and gait speed measures (62% women, 71% Hispanic, mean age 74.1 ± 8.4), the mean gait speed was 1.02 ± 0.26 m/s. Physical inactivity was associated with a greater odds of gait speed in the lowest quartile (<0.85 m/s, adjusted OR 1.90, 95% CI 1.17-3.08), and in quantile regression with 0.06 m/s slower gait speed at the lowest 20 percentile (p = 0.005). Conclusions: Physical inactivity is associated with slower gait speed independently of osteoarthritis, grip strength, and subclinical ischemic brain injury. Modifying sedentary behavior poses a target for interventions aimed at reducing decline in mobility.

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Magnussen, S., and P. Boudewyn. "Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators." Canadian Journal of Forest Research 28, no. 7 (1998): 1016–31. http://dx.doi.org/10.1139/x98-078.

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The distribution of canopy heights obtained with an airborne laser scanner over a field trial with Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) was a function of the vertical distribution of foliage area. Over a wide range of canopy structures, the proportion of laser pulses returned from or above a given reference height was proportional to the fraction of leaf area above it. We hypothesized that the quantile of the laser canopy heights matching in probability the fraction of leaf area above a desired height would be an unbiased estimator of same. This was confirmed in 36 (20 × 20 m) plots and 6 older validation plots. Canopy-based quantiles of the laser canopy height data were within 6% (mean 3%) of the field estimates. Laser and field estimates were strongly correlated (r ~ 0.8), and statistical tests supported the null hypotheses of no difference in mean stand height (P > 0.3). A geometric model successfully predicted the mean difference between the laser canopy heights and the mean tree height. Our results explicate why estimation of stand heights from laser scanner data based on the maximum canopy height value in each cell of a fixed area grid has been successful in practice.

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Diamantopoulou, Maria J., Ramazan Özçelik, Ünal Eler, and Burak Koparan. "From Regression to Machine Learning: Modeling Height–Diameter Relationships in Crimean Juniper Stands Without Calibration Overhead." Forests 16, no. 6 (2025): 972. https://doi.org/10.3390/f16060972.

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Accurate modeling of height–diameter (h–d) relationships is critical for forest inventory and management, particularly in complex forest ecosystems such as natural and pure Crimean juniper (Juniperus excelsa Bieb.) stands. This study evaluates both traditional parametric and modern machine learning (ML) approaches to develop reliable h–d models based on 2135 sample trees measured in southern Türkiye. The modeling approaches include fixed-effects (FE), mixed-effects (ME), three quantile regression (QR) models based on three, five, and nine quantile levels, and non-parametric ML methods: shallow multilayer perceptron (S_MLP), extreme gradient boost (XGBoost), and random forest (RF). According to the assessment metrics for the fitting and test datasets, the XGBoost modeling approach achieved the most accurate performance. For the fitting dataset, it achieved root mean square error values of 1.11 m and 1.21 m. For the test dataset, the corresponding error values were 1.16 m and 1.24 m, resulting in the highest accuracy among all models, closely followed by the RF and S_MLP models. A key practical advantage of ML approaches is that they do not depend on calibration scenarios, meaning they can operate without the need for preliminary parameter configuration. In contrast, the ME model showed the highest accuracy among the parametric methods when calibration was applied. In this case, when applying ME models, the study recommends calibrating the model by measuring four randomly selected trees per plot to balance prediction accuracy and field sampling effort.

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Fernandes, Renata Cordeiro, and Doroteia Aparecida Höfelmann. "Patterns of energy balance-related behaviors and food insecurity in pregnant women." Ciência & Saúde Coletiva 28, no. 3 (2023): 909–20. http://dx.doi.org/10.1590/1413-81232023283.13342022.

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Abstract The behaviors related to caloric balance during pregnancy can lead to short- and long-term repercussion over the life course. This study aimed to identify patterns of energy balance-related behavior (EBRB) and its association with food insecurity (FI) in pregnant women. Cross-sectional, with pregnant women undergoing prenatal care in public health units in Colombo, Brazil, in 2018/2019. EBRB patterns were identified by factor analysis, and the scores were compared according to FI levels (mild and moderate/severe (M/S) through quantile regression. Four EBRB patterns were identified among 535 pregnant women: Factor 1- household/caregiving activities, exercise/sport, and physical inactivity; Factor 2 - fruits and vegetables; Factor 3 - paid work and commuting; Factor 4 - soda and sweetened beverage, sweets, and goodies. After adjusted analyses, women with mild FI presented higher scores for Factor 1 and lower scores for Factor 3. Higher scores for Factor 4 (p25) were observed among women with mild FI in simultaneous quantile regression. M/S FI was associated with lower scores for Factor 3 (p75). Mixed patterns with factors negatively and positively associated with energy balance were identified among pregnant women with FI.

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Johansen, Søren, and Bent Nielsen. "BOUNDEDNESS OF M-ESTIMATORS FOR LINEAR REGRESSION IN TIME SERIES." Econometric Theory 35, no. 03 (2018): 653–83. http://dx.doi.org/10.1017/s0266466618000257.

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We show boundedness in probability uniformly in sample size of a general M-estimator for multiple linear regression in time series. The positive criterion function for the M-estimator is assumed lower semicontinuous and sufficiently large for large argument. Particular cases are the Huber-skip and quantile regression. Boundedness requires an assumption on the frequency of small regressors. We show that this is satisfied for a variety of deterministic and stochastic regressors, including stationary and random walks regressors. The results are obtained using a detailed analysis of the condition on the regressors combined with some recent martingale results.

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De Vleeschauwer, D., G. H. Petit, B. Steyaert, S. Wittevrongel, and H. Bruneel. "Calculation of end-to-end delay quantile in network of M/G/1 queues." Electronics Letters 37, no. 8 (2001): 535. http://dx.doi.org/10.1049/el:20010327.

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Frumento, Paolo, and Nicola Salvati. "Parametric modelling of M ‐quantile regression coefficient functions with application to small area estimation." Journal of the Royal Statistical Society: Series A (Statistics in Society) 183, no. 1 (2019): 229–50. http://dx.doi.org/10.1111/rssa.12495.

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Alamari, Mohammed B., Fatimah A. Almulhim, Zoulikha Kaid, and Ali Laksaci. "Functional Time Series Analysis Using Single-Index L1-Modal Regression." Symmetry 17, no. 3 (2025): 460. https://doi.org/10.3390/sym17030460.

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A new predictor in functional time series (FTS ) is considered. It is based on the asymmetric weighting function of quantile regression. More precisely, we assume that FTS is generated from a single-index model that permits the observation of endogenous–exogenous variables by combining the nonparametric model with a linear one. In parallel, the L1-modal predictor is estimated using the M-estimation of the derivative of the conditional quantile of the generated FTS. In the mathematical part, we prove the complete convergence of the constructed estimator, and we determine its convergence rate. An empirical analysis is performed to prove the applicability of the estimator and to evaluate the impact of different structures involved in the smoothing approach. This analysis is carried out using simulated and real data. Finally, the regressive nature of the constructed predictor allows it to provide a robust instantaneous predictor for environmental data.

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Wang, Yi, Haomiao Cheng, Bin Cai, and Fanding Xiang. "Identifying the Main Urban Density Factors and Their Heterogeneous Effects on PM2.5 Concentrations in High-Density Historic Neighborhoods from a Social-Biophysical Perspective: A Case Study in Beijing." Sustainability 17, no. 8 (2025): 3309. https://doi.org/10.3390/su17083309.

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The contradiction between urban density and sustainable environmental development is increasingly prominent. Although numerous studies have examined the impact of urban density on air pollution at the macro level, most previous research at the micro scale has either neglected socioeconomic factors, failed to analyze heterogeneous effects, or ignored historic neighborhoods where high pollution coexists with high density. By considering population, commercial buildings, vegetation, and road factors, an integrated social-biophysical perspective was introduced to evaluate how urban density influences PM2.5 concentration in a historic neighborhood. The study area was divided into 56 units of 120 m × 150 m granularity, as determined by the precision of the LBS population data. The lasso regression and quantile regression were adopted to explore the main factors affecting PM2.5 and their heterogeneous effects. The results showed that (1) building density was the most important driving factor of pollutants. It had a strong and consistent negative effect on PM2.5 concentrations at all quantile levels, indicating the homogeneity effect. (2) Short-term human mobility represented by the visiting population density was the second main factor influencing pollutants, which has a significantly positive influence on PM2.5. The heterogeneous effects suggested that the areas with moderate pollution levels were the key areas to control PM2.5. (3) Vegetation Patch Shape Index was the third main factor, which has a positive influence on PM2.5, indicating the complex vegetation patterns are not conducive to PM2.5 dispersion in historic neighborhoods. Its heterogeneous effect presented a curvilinear trend, peaking at the 50th quantile, indicating that moderately polluted areas are the most responsive to improvements in vegetation morphology for PM2.5 reduction. These findings can provide effective support for the improvement of air quality in historical neighborhoods of the city’s central area.

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Boiko, Yuri M. "Evolution of Statistical Strength during the Contact of Amorphous Polymer Specimens below the Glass Transition Temperature: Influence of Chain Length." Materials 16, no. 2 (2023): 491. http://dx.doi.org/10.3390/ma16020491.

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A comprehensive study of the statistical distribution of the auto-adhesion lap-shear strength (σ) of amorphous polymer–polymer interfaces using various types of statistical tests and models is a useful approach aimed at a better understanding of the mechanisms of the self-healing interface. In the present work, this approach has been applied, for the first time, to a temperature (T) range below the bulk glass transition temperature (Tgbulk). The interest of this T range consists in a very limited or even frozen translational segmental motion giving little or no chance for adhesion to occur. To clarify this issue, the two identical samples of entangled amorphous polystyrene (PS) with a molecular weight (M) of 105 g/mol or 106 g/mol were kept in contact at T = Tgbulk − 33 °C for one day. The as-self-bonded PS–PS auto-adhesive joints (AJ) of PSs differing in M by an order of magnitude were fractured at ambient temperature, and their σ distributions were analyzed using the Weibull model, the quantile-quantile plots, the normality tests, and the Gaussian distribution. It has been shown that the Weibull model most correctly describes the σ statistical distributions of the two self-bonded PS–PS AJs with different M due to the joints’ brittleness. The values of the Weibull modulus (a statistical parameter) m = 2.40 and 1.89 calculated for PSs with M = 105 and 106 g/mol, respectively, were rather close, indicating that the chain length has a minor effect on the σ data scatter. The Gaussian distribution has been found to be less appropriate for this purpose, though all the normality tests performed have predicted the correctness of the normal distribution for these PS–PS interfaces.

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Khalid, Noreen, Raja Fawad Zafar, Qasim Raza Syed, and Roni Bhowmik. "The Heterogeneous Effects of COVID-19 Outbreak on Stock Market Returns and Volatility: Evidence from Panel Quantile Regression Model." ETIKONOMI 20, no. 2 (2021): 225–38. http://dx.doi.org/10.15408/etk.v20i2.20587.

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The purpose of this study is to probe the impact of the novel coronavirus (COVID-19) outbreak on stock market returns and volatility in developed markets. We employ a panel quantile regression model to capture unobserved individual heterogeneity and distributional heterogeneity. The study's findings reveal that there is a heterogeneous impact of COVID-19 on stock market returns and volatility. More specifically, there is a negative impact of COVID-19 on stock returns in the bearish stock market; however, there is an insignificant impact of COVID-19 on stock returns in the bullish stock market. Furthermore, COVID-19 has a positive impact on stock market volatility across all quantiles.JEL Classification: G24, G30, O16How to Cite:Khalid, N., Zafar, R. F., Syed, Q. R., Bhowmik, R., & Jamil, M. (2021). The Heterogeneous Effects of COVID-19 Outbreak on Stock Market Returns and Volatility: Evidence from Panel Quantile Regression Model. Etikonomi, 20(2), xx – xx. https://doi.org/10.15408/etk.v20i2.20587.

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