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1

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 methodol
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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 asymptotical
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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 i
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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
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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 parame
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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 duratio
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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. M
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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 equi
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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 inte
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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 regre
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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
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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 i
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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
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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, whic
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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 i
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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-au
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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, sugge
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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 interpre
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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 bas
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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
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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 oc
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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-variabl
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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) pl
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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
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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 amo
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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
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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. A
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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 influen
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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
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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.
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