Journal articles: 'M-quantile regression'

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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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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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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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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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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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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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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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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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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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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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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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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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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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Haupt, Harry, Friedrich Lösel, and Mark Stemmler. "Quantile Regression Analysis and Other Alternatives to Ordinary Least Squares Regression." Methodology 10, no. 3 (2014): 81–91. http://dx.doi.org/10.1027/1614-2241/a000077.

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Data analyses by classical ordinary least squares (OLS) regression techniques often employ unrealistic assumptions, fail to recognize the source and nature of heterogeneity, and are vulnerable to extreme observations. Therefore, this article compares robust and non-robust M-estimator regressions in a statistical demonstration study. Data from the Erlangen-Nuremberg Development and Prevention Project are used to model risk factors for physical punishment by fathers of 485 elementary school children. The Corporal Punishment Scale of the Alabama Parenting Questionnaire was the dependent variable. Fathers’ aggressiveness, dysfunctional parent-child relations, various other parenting characteristics, and socio-demographic variables served as predictors. Robustness diagnostics suggested the use of trimming procedures and outlier diagnostics suggested the use of robust estimators as an alternative to OLS. However, a quantile regression analysis provided more detailed insights beyond the measures of central tendency and detected sources of considerable heterogeneity in the risk structure of father’s corporal punishment. Advantages of this method are discussed with regard to methodological and content issues.

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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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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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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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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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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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Schwanghart, Wolfgang, and Dirk Scherler. "Bumps in river profiles: uncertainty assessment and smoothing using quantile regression techniques." Earth Surface Dynamics 5, no. 4 (2017): 821–39. http://dx.doi.org/10.5194/esurf-5-821-2017.

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Abstract. The analysis of longitudinal river profiles is an important tool for studying landscape evolution. However, characterizing river profiles based on digital elevation models (DEMs) suffers from errors and artifacts that particularly prevail along valley bottoms. The aim of this study is to characterize uncertainties that arise from the analysis of river profiles derived from different, near-globally available DEMs. We devised new algorithms – quantile carving and the CRS algorithm – that rely on quantile regression to enable hydrological correction and the uncertainty quantification of river profiles. We find that globally available DEMs commonly overestimate river elevations in steep topography. The distributions of elevation errors become increasingly wider and right skewed if adjacent hillslope gradients are steep. Our analysis indicates that the AW3D DEM has the highest precision and lowest bias for the analysis of river profiles in mountainous topography. The new 12 m resolution TanDEM-X DEM has a very low precision, most likely due to the combined effect of steep valley walls and the presence of water surfaces in valley bottoms. Compared to the conventional approaches of carving and filling, we find that our new approach is able to reduce the elevation bias and errors in longitudinal river profiles.

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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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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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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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Magill, John R., Heather S. Myers, Trevor A. Lentz, et al. "Establishing Age- and Sex-Specific Norms for Pediatric Return-to-Sports Physical Performance Testing." Orthopaedic Journal of Sports Medicine 9, no. 8 (2021): 232596712110231. http://dx.doi.org/10.1177/23259671211023101.

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Background: Graft tears and contralateral anterior cruciate ligament (ACL) tears are common in pediatric athletes after ACL reconstruction. Use of objective return-to-sports (RTS) criteria, in particular physical performance tests (PPTs), is believed to reduce the incidence of secondary injury; however, pediatric norms for these tests are unknown. Purpose: To establish a proof of concept for the creation of age- and sex-based norms for commonly used RTS PPTs in healthy pediatric athletes, allowing the creation of growth curves for clinical referencing. Study Design: Cross-sectional study; Level of evidence, 3. Methods: A total of 100 healthy people who were between the ages of 6 and 18 years and involved in organized sports were enrolled, with even distributions of age and sex. All participants underwent 9 common RTS PPTs: stork test, stork test on Bosu, single-leg squat, single-leg squat on Bosu, clockwise and counterclockwise quadrant hops, single-leg hop for distance, 6-m timed hop, and triple crossover hop for distance. Mean performance across limbs was calculated for each individual. Chronological age, height, weight, sex, and self-reported Pubertal Maturational Observational Scale (PMOS) score were recorded. Univariable and multivariable models were created for each PPT, assessing the importance of the recorded descriptive variables. Quantile regression was used to create growth curves for each PPT. Results: The cohort was 52% female, and the mean ± standard deviation age was 11.7 ± 3.6 years. PMOS was highly correlated with age ( r = 0.86) and was excluded from the regressions. In univariable regression, age, height, and weight were strong predictors of performance for all PPTs, whereas sex was a predictor of performance on the single-leg and triple crossover hops for distance (with males outperforming females). Height and weight were excluded from multivariable regression because of multicollinearity with age. Multivariable regression showed predictive patterns for age and sex that were identical to those shown in the univariable analysis. Given ceiling effects, quantile regression for the stork tests was not possible, but quantile regression growth curves were successfully created for the 7 remaining PPTs. Conclusion: Chronological age and sex accurately predicted performance on common RTS PPTs in pediatric patients. The growth curves presented herein could assist clinicians with benchmarking pediatric patients postoperatively against a healthy athletic cohort.

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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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Malmstadt, Jill C., James B. Elsner, and Thomas H. Jagger. "Risk of Strong Hurricane Winds to Florida Cities." Journal of Applied Meteorology and Climatology 49, no. 10 (2010): 2121–32. http://dx.doi.org/10.1175/2010jamc2420.1.

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Abstract A statistical procedure for estimating the risk of strong winds from hurricanes is demonstrated and applied to several major cities in Florida. The procedure, called the hurricane risk calculator, provides an estimate of wind risk over different length periods and can be applied to any location experiencing this hazard. Results show that the city of Miami can expect to see hurricane winds blowing at 50 m s−1 [45.5–54.5 m s−1 is the 90% confidence interval (CI)] or stronger, on average, once every 12 yr. In comparison, the city of Pensacola can expect to see hurricane winds of 50 m s−1 (46.9–53.1 m s−1, 90% CI) or stronger once every 24 yr. A quantile regression is applied to hurricane wind speeds in the vicinity of Florida. Results show that the strongest hurricanes are getting stronger as a consequence of higher offshore intensification rates.

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Spagnolo, Francesco Schirripa, Nicola Salvati, Antonella D’Agostino, and Ides Nicaise. "The use of sampling weights in M ‐quantile random‐effects regression: an application to Programme for International Student Assessment mathematics scores." Journal of the Royal Statistical Society: Series C (Applied Statistics) 69, no. 4 (2020): 991–1012. http://dx.doi.org/10.1111/rssc.12418.

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Marchetti, Stefano, and Nikos Tzavidis. "Robust Estimation of the Theil Index and the Gini Coeffient for Small Areas." Journal of Official Statistics 37, no. 4 (2021): 955–79. http://dx.doi.org/10.2478/jos-2021-0041.

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Abstract Small area estimation is receiving considerable attention due to the high demand for small area statistics. Small area estimators of means and totals have been widely studied in the literature. Moreover, in the last years also small area estimators of quantiles and poverty indicators have been studied. In contrast, small area estimators of inequality indicators, which are often used in socio-economic studies, have received less attention. In this article, we propose a robust method based on the M-quantile regression model for small area estimation of the Theil index and the Gini coefficient, two popular inequality measures. To estimate the mean squared error a non-parametric bootstrap is adopted. A robust approach is used because often inequality is measured using income or consumption data, which are often non-normal and affected by outliers. The proposed methodology is applied to income data to estimate the Theil index and the Gini coefficient for small domains in Tuscany (provinces by age groups), using survey and Census micro-data as auxiliary variables. In addition, a design-based simulation is carried out to study the behaviour of the proposed robust estimators. The performance of the bootstrap mean squared error estimator is also investigated in the simulation study.

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Salvati, N., M. G. Ranalli, and M. Pratesi. "Small area estimation of the mean using non-parametric M-quantile regression: a comparison when a linear mixed model does not hold." Journal of Statistical Computation and Simulation 81, no. 8 (2011): 945–64. http://dx.doi.org/10.1080/00949650903575237.

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Zampogna, E., N. Ambrosino, R. Centis, et al. "Minimal clinically important difference of the 6-min walking test in patients with asthma." International Journal of Tuberculosis and Lung Disease 25, no. 3 (2021): 215–21. http://dx.doi.org/10.5588/ijtld.20.0928.

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BACKGROUND: The 6‐min walking test (6MWT) is responsive to physiological changes and pulmonary rehabilitation (PR) in patients with asthma. The minimal clinically important difference (MCID) has not been established yet.OBJECTIVE: To determine the MCID of 6MWT in patients with asthma.METHODS: Using the perceived change in walking ability and the modified Medical Research Council (mMRC) score as anchors, receiver operating characteristic curves and quantile regression, we evaluated 6MWT before and after PR in these patients. The St George Respiratory Questionnaire (SGRQ), the COPD assessment test (CAT) and other outcome measures were also assessed.RESULTS: Of 142 patients with asthma, 37 were enrolled. After PR, 6MWT increased from 453.4 m ± 88.8 to 493.0 m ± 97.2 (P = 0.0001); other outcome measures also increased. There was a slight correlation between baseline 6MWT and SGRQ, CAT and mMRC. No significant correlations were found between post‐PR changes in 6MWT and in other outcome measures. Comparing different methods of assessment, the MCID ranged from 26 m to 27 m.CONCLUSION: The most conservative estimate of the MCID of 6MWT after PR was 26 m in patients with asthma. This estimate may be useful in clinical interpretation of data, particularly in response to intervention studies.

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Del Sarto, Simone, Maria Francesca Marino, Maria Giovanna Ranalli, and Nicola Salvati. "Using finite mixtures of M-quantile regression models to handle unobserved heterogeneity in assessing the effect of meteorology and traffic on air quality." Stochastic Environmental Research and Risk Assessment 33, no. 7 (2019): 1345–59. http://dx.doi.org/10.1007/s00477-019-01687-x.

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Barmpadimos, I., J. Keller, D. Oderbolz, C. Hueglin, and A. S. H. Prévôt. "One decade of parallel PM10 and PM2.5 measurements in Europe: trends and variability." Atmospheric Chemistry and Physics Discussions 12, no. 1 (2012): 1–43. http://dx.doi.org/10.5194/acpd-12-1-2012.

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Abstract. The trends and variability of PM10, PM2.5 and PMcoarse concentrations at seven urban and rural background stations in five European countries for the period between 1998 and 2010 were investigated. Collocated or nearby PM measurements and meteorological observations were used in order to construct Generalized Additive Models, which model the effect of each meteorological variable on PM concentrations. In agreement with previous findings, the most important meteorological variables affecting PM concentrations were wind speed, wind direction, boundary layer depth, precipitation, temperature and number of consecutive days with synoptic weather patterns that favor high PM concentrations. Temperature has a negative relationship to PM2.5 concentrations for low temperatures and a positive relationship for high temperatures. The stationary point of this relationship varies between 5 and 15 °C depending on the station. PMcoarse concentrations increase for increasing temperatures almost throughout the temperature range. Wind speed has a monotonic relationship to PM2.5 except for one station, which exhibits a stationary point. Considering PMcoarse, concentrations tend to increase or stabilize for large wind speeds at most stations. It was also observed that at all stations except one, higher PM2.5 concentrations occurred for east wind direction, compared to west wind direction. Meteorologically adjusted PM time series were produced by removing most of the PM variability due to meteorology. It was found that PM10 and PM2.5 concentrations decrease at most stations. The average trends of the raw and meteorologically adjusted data are −0.4 μg m−3 yr−1 for PM10 and PM2.5 size fractions. PMcoarse have much smaller trends and after averaging over all stations, no significant trend was detected at the 95% level of confidence. It is suggested that decreasing PMcoarse in addition to PM2.5 can result in a faster decrease of PM10 in the future. The trends of the 90th quantile of PM10 and PM2.5 concentrations were examined by quantile regression in order to detect long term changes in the occurrence of very large PM concentrations. The meteorologically adjusted trends of the 90th quantile were significantly larger (as an absolute value) on average over all stations (−0.6 μg m−3 yr−1).

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Barmpadimos, I., J. Keller, D. Oderbolz, C. Hueglin, and A. S. H. Prévôt. "One decade of parallel fine (PM2.5) and coarse (PM10–PM2.5) particulate matter measurements in Europe: trends and variability." Atmospheric Chemistry and Physics 12, no. 7 (2012): 3189–203. http://dx.doi.org/10.5194/acp-12-3189-2012.

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Abstract. The trends and variability of PM10, PM2.5 and PMcoarse concentrations at seven urban and rural background stations in five European countries for the period between 1998 and 2010 were investigated. Collocated or nearby PM measurements and meteorological observations were used in order to construct Generalized Additive Models, which model the effect of each meteorological variable on PM concentrations. In agreement with previous findings, the most important meteorological variables affecting PM concentrations were wind speed, wind direction, boundary layer depth, precipitation, temperature and number of consecutive days with synoptic weather patterns that favor high PM concentrations. Temperature has a negative relationship to PM2.5 concentrations for low temperatures and a positive relationship for high temperatures. The stationary point of this relationship varies between 5 and 15 °C depending on the station. PMcoarse concentrations increase for increasing temperatures almost throughout the temperature range. Wind speed has a monotonic relationship to PM2.5 except for one station, which exhibits a stationary point. Considering PMcoarse, concentrations tend to increase or stabilize for large wind speeds at most stations. It was also observed that at all stations except one, higher PM2.5 concentrations occurred for east wind direction, compared to west wind direction. Meteorologically adjusted PM time series were produced by removing most of the PM variability due to meteorology. It was found that PM10 and PM2.5 concentrations decrease at most stations. The average trends of the raw and meteorologically adjusted data are −0.4 μg m−3 yr−1 for PM10 and PM2.5 size fractions. PMcoarse have much smaller trends and after averaging over all stations, no significant trend was detected at the 95% level of confidence. It is suggested that decreasing PMcoarse in addition to PM2.5 can result in a faster decrease of PM10 in the future. The trends of the 90th quantile of PM10 and PM2.5 concentrations were examined by quantile regression in order to detect long term changes in the occurrence of very large PM concentrations. The meteorologically adjusted trends of the 90th quantile were significantly larger (as an absolute value) on average over all stations (−0.6 μg m−3 yr−1).

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Campos, Ricardo M., Carolina B. Gramcianinov, Ricardo de Camargo, and Pedro L. da Silva Dias. "Assessment and Calibration of ERA5 Severe Winds in the Atlantic Ocean Using Satellite Data." Remote Sensing 14, no. 19 (2022): 4918. http://dx.doi.org/10.3390/rs14194918.

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In this paper, we analyze the surface winds of ECMWF ERA5 reanalysis in the Atlantic Ocean. The first part addresses a reanalysis validation, studying the spatial distribution of the errors and the performance as a function of the percentiles, with a further investigation under cyclonic conditions. The second part proposes and compares two calibration models, a simple least-squares linear regression (LR) and the quantile mapping method (QM). Our results indicate that ERA5 provides high-quality winds for non-extreme conditions, especially at the eastern boundaries, with bias between −0.5 and 0.3 m/s and RMSE below 1.5 m/s. The reanalysis errors are site-dependent, where large RMSE and severe underestimation are found in tropical latitudes and locations following the warm currents. The most extreme winds in tropical cyclones show the worst results, with RMSE above 5 m/s. Apart from these areas, the strong winds at extratropical locations are well represented. The bias-correction models have proven to be very efficient in removing systematic bias. The LR works well for low-to-mild wind intensities while the QM is better for the upper percentiles and winds above 15 m/s—an improvement of 10% in RMSE and 50% for the bias compared to the original reanalysis is reported.

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Sobie, Stephen R., and Trevor Q. Murdock. "High-Resolution Statistical Downscaling in Southwestern British Columbia." Journal of Applied Meteorology and Climatology 56, no. 6 (2017): 1625–41. http://dx.doi.org/10.1175/jamc-d-16-0287.1.

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AbstractKnowledge from high-resolution daily climatological parameters is frequently sought after for increasingly local climate change assessments. This research investigates whether applying a simple postprocessing methodology to existing statistically downscaled temperature and precipitation fields can result in improved downscaled simulations useful at the local scale. Initial downscaled daily simulations of temperature and precipitation at 10-km resolution are produced using bias correction constructed analogs with quantile mapping (BCCAQ). Higher-resolution (800 m) values are then generated using the simpler climate imprint technique in conjunction with temperature and precipitation climatologies from the Parameter-Elevation Regression on Independent Slopes Model (PRISM). The potential benefit of additional downscaling to 800 m is evaluated using the “Climdex” set of 27 indices of extremes established by the Expert Team on Climate Change Detection and Indices (ETCCDI). These indices are also calculated from weather station observations recorded at 22 locations within southwestern British Columbia, Canada, to evaluate the performance of both the 10-km and 800-m datasets in replicating the observed quantities. In a 30-yr historical evaluation period, Climdex indices computed from 800-m simulated values display reduced error relative to local station observations than those from the 10-km dataset, with the greatest reduction in error occurring at high-elevation sites for precipitation-based indices.

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Nwakuya, M. T., and E. O. Biu. "Investigating Some Imputation Methods of Multivariate Imputation Chained Equations." European Journal of Mathematics and Statistics 3, no. 3 (2022): 13–20. http://dx.doi.org/10.24018/ejmath.2022.3.3.109.

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This paper investigates three MICE methods: Predictive Mean Matching (PMM), Quantile Regression-based Multiple Imputation (QR-basedMI) and Simple Random Sampling Imputation (SRSI) at imputation numbers 5, 15, 20 and 30 with 5% and 20% missing values, to ascertain the one that produces imputed values that best matches the observed values and compare the model fit based on the AIC and MSE. The results show that; QR-basedMI produced more imputed values that didn’t match the observed, SRSI produced imputed values that match the observed values better as the number of imputations increases while PMM produced imputed values that matched the observed at all number of imputations and missingness considered. The model fit results for 5% missingness showed that QR-basedMI produced the best results in terms of MSE except for M=15, while AIC results showed that PMM produced best result for M= 5, QR-basedMI produced best results for M=15 and for M=20 and 30 SRSI produced the best results. The model fit results for 20% missingness shows that PMM produced the best results at all the number of imputations considered for both AIC and MSE except the AIC at M=15 where SRSI was seen to produce the best results. It is concluded that in comparison, the PMM is most suited when missingness is 20% but for 5% missingness the model fit is best with QR-basedMI.

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Antoch, J., and P. Janssen. "Nonparametric regression M-quantiles." Statistics & Probability Letters 8, no. 4 (1989): 355–62. http://dx.doi.org/10.1016/0167-7152(89)90044-8.

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Kokic, Philip, Rohan Nelson, Holger Meinke, Andries Potgieter, and John Carter. "From rainfall to farm incomes—transforming advice for Australian drought policy. I. Development and testing of a bioeconomic modelling system." Australian Journal of Agricultural Research 58, no. 10 (2007): 993. http://dx.doi.org/10.1071/ar06193.

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In this paper we report the development of a bioeconomic modelling system, AgFIRM, designed to help close a relevance gap between climate science and policy in Australia. We do this by making a simple econometric farm income model responsive to seasonal forecasts of crop and pasture growth for the coming season. The key quantitative innovation was the use of multiple and M-quantile regression to calibrate the farm income model, using simulated crop and pasture growth from 2 agroecological models. The results of model testing demonstrated a capability to reliably forecast the direction of movement in Australian farm incomes in July at the beginning of the financial year (July–June). The structure of the model, and the seasonal climate forecasting system used, meant that its predictive accuracy was greatest across Australia’s cropping regions. In a second paper, Nelson et al. (2007, this issue), we have demonstrated how the bioeconomic modelling system developed here could be used to enhance the value of climate science to Australian drought policy.

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Rao, Leela E., John R. Matchett, Matthew L. Brooks, Robert F. Johnson, Richard A. Minnich, and Edith B. Allen. "Relationships between annual plant productivity, nitrogen deposition and fire size in low-elevation California desert scrub." International Journal of Wildland Fire 24, no. 1 (2015): 48. http://dx.doi.org/10.1071/wf13152.

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Although precipitation is correlated with fire size in desert ecosystems and is typically used as an indirect surrogate for fine fuel load, a direct link between fine fuel biomass and fire size has not been established. In addition, nitrogen (N) deposition can affect fire risk through its fertilisation effect on fine fuel production. In this study, we examine the relationships between fire size and precipitation, N deposition and biomass with emphasis on identifying biomass and N deposition thresholds associated with fire spreading across the landscape. We used a 28-year fire record of 582 burns from low-elevation desert scrub to evaluate the relationship of precipitation, N deposition and biomass with the distribution of fire sizes using quantile regression. We found that models using annual biomass have similar predictive ability to those using precipitation and N deposition at the lower to intermediate portions of the fire size distribution. No distinct biomass threshold was found, although within the 99th percentile of the distribution fire size increased with greater than 125 g m–2 of winter fine fuel production. The study did not produce an N deposition threshold, but did validate the value of 125 g m–2 of fine fuel for spread of fires.

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Koch, Julian, Helen Berger, Hans Jørgen Henriksen, and Torben Obel Sonnenborg. "Modelling of the shallow water table at high spatial resolution using random forests." Hydrology and Earth System Sciences 23, no. 11 (2019): 4603–19. http://dx.doi.org/10.5194/hess-23-4603-2019.

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Abstract. Machine learning provides great potential for modelling hydrological variables at a spatial resolution beyond the capabilities of physically based modelling. This study features an application of random forests (RF) to model the depth to the shallow water table, for a wintertime minimum event, at a 50 m resolution over a 15 000 km2 domain in Denmark. In Denmark, the shallow groundwater poses severe risks with respect to groundwater-induced flood events, affecting both urban and agricultural areas. The risk is especially critical in wintertime, when the shallow groundwater is close to terrain. In order to advance modelling capabilities of the shallow groundwater system and to provide estimates at the scales required for decision-making, this study introduces a simple method to unify RF and physically based modelling. Results from the national water resources model in Denmark (DK-model) at a 500 m resolution are employed as covariates in the RF model. Thus, RF ensures physical consistency at a coarse scale and fully exhausts high-resolution information from readily available environmental variables. The vertical distance to the nearest water body was rated as the most important covariate in the trained RF model followed by the DK-model. The evaluation test of the trained RF model was very satisfying with a mean absolute error of 76 cm and a coefficient of determination of 0.56. The resulting map underlines the severity of groundwater flooding risk in Denmark, as the average depth to the shallow groundwater is 1.9 m and approximately 29 % of the area is characterized as having a depth of less than 1 m during a typical wintertime minimum event. This study brings forward a novel method for assessing the spatial patterns of covariate importance of the RF predictions that contributes to an increased interpretability of the RF model. Quantifying the uncertainty of RF models is still rare for hydrological applications. Two approaches, namely random forests regression kriging (RFRK) and quantile regression forests (QRF), were tested to estimate uncertainties related to the predicted groundwater levels.

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Li, Yuwei, Liming Song, Tom Nishida, and Panfeng Gao. "Development of integrated habitat indices for bigeye tuna, Thunnus obesus, in waters near Palau." Marine and Freshwater Research 63, no. 12 (2012): 1244. http://dx.doi.org/10.1071/mf12072.

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A survey was conducted in waters near Palau in 2005, to improve our understanding of the relationship between environmental variables and the spatial distributions of Thunnus obesus. Catch rates and environmental variables (water temperature, salinity and dissolved oxygen) at six depth strata between 40 and 280 m were collected at 77 sampling stations in the survey. Models were developed to estimate an integrated habitat index (IHI) for T. obesus on the basis of quantile regression. The findings of the present study were as follows: (1) the performance of IHI models in predicting habitat utilisation by T. obesus was good, (2) the impacts of the weighted average temperature and dissolved oxygen were significant on the spatial distribution of T. obesus, (3) the influence of the environmental variables on T. obesus distribution differed among different depth strata, (4) the present study provides an effective approach to predict the spatial distribution of the pelagic fishes caught by longline and (5) the weighted average temperature and dissolved oxygen should be included in the T. obesus catch per unit effort (CPUE) standardisations.

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Benáček, Patrik, Aleš Farda, and Petr Štěpánek. "Postprocessing of Ensemble Weather Forecast Using Decision Tree–Based Probabilistic Forecasting Methods." Weather and Forecasting 38, no. 1 (2023): 69–82. http://dx.doi.org/10.1175/waf-d-22-0006.1.

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Abstract Producing an accurate and calibrated probabilistic forecast has high social and economic value. Systematic errors or biases in the ensemble weather forecast can be corrected by postprocessing models whose development is an urgent challenge. Traditionally, the bias correction is done by employing linear regression models that estimate the conditional probability distribution of the forecast. Although this model framework works well, it is restricted to a prespecified model form that often relies on a limited set of predictors only. Most machine learning (ML) methods can tackle these problems with a point prediction, but only a few of them can be applied effectively in a probabilistic manner. The tree-based ML techniques, namely, natural gradient boosting (NGB), quantile random forests (QRF), and distributional regression forests (DRF), are used to adjust hourly 2-m temperature ensemble prediction at lead times of 1–10 days. The ensemble model output statistics (EMOS) and its boosting version are used as benchmark models. The model forecast is based on the European Centre for Medium-Range Weather Forecasts (ECMWF) for the Czech Republic domain. Two training periods 2015–18 and 2018 only were used to learn the models, and their prediction skill was evaluated in 2019. The results show that the QRF and NGB methods provide the best performance for 1–2-day forecasts, while the EMOS method outperforms other methods for 8–10-day forecasts. Key components to improving short-term forecasting are additional atmospheric/surface state predictors and the 4-yr training sample size. Significance Statement Machine learning methods have great potential and are beginning to be widely applied in meteorology in recent years. A new technique called natural gradient boosting (NGB) has been released and used in this paper to refine the probabilistic forecast of surface temperature. It was found that the NGB has better prediction skills than the traditional ensemble model output statistics in forecasting 1 and 2 days in advance. The NGB has similar prediction skills with lower computational demands compared to other advanced machine learning methods such as the quantile random forests. We showed a path to employ the NGB method in this task, which can be followed for refining other and more challenging meteorological variables such as wind speed or precipitation.

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Zhang, Keqi, Daniel Gann, Michael Ross, Himadri Biswas, Yuepeng Li, and Jamie Rhome. "Comparison of TanDEM-X DEM with LiDAR Data for Accuracy Assessment in a Coastal Urban Area." Remote Sensing 11, no. 7 (2019): 876. http://dx.doi.org/10.3390/rs11070876.

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The TanDEM-X (TDX) mission launched by the German Aerospace Center delivers unprecedented global coverage of a high-quality digital elevation model (DEM) with a pixel spacing of 12 m. To examine the relationships of terrain, vegetation, and building elevations with hydrologic, geologic, geomorphologic, or ecologic factors, quantification of TDX DEM errors at a local scale is necessary. We estimated the errors of TDX data for open ground, forested, and built areas in a coastal urban environment by comparing the TDX DEM with LiDAR data for the same areas, using a series of error measures including root mean square error (RMSE) and absolute deviation at the 90% quantile (LE90). RMSE and LE90 values were 0.49 m and 0.79 m, respectively, for open ground. These values, which are much lower than the 10 m LE90 specified for the TDX DEM, highlight the promise of TDX DEM data for mapping hydrologic and geomorphic features in coastal areas. The RMSE/LE90 values for mangrove forest, tropical hardwood hammock forest, pine forest, dense residential, sparse residential, and downtown areas were 1.15/1.75, 2.28/3.37, 3.16/5.00, 1.89/2.90, 2.62/4.29 and 35.70/51.67 m, respectively. Regression analysis indicated that variation in canopy height of densely forested mangrove and hardwood hammock was well represented by the TDX DEM. Thus, TDX DEM data can be used to estimate tree height in densely vegetated forest on nearly flat topography next to the shoreline. TDX DEM errors for pine forest and residential areas were larger because of multiple reflection and shadow effects. Furthermore, the TDX DEM failed to capture the many high-rise buildings in downtown, resulting in the lowest accuracy among the different land cover types. Therefore, caution should be exercised in using TDX DEM data to reconstruct building models in a highly developed metropolitan area with many tall buildings separated by narrow open spaces.

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Trojanek, Radoslaw, Michal Gluszak, and Justyna Tanas. "THE EFFECT OF URBAN GREEN SPACES ON HOUSE PRICES IN WARSAW." International Journal of Strategic Property Management 22, no. 5 (2018): 358–71. http://dx.doi.org/10.3846/ijspm.2018.5220.

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In the paper, we analysed the impact of proximity to urban green areas on apartment prices in Warsaw. The data-set contained in 43 075 geo-coded apartment transactions for the years 2010 to 2015. In this research, the hedonic method was used in Ordinary Least Squares (OLS), Weighted Least Squares (WLS) and Median Quantile Regression (Median QR) models. We found substantial evidence that proximity to an urban green area is positively linked with apartment prices. On an average presence of a green area within 100 meters from an apartment increases the price of a dwelling by 2,8% to 3,1%. The effect of park/forest proximity on house prices is more significant for newer apartments than those built before 1989. We found that proximity to a park or a forest is particularly important (and has a higher implicit price as a result) in the case of buildings constructed after 1989. The impact of an urban green was particularly high in the case of a post-transformation housing estate. Close vicinity (less than 100 m distance) to an urban green increased the sales prices of apartments in new residential buildings by 8,0–8,6%, depending on a model.

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Carayanni, Vilelmine, Gregory C. Bogdanis, Elpis Vlachopapadopoulou, et al. "Predicting VO2max in Children and Adolescents Aged between 6 and 17 Using Physiological Characteristics and Participation in Sport Activities: A Cross-Sectional Study Comparing Different Regression Models Stratified by Gender." Children 9, no. 12 (2022): 1935. http://dx.doi.org/10.3390/children9121935.

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Background: The aim of this study is to use different regression models to capture the association between cardiorespiratory fitness VO2max (measured in mL/kg/min) and somatometric characteristics and sports activities and making better predictions. Methods: multiple linear regression (MLR), quantile regression (QR), ridge regression (RR), support vector regression (SVR) with three different kernels, artificial neural networks (ANNs), and boosted regression trees (RTs) were compared to explain and predict VO2max and to choose the best performance model. The sample consisted of 4908 children (2314 males and 2594 females) aged between 6 and 17. Cardiorespiratory fitness was assessed by the 20 m maximal multistage shuttle run test and maximal oxygen uptake (VO2max) was calculated. Welch t-tests, Mann–Whitney-U tests, X2 tests, and ANOVA tests were performed. The performance measures were root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). All analyses were stratified by gender. Results: A comparison of the statistical indices for both the predicted and actual data indicated that in boys, the MLR model outperformed all other models in all indices, followed by the linear SVR model. In girls, the MLR model performed better than the other models in R2 but was outperformed by SVR-RBF in terms of RMSE and MAE. The overweight and obesity categories in both sexes (p < 0.001) and maternal prepregnancy obesity in girls had a significant negative effect on VO2max. Age, weekly football training, track and field, basketball, and swimming had different positive effects based on gender. Conclusion: The MLR model showed remarkable performance against all other models and was competitive with the SVR models. In addition, this study’s data showed that changes in cardiorespiratory fitness were dependent, to a different extent based on gender, on BMI category, weight, height, age, and participation in some organized sports activities. Predictors that are not considered modifiable, such as gender, can be used to guide targeted interventions and policies.

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Journal articles on the topic 'M-quantile regression'

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