Beyond the mean: Quantile regression to differentiate the distributional effects of ambient PM2.5 constituents on sperm quality among men

A growing body of research has examined the determinants of CO2 emissions. This literature has used mean-based regression methods in which only the mean effects of covariates are estimated. In this paper, we use the quantile regression methodology for a panel of 45 countries to investigate whether or not the factors that drive pollution do so in the same way for high and low pollution countries. The Environmental Kuznets Curve is confirmed and the positive effect of economic development is larger in low pollution countries. Energy consumption and financial development increase CO2 emissions and their effects are larger in countries with lower levels of pollution. Industrialization increases pollution especially in countries with higher level of pollution. Openness to trade and urbanization are negatively related to emissions in low pollution countries. All these findings suggest that pollution control policies should be tailored differently across low and high pollution countries.

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Minimum Covariance Determinant-Based Quantile Robust Regression-Type Estimators for Mean Parameter

Mathematical Problems in Engineering ◽

10.1155/2021/5255839 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Usman Shahzad ◽

Nadia H. Al-Noor ◽

Noureen Afshan ◽

David Anekeya Alilah ◽

Muhammad Hanif ◽

...

Keyword(s):

Quantile Regression ◽

Robust Regression ◽

Real Life ◽

Minimum Covariance Determinant ◽

Mean Squared Errors ◽

Regression Estimators ◽

Chi Squared ◽

Class Of Estimators ◽

The Mean ◽

Ratio Estimators

Robust regression tools are commonly used to develop regression-type ratio estimators with traditional measures of location whenever data are contaminated with outliers. Recently, the researchers extended this idea and developed regression-type ratio estimators through robust minimum covariance determinant (MCD) estimation. In this study, the quantile regression with MCD-based measures of location is utilized and a class of quantile regression-type mean estimators is proposed. The mean squared errors (MSEs) of the proposed estimators are also obtained. The proposed estimators are compared with the reviewed class of estimators through a simulation study. We also incorporated two real-life applications. To assess the presence of outliers in these real-life applications, the Dixon chi-squared test is used. It is found that the quantile regression estimators are performing better as compared to some existing estimators.

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Inference for Distributional Effects using Instrumental Quantile Regression

SSRN Electronic Journal ◽

10.2139/ssrn.313426 ◽

2002 ◽

Author(s):

Victor Chernozhukov ◽

Christian Hansen

Keyword(s):

Quantile Regression ◽

Distributional Effects

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A New Quantile Regression Model and Its Diagnostic Analytics for a Weibull Distributed Response with Applications

Mathematics ◽

10.3390/math9212768 ◽

2021 ◽

Vol 9 (21) ◽

pp. 2768

Author(s):

Luis Sánchez ◽

Víctor Leiva ◽

Helton Saulo ◽

Carolina Marchant ◽

José M. Sarabia

Keyword(s):

Maximum Likelihood ◽

Weibull Distribution ◽

Quantile Regression ◽

Regression Model ◽

Likelihood Method ◽

Model Parameters ◽

Distributed Data ◽

Quantile Regression Model ◽

The Mean ◽

Asymmetrically Distributed

Standard regression models focus on the mean response based on covariates. Quantile regression describes the quantile for a response conditioned to values of covariates. The relevance of quantile regression is even greater when the response follows an asymmetrical distribution. This relevance is because the mean is not a good centrality measure to resume asymmetrically distributed data. In such a scenario, the median is a better measure of the central tendency. Quantile regression, which includes median modeling, is a better alternative to describe asymmetrically distributed data. The Weibull distribution is asymmetrical, has positive support, and has been extensively studied. In this work, we propose a new approach to quantile regression based on the Weibull distribution parameterized by its quantiles. We estimate the model parameters using the maximum likelihood method, discuss their asymptotic properties, and develop hypothesis tests. Two types of residuals are presented to evaluate the model fitting to data. We conduct Monte Carlo simulations to assess the performance of the maximum likelihood estimators and residuals. Local influence techniques are also derived to analyze the impact of perturbations on the estimated parameters, allowing us to detect potentially influential observations. We apply the obtained results to a real-world data set to show how helpful this type of quantile regression model is.

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24 RELATIONSHIP BETWEEN BOAR SPERM TRAITS AND FERTILITY RATE FOLLOWING ARTIFICIAL INSEMINATION UNDER SMALLHOLDER PRODUCTION SYSTEMS

Reproduction Fertility and Development ◽

10.1071/rdv29n1ab24 ◽

2017 ◽

Vol 29 (1) ◽

pp. 119

Author(s):

M. B. Matabane ◽

P. Nethenzheni ◽

R. Thomas ◽

D. Norris ◽

K. Nephawe ◽

...

Keyword(s):

Sperm Motility ◽

Production Systems ◽

Sperm Quality ◽

Conception Rate ◽

Mean Values ◽

The Mean ◽

The Relationship ◽

Fertility Traits ◽

Sperm Traits ◽

Motility Rate

The prediction of sperm fertility has a great economic importance to the pig breeding industry. The objective of the study was to determine the relationship between boar sperm quality and fertility following artificial insemination (AI) under smallholder production systems. A total of 18 ejaculates were collected from 3 breeding boars using a hand-gloved technique. Aliquots of diluted semen were assessed for sperm motility using a computer aided sperm analysis before AI. Sperm viability was evaluated using Synthetic Binding CD-14 (SYBR-14+)/propidium iodide (PI–), whereas sperm morphology was evaluated using Eosin Nigrosin staining. Fluorescent microscope was used at 100× magnification to count 200 sperm per slide. The semen was extended with Beltsville Thawing Solution and contained 3 × 109 sperm/dose. A total of 73 multiparous sows were inseminated twice. Fertility was measured by conception rate, farrowing rate, litter size and number of piglets born alive following AI. Sperm quality and fertility data were analysed using one-way ANOVA. Spearman’s rank correlation was used to determine the relationship between sperm quality and fertility traits. The mean values for total sperm motility ranged from 93.5 to 96.8%. Progressive and rapid sperm motility differed significantly (P < 0.05) among the boars. However, no significant differences were found for sperm velocity traits. The mean values for morphologically normal sperm ranged from 47.8 to 60.9% and live sperm ranged from 71.8 to 77.2%, but did not differ significantly among the boars (P > 0.05). Conception rate from different boars varied (P < 0.05) from 63.6 to 93.3%. Of all fertility traits studied, conception rate was significantly related to total sperm motility rate (r = 0.34, P < 0.0029), progressive motility (r = 0.29, P < 0.0141) and rapid motility (r = 0.34, P < 0.0032), although relatively low. There was a low positive relationship between morphologically normal sperm and fertility traits (P > 0.05). In conclusion, total, progressive, and rapid sperm motility rate were the only sperm traits significantly related to conception rate. Conversely, litter size and number born alive were not correlated with sperm motility, viability, or morphology traits.

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Designing for the Extremes: Modeling Drivers’ Response Time to Take Back Control From Automation Using Bayesian Quantile Regression

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/0018720819893429 ◽

2019 ◽

pp. 001872081989342 ◽

Cited By ~ 1

Author(s):

Azadeh DinparastDjadid ◽

John D. Lee ◽

Joshua Domeyer ◽

Chris Schwarz ◽

Timothy L. Brown ◽

...

Keyword(s):

Response Time ◽

Quantile Regression ◽

Driving Simulator ◽

Time Budget ◽

Response Time Distribution ◽

Event Type ◽

Actual System ◽

Bayesian Quantile Regression ◽

Mean Response Time ◽

The Mean

Objective Understanding the factors that affect drivers’ response time in takeover from automation can help guide the design of vehicle systems to aid drivers. Higher quantiles of the response time distribution might indicate a higher risk of an unsuccessful takeover. Therefore, assessments of these systems should consider upper quantiles rather than focusing on the central tendency. Background Drivers’ responses to takeover requests can be assessed using the time it takes the driver to take over control. However, all the takeover timing studies that we could find focused on the mean response time. Method A study using an advanced driving simulator evaluated the effect of takeover request timing, event type at the onset of a takeover, and visual demand on drivers’ response time. A mixed effects model was fit to the data using Bayesian quantile regression. Results Takeover request timing, event type that precipitated the takeover, and the visual demand all affect driver response time. These factors affected the 85th percentile differently than the median. This was most evident in the revealed stopped vehicle event and conditions with a longer time budget and scenes with lower visual demand. Conclusion Because the factors affect the quantiles of the distribution differently, a focus on the mean response can misrepresent actual system performance. The 85th percentile is an important performance metric because it reveals factors that contribute to delayed responses and potentially dangerous outcomes, and it also indicates how well the system accommodates differences between drivers.

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