Quantile Regression in R: A Vignette

Koenker, Roger

doi:10.1017/cbo9780511754098.011

Cited by 918 publications

(1,270 citation statements)

References 30 publications

Supporting

Mentioning

1,236

Contrasting

Unclassified

Order By: Relevance

“…, 98%, 100% quantiles. They are estimated using local quantile regression [13], where the weighting is local in time. They clearly show that the distribution of the measurements is heavily skewed, as only two percent of the values are between the two upper lines, which cover more than half of the range.…”

Section: Heat Load Measurementsmentioning

confidence: 99%

Short-term heat load forecasting for single family houses

Bacher

Madsen

Nielsen³

et al. 2013

Energy and Buildings

100

View full text Add to dashboard Cite

This paper presents a method for forecasting the load for space heating in a single-family house. The forecasting model is built using data from sixteen houses located in Sønderborg, Denmark, combined with local climate measurements and weather forecasts. Every hour the hourly heat load for each house the following two days is forecasted. The forecast models are adaptive linear time-series models and the climate inputs used are: ambient temperature, global radiation and wind speed. A computationally efficient recursive least squares scheme is used. The models are optimized to fit the individual characteristics for each house, such as the level of adaptivity and the thermal dynamical response of the building, which is modeled with simple transfer functions. Identification of a model, which is suitable for all the houses, is carried out. The results show that the one-step ahead errors are close to white noise and that practically all correlation to the climate variables are removed. Furthermore, the results show that the forecasting errors mainly are related to: unpredictable high frequency variations in the heat load signal (predominant only for some houses), shifts in resident behavior patterns and uncertainty of the weather forecasts for longer horizons, especially for solar radiation.

show abstract

Section: Heat Load Measurementsmentioning

confidence: 99%

Short-term heat load forecasting for single family houses

Bacher

Madsen

Nielsen³

et al. 2013

Energy and Buildings

100

View full text Add to dashboard Cite

show abstract

“…We expect a similar sign across quantiles but all Figure 1. Coefficients for each explanatory variable for each quantile explanatory variables will not necessarily be significant across quantiles because different drivers affect the WTP at different quantiles (Koenker, 2005;O'Garra and Mourato, 2006). To the best of our knowledge, the literature does not provide any discussion about the pattern associated with the impact of explanatory variables on WTP across quantiles.…”

Section: Resultsmentioning

confidence: 99%

A Bayesian quantile binary regression approach to estimate payments for environmental services

2016

View full text Add to dashboard Cite

ABSTRACT. Stated preference approaches, such as contingent valuation, focus mainly on the estimation of the mean or median willingness to pay (WTP) for an environmental good. Nevertheless, these two welfare measures may not be appropriate when there are social and political concerns associated with implementing a payment for environmental services (PES) scheme. In this paper the authors used a Bayesian estimation approach to estimate a quantile binary regression and the WTP distribution in the context of a contingent valuation PES application. Our results show that the use of other quantiles framed in the supermajority concept provides a reasonable interpretation of the technical nonmarket valuation studies in the PES area. We found that the values of the mean WTP are 10-37 times higher than the value that would support a supermajority of 70 per cent of the population.

show abstract

“…A comprehensive bibliography of uncertainty quantification techniques is available in this reference, and a review of these works is not reiterated here. 16 describes an implementation of the QR methods in the R language and offers a brief introduction and tutorial use of this software package.…”

Section: Uncertainty Quantificationmentioning

confidence: 99%

“…24 Additionally, when î(x i , â) is formulated as a linear function of parameters, the resulting minimisation problem can be solved very efficiently by linear programming methods. For instance, the R language quantile regression package developed by Koenker 16 relies upon an interior point (FrischNewton) method to solve the linear programming construct of the quantile regression problem.…”

Section: Quantile Regressionmentioning

confidence: 99%

“…Another item of paramount importance is the implementation of QR methods in R. Koenker 16 provides a QR library package for R, so no coding of the QR routines was required of the authors. In addition, the functionality of Koenker's QR implementation in R has been previously verified to work properly and validated against real-world test cases.…”

Section: Analysis Toolsmentioning

confidence: 99%

See 1 more Smart Citation

The application of a quantile regression metamodel for salinity event detection confirmation within New York Harbour oceanographic data

Kerman

Jiang

Blumberg

et al. 2009

Journal of Operational Oceanography

View full text Add to dashboard Cite

This paper presents a continuation of research regarding the utilisation of robust metamodels for uncertainty quantification and event detection within a geophysical system. Using salinity data supplied by the New York Harbour Observing and Prediction System (NYHOPS) for two test datasets and three actual sensors, event detection results from a static threshold method are compared against those of a dynamic uncertainty quantification-based technique and a composite technique that combines both the static and dynamic methods. The results clearly show an appreciable reduction in the number of false positive detections when using the composite event detection method; in test data void of salinity events, false detection rates for low salinity conditions decreased by as much as 80%.

show abstract

Quantile Regression in R: A Vignette

Cited by 918 publications

References 30 publications

Short-term heat load forecasting for single family houses

Short-term heat load forecasting for single family houses

A Bayesian quantile binary regression approach to estimate payments for environmental services

The application of a quantile regression metamodel for salinity event detection confirmation within New York Harbour oceanographic data

Contact Info

Product

Resources

About