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Quantile regression for climate data.

Marasinghe, Dilhani Shalika.

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Quantile regression for climate data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Quantile regression for climate data./
作者:	Marasinghe, Dilhani Shalika.
面頁冊數:	62 p.
附註:	Source: Masters Abstracts International, Volume: 53-06.
Contained By:	Masters Abstracts International53-06(E).
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1564897
ISBN:	9781321187410

Quantile regression for climate data.
Marasinghe, Dilhani Shalika.

Quantile regression for climate data. - 62 p.

Source: Masters Abstracts International, Volume: 53-06.

Thesis (M.S.)--Clemson University, 2014.

This item must not be sold to any third party vendors.

Quantile regression is a developing statistical tool which is used to explain the relationship between response and predictor variables. This thesis describes two examples of climatology using quantile regression.Our main goal is to estimate derivatives of a conditional mean and/or conditional quantile function. We introduce a method to handle autocorrelation in the framework of quantile regression and used it with the temperature data. Also we explain some properties of the tornado data which is non-normally distributed. Even though quantile regression provides a more comprehensive view, when talking about residuals with the normality and the constant variance assumption, we would prefer least square regression for our temperature analysis. When dealing with the non-normality and non constant variance assumption, quantile regression is a better candidate for the estimation of the derivative.

ISBN: 9781321187410Subjects--Topical Terms:

517247
Statistics.

Quantile regression for climate data.
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502 $a Thesis (M.S.)--Clemson University, 2014.
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506 $a This item must not be added to any third party search indexes.
520 $a Quantile regression is a developing statistical tool which is used to explain the relationship between response and predictor variables. This thesis describes two examples of climatology using quantile regression.Our main goal is to estimate derivatives of a conditional mean and/or conditional quantile function. We introduce a method to handle autocorrelation in the framework of quantile regression and used it with the temperature data. Also we explain some properties of the tornado data which is non-normally distributed. Even though quantile regression provides a more comprehensive view, when talking about residuals with the normality and the constant variance assumption, we would prefer least square regression for our temperature analysis. When dealing with the non-normality and non constant variance assumption, quantile regression is a better candidate for the estimation of the derivative.
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650 4 $a Atmospheric sciences. $3 3168354
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