Linear regression

Su, Xiaogang; Yan, Xin; Tsai, Chih‐Ling

doi:10.1002/wics.1198

Cited by 286 publications

(124 citation statements)

References 100 publications

(56 reference statements)

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“…This experiment is intended to carry out a comparison between three time-series algorithms used to forecast the filllevel for all containers. The algorithms used in this comparison are: Linear Regression (Su et al, 2012), Gaussian processes (Mackay, 1998) and Support Vector Machine for regression (SMOreg) (Rivas-Perea, 2013). We briefly describe the techniques on which the algorithms are based:…”

Section: Experiments 1: Comparison Of Time Series Algorithmsmentioning

confidence: 99%

BIN-CT: Urban waste collection based on predicting the container fill level

Ferrer

Alba

2019

Biosystems

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The fast demographic growth, together with the population concentration in cities and the increasing amount of daily waste, are factors that are pushing to the limit the ability of waste assimilation by Nature. Therefore, we need technological means to optimally manage of the waste collection process, which represents 70% of the operational cost in waste treatment. In this article, we present a free intelligent software system called BIN-CT (BIN for the CiTy), based on computational learning algorithms, which plans the best routes for waste collection supported by past (historical) and future (predictions) data. The objective of the system is to reduction the cost of the waste collection service minimizing the distance traveled by a truck to collect the waste from a container, thereby reducing the fuel consumption. At the same time the quality of service for the citizen is increased, avoiding the annoying overflows of containers thanks to the accurate fill-level predictions given by BIN-CT. In this article we show the features of our software system, illustrating its operation with a real case study of a Spanish city. We conclude that the use of BIN-CT avoids unnecessary trips to containers, reduces the distance traveled to collect a container by 20%, and generates routes 33.2% shorter than the routes used by the company. Therefore it enables a considerable reduction of total costs and harmful emissions thrown up into the atmosphere.

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Section: Experiments 1: Comparison Of Time Series Algorithmsmentioning

confidence: 99%

BIN-CT: Urban waste collection based on predicting the container fill level

Ferrer

Alba

2019

Biosystems

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“…Model form was expressed as in eqn.2 (see below -Peng & Lu 2012, Su et al 2012. The autocorrelation was addressed using three residual autocorrelation structures: a first-order autoregressive structure [AR (1) …”

Section: Model Buildingmentioning

confidence: 99%

Compatible taper-volume models of Quercus variabilis Blume forests in north China

Zheng¹,

Wang²,

Jia³

et al. 2017

iForest

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Compatible taper and volume models were created for Quercus variabilis Blume (cork oak) forests in North China. 174 trees were felled to obtain stem analysis data. Linear mixed effects analyses were used in modelling. Firstly, a bark thickness model was built. Then diameter at breast height over bark (DBHob) for the inner layers of the 174 trees could be calculated, based on which a total volume model was built. The estimated volume and a specific parameter restriction were then substituted into a polynomial taper model, finally the taper model was fitted and compatible taper and volume models were obtained. Four sets of models based on different data sets were separately built and compared through coefficients of determination (R 2 ), root mean square error (RMSE), value of Akaike's information criterion (AIC), residuals plots and histograms of residuals. Models based on data of the analyzed stems without ramicorns and simultaneously with relative diameter under 1.5 were chosen as the most precise. Further testing of the chosen models using the jackknife method for the bark thickness and total volume models and a validation data set for the taper model verified that those models can be used to predict bark thickness, diameter at a specific point along the stem, merchantable volume and total stem volume of cork oak forests in North China within specific tree diameter at breast height and height ranges.

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“…Or in other words we can say that examines the changes occurred in estimates for ̂ when some cases are deleted. This is the basic idea in influence analysis as introduced by Cook [3].…”

Section: Cook's Distancementioning

confidence: 99%

“…The diagonal is the standardized measure of the distance of the i th observation from the centre (or centroid) of the X-space [6]. Generally ⁄ with an average value ̅̅̅ ( ) ⁄ ( ) ⁄ and the data points with ( ) may be regarded as outliers in X-space [3]. Larger the value of , smaller is the ( ).…”

Section: Hat Matrix Diagonals As a Measure Of Influencementioning

confidence: 99%

Observations and Cutoffs of Different Influence Measures in Multiple Linear Regression

Kr.Das¹

2015

IJCTS

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Abstract:The analysis of data for outliers is a part of model building and data summarizing for model testing, parameter estimation, prediction and peculiarity investigations. Any influential point can disproportionately pull the ordinary least squares line and distort the predictions. Thus the detection of outlying observations is very essential in the course of model building in various disciplines, such as, medical research, economics, sociology, computer science, etc. A point is an influential one if it causes dramatic change in the model after its deletion. Each of the available test statistics has different cutoff values that indicate the amount of outlyingness. Sometimes only one statistic is sufficient to provide the information about influential points but often it is necessary to examine the cutoff of more than one influence measure. The reason behind is that all the cutoff values are either a function of the sample size or number of predictors or both. Also validity of the cutoff value is subjected to some additional conditions. In this paper we try to critically examine those conditions with the help of simulation study. We shall use a few combinations of ( ) , where is the sample size and is the no. of outliers for assessing the performances.

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Linear regression

Cited by 286 publications

References 100 publications

BIN-CT: Urban waste collection based on predicting the container fill level

BIN-CT: Urban waste collection based on predicting the container fill level

Compatible taper-volume models of Quercus variabilis Blume forests in north China

Observations and Cutoffs of Different Influence Measures in Multiple Linear Regression

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