Locally Weighted Least Squares Kernel Regression and Statistical Evaluation of Lidar Measurements

Holst, Ulla; Hössjer, Ola; Björklund, Claes; Ragnarson, P; Edner, Hans

doi:10.1002/(sici)1099-095x(199607)7:4<401::aid-env221>3.0.co;2-d

Cited by 52 publications

(23 citation statements)

References 13 publications

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“…DMRR seeks to utilize as much of the researcher's parametric knowledge as possible while still allowing for specific deviations in the data to be captured. Holst et al (1996) discuss the use of local polynomial regression for evaluation of the concentration of atmospheric atomic mercury measured with LIDAR (see Sigrist, 1994). The data are plotted in Fig.…”

Section: Dmrr Algorithmmentioning

confidence: 99%

A semi-parametric approach to dual modeling when no replication exists

Robinson¹,

Birch²,

Starnes³

2010

Journal of Statistical Planning and Inference

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Section: Dmrr Algorithmmentioning

confidence: 99%

A semi-parametric approach to dual modeling when no replication exists

Robinson¹,

Birch²,

Starnes³

2010

Journal of Statistical Planning and Inference

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“…In this section we illustrate how to perform a nonparametric regression analysis on the LIDAR data (Holst, Hössjer, Björklund, Ragnarson, and Edner 1996) and a two dimensional toy example with StatLSSVM in MATLAB.…”

Section: Standard Nonparametric Regressionmentioning

confidence: 99%

Nonparametric Regression viaStatLSSVM

Brabanter¹,

Suykens²,

Moor³

2013

J. Stat. Soft.

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We present a new MATLAB toolbox under Windows and Linux for nonparametric regression estimation based on the statistical library for least squares support vector machines (StatLSSVM). The StatLSSVM toolbox is written so that only a few lines of code are necessary in order to perform standard nonparametric regression, regression with correlated errors and robust regression. In addition, construction of additive models and pointwise or uniform confidence intervals are also supported. A number of tuning criteria such as classical cross-validation, robust cross-validation and cross-validation for correlated errors are available. Also, minimization of the previous criteria is available without any user interaction.

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“…The predictor is the distance travelled before the light is reflected back to its source (range). The original data comes from Holst et al (1996) and has been analyzed by for example Ruppert et al (2003) and Leslie et al (2007). Our aim is to model the predictive density p(logratio | range).…”

Section: Feng LI Mattias Villani and Robert Kohnmentioning

confidence: 99%

Modelling Conditional Densities Using Finite Smooth Mixtures

Villani

Kohn

2011

Mixtures

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ABSTRACT. Smooth mixtures, i.e. mixture models with covariate-dependent mixing weights, are very useful flexible models for conditional densities. Previous work shows that using too simple mixture components for modeling heteroscedastic and/or heavy tailed data can give a poor fit, even with a large number of components. This paper explores how well a smooth mixture of symmetric components can capture skewed data. Simulations and applications on real data show that including covariate-dependent skewness in the components can lead to substantially improved performance on skewed data, often using a much smaller number of components. Furthermore, variable selection is effective in removing unnecessary covariates in the skewness, which means that there is little loss in allowing for skewness in the components when the data are actually symmetric. We also introduce smooth mixtures of gamma and log-normal components to model positively-valued response variables.

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Locally Weighted Least Squares Kernel Regression and Statistical Evaluation of Lidar Measurements

Cited by 52 publications

References 13 publications

A semi-parametric approach to dual modeling when no replication exists

A semi-parametric approach to dual modeling when no replication exists

Nonparametric Regression viaStatLSSVM

Modelling Conditional Densities Using Finite Smooth Mixtures

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