A comparison of two nonparametric estimation schemes: MARS and neural networks

Veaux, Richard D. De; Psichogios, Dimitris C.; Ungar, Lyle H.

doi:10.1016/0098-1354(93)80066-v

Cited by 115 publications

(57 citation statements)

References 16 publications

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“…Gamma regression was generally lower in Class II hits than MARS models, except for This study basically aims to proposed models being able to detect concentrations above the threshold of 240 µg/m 3 that mandate periods of epidemiological alert in Santiago de Chile; this consideration would place the MARS modeling as a tool statistically more powerful than Gamma modeling. This last point is consistent with previous findings showing that M A R S i s m o r e e f f i c i e n t than other techniques 4,23,27 . This could be explained by the smoothing approximation that uses this methodology generating breakdowns in the predictors time series and locally adjusting the basis functions in function on such nodes.…”

Section: Discussionsupporting

confidence: 82%

Critical Episodes of PM10 Particulate Matter Pollution in Santiago of Chile, an Approximation Using Two Prediction Methods: MARS Models and Gamma Models

Alvarado¹,

Silva²,

Cáceres³

2012

Air Pollution - Monitoring, Modelling and Health

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Section: Discussionsupporting

confidence: 82%

Critical Episodes of PM10 Particulate Matter Pollution in Santiago of Chile, an Approximation Using Two Prediction Methods: MARS Models and Gamma Models

Alvarado¹,

Silva²,

Cáceres³

2012

Air Pollution - Monitoring, Modelling and Health

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“…On problems with a reasonably small number of predictors and when order interactions between them is not larger than 3 (i.e. the regression may have the term x i , x i *x j , and x i *x j *x k , where x i denotes i−th predictor), MARS competes very favorably with nonlinear models, such as artificial neural networks (de Veaux et al, 1993b). The authors suggested that MARS could be used instead of neural nets in a wide variety of applications because MARS was always much faster and more interpretable than a neural net and was often more accurate as well.…”

Section: Pure Statistical Model -Marsmentioning

confidence: 99%

“…It has been applied in a wide range of disciplines (e.g. de Veaux et al, 1993a;Taliani et al, 1996;and Finizio and Palmieri, 1998;Krzyścin, 2003). On problems with a reasonably small number of predictors and when order interactions between them is not larger than 3 (i.e.…”

Section: Pure Statistical Model -Marsmentioning

confidence: 99%

Long-term variations of the UV-B radiation over Central Europe as derived from the reconstructed UV time series

2004

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Abstract. The daily doses of the erythemally weighted UV radiation are reconstructed for three sites in Central Europe: Belsk-Poland (1966-2001, Hradec Kralove-Czech Republic (1964-2001, and Tõravere-Estonia (1967-2001 to discuss the UV climatology and the long-term changes of the UV-B radiation since the mid 1960s. Various reconstruction models are examined: a purely statistical model based on the Multivariate Adaptive Regression Splines (MARS) methodology, and a hybrid model combining radiative transfer model calculations with empirical estimates of the cloud effects on the UV radiation. Modeled long-term variations of the surface UV doses appear to be in a reasonable agreement with the observed ones. A simple quality control procedure is proposed to check the homogeneity of the biometer and pyranometer data. The models are verified using the results of UV observations carried out at Belsk since 1976. MARS provides the best estimates of the UV doses, giving a mean difference between the modeled and observed monthly means equal to 0.6±2.5%. The basic findings are: similar climatological forcing by clouds for all considered stations (∼30% reduction in the surface UV), long-term variations in UV monthly doses having the same temporal pattern for all stations with extreme low monthly values (∼5% below overall mean level) at the end of the 1970s and extreme high monthly values (∼5% above overall mean level) in the mid 1990s, regional peculiarities in the cloud long-term forcing sometimes leading to extended periods with elevated UV doses, recent stabilization of the ozone induced UV long-term changes being a response to a trendless tendency of total ozone since the mid 1990s. In the case of the slowdown of the total ozone trend over Northern Hemisphere mid-latitudes it seems that clouds will appear as the most important modulator of the UV radiation both in long-and short-time scales over next decades.

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“…DeVeaux et al (1993) and DeVeaux (1995) provide more general discussions comparing these techniques. All modelling and analyses were conducted in S-PLUS.…”

Section: Modellingmentioning

confidence: 99%

Comparing five modelling techniques for predicting forest characteristics

Moisen

Frescino

2002

Ecological Modelling

375

244

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Broad-scale maps of forest characteristics are needed throughout the United States for a wide variety of forest land management applications. Inexpensive maps can be produced by modelling forest class and structure variables collected in nationwide forest inventories as functions of satellite-based information. But little work has been directed at comparing modelling techniques to determine which tools are best suited to mapping tasks given multiple objectives and logistical constraints. Consequently, five modelling techniques were compared for mapping forest characteristics in the Interior Western United States. The modelling techniques included linear models (LMs), generalized additive models (GAMs), classification and regression trees (CARTs), multivariate adaptive regression splines (MARS), and artificial neural networks (ANNs). Models were built for two discrete and four continuous forest response variables using a variety of satellite-based predictor variables within each of five ecologically different regions. All techniques proved themselves workable in an automated environment. When their potential mapping ability was explored through simulations, tremendous advantages were seen in use of MARS and ANN for prediction over LMs, GAMs, and CART. However, much smaller differences were seen when using real data. In some instances, a simple linear approach worked virtually as well as the more complex models, while small gains were seen using more complex models in other instances. In real data runs, MARS and GAMS performed (marginally) best for prediction of forest characteristics.

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A comparison of two nonparametric estimation schemes: MARS and neural networks

Cited by 115 publications

References 16 publications

Critical Episodes of PM10 Particulate Matter Pollution in Santiago of Chile, an Approximation Using Two Prediction Methods: MARS Models and Gamma Models

Critical Episodes of PM10 Particulate Matter Pollution in Santiago of Chile, an Approximation Using Two Prediction Methods: MARS Models and Gamma Models

Long-term variations of the UV-B radiation over Central Europe as derived from the reconstructed UV time series

Comparing five modelling techniques for predicting forest characteristics

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