Pemodelan Multivariate Adaptive Generalized Poisson Regression Spline pada Kasus Jumlah Kematian Ibu di Provinsi Jawa Timur

Relevance vector machine and multivariate adaptive regression spline for modelling ultimate capacity of pile foundation

This study examines the capability of the Relevance Vector Machine (RVM) and Multivariate Adaptive Regression Spline (MARS) for prediction of ultimate capacity of driven piles and drilled shafts. RVM is a sparse method for training generalized linear models, while MARS technique is basically an adaptive piece-wise regression approach. In this paper, pile capacity prediction models are developed...

Censored generalized Poisson regression model

This paper develops a censored generalized Poisson regression model that can be used to predict a response variable that is a2ected by one or more explanatory variables. The censored generalized Poisson regression model is suitable for modeling count data that exhibit either overor under-dispersion. The regression parameters are estimated by the method of maximum likelihood and approximate test...

Multivariate Adaptive Regression Splines

Predicting Flow Number of Asphalt Mixtures Based on the Marshall Mix design Parameters Using Multivariate Adaptive Regression Spline (MARS)

Rutting is one of the major distresses in the flexible pavements, which is heavily influenced by the asphalt mixtures properties at high temperatures. There are several methods for the characterization of the rutting resistance of asphalt mixtures. Flow number is one of the most important parameters that can be used for the evaluation of rutting. The flow number is measured by the dynamic creep...

Discussion: Multivariate Adaptive Regression Splines

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