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Regression spline

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

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 ...

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Estimation of a regression spline sample selection model

Estimation of a regression spline sample selection model

... Smoothing parameter selection is important for practical modeling. In principle, it can be achieved by direct grid search optimization of, for instance, the Akaike information criterion (AIC; Akaike, 1973). However, if ...

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Regression spline bivariate probit models: A practical approach to testing for exogeneity

Regression spline bivariate probit models: A practical approach to testing for exogeneity

... penalized regression spline bivariate models. This is because the number of degrees of freedom of the test should be calculated using the notion of estimated degrees of freedom (edf ). The total edf is ...

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Copula regression spline sample selection models:
the R Package SemiParSampleSel

Copula regression spline sample selection models: the R Package SemiParSampleSel

... copula regression spline sample selection model, the absence of sample selection bias is equivalent to θ = 0 which in turn is equivalent to the condition that the Kendall’s τ coefficient equals ...

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Copula Regression Spline Sample Selection Models: The R Package SemiParSampleSel

Copula Regression Spline Sample Selection Models: The R Package SemiParSampleSel

... copula regression spline sample selection model, the absence of sample selection bias is equivalent to θ = 0 which in turn is equivalent to the condition that the Kendall’s τ coefficient equals ...

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Estimation of a regression spline sample selection model

Estimation of a regression spline sample selection model

... flexibility in the model) and then the corresponding β k penalized in order to suppress that part of non-linearity which is not supported from the data. As it will be shown in the next section, this can be achieved using ...

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Copula regression spline models for binary outcomes

Copula regression spline models for binary outcomes

... We introduce a framework for estimating the effect that a binary treatment has on a binary outcome in the presence of unobserved confounding. The methodology is applied to a case study which uses data from the Medical ...

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Kernel and regression spline smoothing techniques to estimate coefficient in rates model and its application in psoriasis

Kernel and regression spline smoothing techniques to estimate coefficient in rates model and its application in psoriasis

... To fit the model whose specific coefficient was time- dependent, b-spline functions with third degree as well as kernel functions were used. Thus, first, we had to specify the number of nodes used to build ...

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A Multivariate Adaptive Regression Spline Approach for Prediction of Maximum Shear Modulus and Minimum Damping Ratio

A Multivariate Adaptive Regression Spline Approach for Prediction of Maximum Shear Modulus and Minimum Damping Ratio

... procedure [7]. It can be considered as a generalisation of classification and regression trees (CART) [8]. It uses a lot of piecewise regression equations in the model. It has been successfully used for ...

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Copula regression spline models for binary outcomes

Copula regression spline models for binary outcomes

... plate regression splines with basis dimensions equal to 20 and penalties based on second order derivatives (Wood, ...more spline bases is advis- able when the effective degrees of freedom of the smooth ...

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Predictive validity of a brief antiretroviral adherence index: Retrospective cohort analysis under conditions of repetitive administration

Predictive validity of a brief antiretroviral adherence index: Retrospective cohort analysis under conditions of repetitive administration

... the regression spline plots for both outcome metrics (Figures 3 and 4) in the current study, we felt that a cut point around 5 identified a region above which a monotonic relationship between adherence ...

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Application of Nonparametric Quantile Regression to Estimating Value at Risk.

Application of Nonparametric Quantile Regression to Estimating Value at Risk.

... smoothing spline is a simpler procedure than the knots selections procedure for regression spline because it only involves an optimization of one parameter in (0, ...smoothing spline models, ...

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Using spline test-day model for estimating the genetic parameters for cows milk yield

Using spline test-day model for estimating the genetic parameters for cows milk yield

... random regression spline model. The spline model has been considered as a good alternative to Legendre polynomials to direct interpretation of ...random regression was fitted with linear ...

5

Healthcare workers’ willingness to respond following a disaster: a novel statistical approach toward data analysis

Healthcare workers’ willingness to respond following a disaster: a novel statistical approach toward data analysis

... the injured and ill and their WTR; the two variables were also identified as significant predictors of WTR in both re- gression models and across all WTR levels (quantiles). These factors seem to overshadow other ...

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Infants’ Weight Growth Model in Surabaya (Indonesia) By Using Weighted Spline Regression

Infants’ Weight Growth Model in Surabaya (Indonesia) By Using Weighted Spline Regression

... of spline include Wu and Zhang [33] and Diggle, et. al., [14] for the spline model of longitudinal data, Wahba [30] for the original spline model, Cox and O'Sullivan [12] for the spline model ...

5

Modeling the Percentage of Poor People in Indonesia Using Spline Nonparametric Regression Approach

Modeling the Percentage of Poor People in Indonesia Using Spline Nonparametric Regression Approach

... Some researchers are intensively investigated poverty in Indonesia include [3] which models the poverty using Spline Bayesian approach. Then, [6] concluded that there are three causes of household poverty in ...

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Testing for the absence of unobserved confounding in
Semiparametric Bivariate Probit Models

Testing for the absence of unobserved confounding in Semiparametric Bivariate Probit Models

... The expected information matrix employed for computing Var ρ ˆ corresponds to the Bayesian covariance matrix typically used for constructing ‘confidence’ intervals for the terms of a penalized regression ...

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Layer-Wise Learning Strategy for Nonparametric Tensor Product Smoothing Spline Regression and Graphical Models

Layer-Wise Learning Strategy for Nonparametric Tensor Product Smoothing Spline Regression and Graphical Models

... nonparametric regression to nonparametric graphical ...smoothing spline analysis of variance (SSANOVA) framework, which assumes that the multivariate function can be decomposed into the summation of main ...

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Multi-Level Image Segmentation Model for Standing Human Body Extraction Based On Spline Regression

Multi-Level Image Segmentation Model for Standing Human Body Extraction Based On Spline Regression

... the presence of humans in an image, greatly reduces the search space for the upper body, and provides information about skin color. Face dimensions also aid in determining the di[r] ...

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Partial mixture model for tight clustering of gene expression time course

Partial mixture model for tight clustering of gene expression time course

... partial regression clustering algorithm is proposed for tight clustering which naturally incorporates replication information and allows a set of scattered genes to be left ...partial regression clustering ...

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