Spline smoothing and regression
Kernel and regression spline smoothing techniques to estimate coefficient in rates model and its application in psoriasis
... It seems that these differences are due to biological and climatic conditions as well as different nutritional condi- tions. Therefore, we recommend examining all patients with psoriasis, regardless of their disease ...
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Comparison between fractional polynomials, spline smoothing, and multiple logistic regression models in the study of associated hypertension risk factors
... Renal failure, sex, history of diabetes and type of diabetes were considered dichotomous, and the other variables were continuous. A logistic model was fitted. As mentioned from previous knowledge, the shape of BMI ...
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Comparison of Significant Approaches of Penalized Spline Regression (P-splines)
... semiparametric regression methods combine the leads of parametric and nonparametric approaches to regression analysis, while in precise, penalized spline regression uses the knowledge of ...
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Smoothing Parameter Selection for Nonparametric Regression Using Smoothing Spline
... of smoothing parameter (or penalty pa- rameter) through Monte Carlo simulation ...study. Smoothing parameters play a crucial role in this ...of smoothing parameter overfit the data, whereas too high ...
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Estimasi Fungsi Regresi Dalam Model Regresi Nonparametrik Birespon Menggunakan Estimator Smoothing Spline dan Estimator Kernel
... a regression model which is illustrating of the connection pattern between respond variable and one or more predictor variables, where between first respond and second respond have correlation each ...of ...
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Bumps in river profiles: uncertainty assessment and smoothing using quantile regression techniques
... Abstract. The analysis of longitudinal river profiles is an important tool for studying landscape evolution. However, characterizing river profiles based on digital elevation models (DEMs) suffers from errors and ...
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Penalized Regression Methods with Application to Generalized Linear Models, Generalized Additive Models, and Smoothing
... Smoothing can also be used to filter out noise from a distorted image. As an exam- ple we consider the case of a mammography. In the upper left part of Fig. 5 a mammography is displayed. In the upper part of the ...
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Infants’ Weight Growth Model in Surabaya (Indonesia) By Using Weighted Spline Regression
... It can be seen visually that the growth model for infants in the city of Surabaya has inequality of variance. There is a trend of increasing age, the variance also continues to expand. To make sure about the inequality ...
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Glucose Calibration Modeling in Blood with Spline Regression Approaching to Non-Invasive Tools
... using spline regression can determine the best ...linear Spline will be obtained a model with the smallest GCV, the largest R-square, and the smallest ...quadratic spline model, and the cubic ...
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Copula Regression Spline Sample Selection Models: The R Package SemiParSampleSel
... smoothing splines. The latter further extended this approach by introducing a Bayesian al- gorithm based on low rank penalized B-splines for non-linear and varying-coefficient effects and Markov random-field priors ...
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Modeling the Percentage of Poor People in Indonesia Using Spline Nonparametric Regression Approach
... parametric regression approaches such as linear regression, quadratic or ...Quadratic spline nonparametric regression approach with two points knots, very decent used to model the percentage ...
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Fingerprint Classification Using Kernel Smoothing Technique and Generalized Regression Neural Network and Probabilistic Neural Network
... With respect to classifiers, many methods have been used. Fuzzy system is used in both classification and feature extraction [11-13]. Artificial Neural Network (ANN) is another popular method used for classification ...
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Relevance vector machine and multivariate adaptive regression spline for modelling ultimate capacity of pile foundation
... Adaptive Regression Spline (MARS) for prediction of axial capacity of driven piles and drilled shafts using data of in- situ pile load tests and Cone Penetration Test(CPT) ...to regression that has ...
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Smoothing and Household Choices
... costly smoothing mechanisms: following a health shock, the household with access to health insurance invests more in children’s human capital and agriculture, relative to the level it would have chosen in the ...
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Smoothing error pitfalls
... estimated smoothing errors (in terms of vari- ances/covariances) at 24, 25 and 26 km altitude: the smoothing er- rors on the fine grid estimated by Gaussian error estimation (red) are largest at 25 km, an ...
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MOTION SMOOTHING WITH FILTER
... The Block matching algorithm suffers from limitation concerning the fidelity of the predicted image: blocking effect is introduced for the lack of coherence of the estimated motion to the actual motion. It is possible to ...
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Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model
... each spline, inputs, x, are split into subgroups and knots so that they are located between the x and the interval in the same x to separate the subgroups (Friedman, 1991; Sephton, ...
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Consumption and Income Smoothing
... The purpose of this paper is to provide an original interpretation for consumption and income smoothing. The underground economy produces a second cycle which gives to households the opportunity to ensure ...
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Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized Regression Neural Network and Adaptive Resonance Theory with Mapping
... Abstract: The necessity of fast and precise identification from fingerprints might be fulfilled via systems benefiting from intelligent elements such as Neural Networks. The process of recognition and classification have ...
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Forecasting With Exponential Smoothing Whats The Right Smoothing Constant?
... In the second type, too, demand was normally distributed, but this time with a mean that was a linear function of time. The intercept and slope were arbitrarily picked. The standard deviation of demand was also ...
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