scholarly journals A penalized regression model for spatial functional data with application to the analysis of the production of waste in Venice province

2016 ◽  
Vol 31 (1) ◽  
pp. 23-38 ◽  
Author(s):  
Mara S. Bernardi ◽  
Laura M. Sangalli ◽  
Gabriele Mazza ◽  
James O. Ramsay
Keyword(s):  
Regression Model ◽  
Functional Data ◽  
Penalized Regression ◽  
Spatial Functional Data

scholarly journals On the performance of two clustering methods for spatial functional data

2015 ◽  
Vol 99 (4) ◽  
pp. 467-492 ◽  
Author(s):  
Elvira Romano ◽  
Jorge Mateu ◽  
Ramon Giraldo
Keyword(s):  
Functional Data ◽  
Clustering Methods ◽  
Spatial Functional Data

scholarly journals Selected Robust Logistic Regression Specification for Classification of Multi‑dimensional Functional Data in Presence of Outlier

2018 ◽  
Vol 2 (334) ◽  
Author(s):  
Mirosław Krzyśko ◽  
Łukasz Smaga
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Functional Data ◽  
Logistic Regression Model ◽  
Binary Classification ◽  
Classification Problem ◽  
Classification Rule ◽  
Unknown Parameters ◽  
Explanatory Variables

In this paper, the binary classification problem of multi‑dimensional functional data is considered. To solve this problem a regression technique based on functional logistic regression model is used. This model is re‑expressed as a particular logistic regression model by using the basis expansions of functional coefficients and explanatory variables. Based on re‑expressed model, a classification rule is proposed. To handle with outlying observations, robust methods of estimation of unknown parameters are also considered. Numerical experiments suggest that the proposed methods may behave satisfactory in practice.

A universal kriging approach for spatial functional data

2013 ◽  
Vol 27 (7) ◽  
pp. 1553-1563 ◽  
Author(s):  
William Caballero ◽  
Ramón Giraldo ◽  
Jorge Mateu
Keyword(s):  
Functional Data ◽  
Universal Kriging ◽  
Spatial Functional Data ◽  
Kriging Approach

scholarly journals Nomogram to Early Screen Multiparous Women for Preterm Birth in a Cohort Study

2020 ◽  
Author(s):  
Mayssa Traboulsi ◽  
Zainab El Alaoui Talibi ◽  
Abdellatif Boussaid
Keyword(s):  
Logistic Regression ◽  
Cohort Study ◽  
Preterm Birth ◽  
Regression Model ◽  
Low Income ◽  
Logistic Regression Model ◽  
Penalized Regression ◽  
Low Income Countries ◽  
Nulliparous Women ◽  
Multiparous Women

Abstract Background: Preterm Birth (PTB) can negatively affect the health of mothers as well as infants. Prediction of this gynecological complication remains difficult especially in Middle and Low-Income countries because of limited access to specific tests and data collection scarcity. Multiparous women in our study presented a higher PTB prevalence compared to nulliparous women. Methods: In a cohort study from Northern Lebanon of 1996 women, 922 were multiparous presenting a PTB prevalence of 8%. We analyzed the personal, demographic, and health indicators available for this group of women. We compared 4 modified logistic regression models (up-sampling, lasso penalized regression) to develop a nomogram that can screen for preterm in multi-parous women. The models were trained and validated on different data sets.Results: The best PTB prediction of the Logistic regression model reached around 88%. This was obtained using a Logistic Regression Model trained on up-sampled datasets and LASSO (Least Absolute Shrinkage and Selection Operator) penalized. The regression coefficients of the 6 selected variables (Pre-hemorrhage, Social status, Residence, Age, BMI, and Weight gain) were used to create a nomogram to screen multiparous women for PTB risk. Conclusions: The nomogram based on readily available indicators for multiparous women reasonably predicted most of the at PTB risk women. This tool will allow physicians to screen women that represent a high risk for spontaneous preterm birth and run furthermore adequate additional tests leading to better medical surveillance that can reduce PTB incidence.

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