Functional data clustering via hypothesis testing k-means

2018 ◽  
Vol 34 (2) ◽  
pp. 527-549 ◽  
Author(s):  
Adriano Zanin Zambom ◽  
Julian A. A. Collazos ◽  
Ronaldo Dias
Keyword(s):  
Hypothesis Testing ◽  
Functional Data ◽  
Data Clustering

scholarly journals Image Analysis and Functional Data Clustering for Random Shape Aggregate Models

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1903
Author(s):  
Jonghyun Yun ◽  
Sanggoo Kang ◽  
Amin Darabnoush Tehrani ◽  
Suyun Ham
Keyword(s):  
Image Analysis ◽  
Mixture Model ◽  
Functional Data ◽  
Data Clustering ◽  
Learning Algorithm ◽  
Morphological Characteristics ◽  
Random Shape ◽  
Aggregate Model ◽  
Random Aggregate ◽  
Aggregate Morphology

This study presents a random shape aggregate model by establishing a functional mixture model for images of aggregate shapes. The mesoscale simulation to consider heterogeneous properties concrete is the highly cost- and time-effective method to predict the mechanical behavior of the concrete. Due to the significance of the design of the mesoscale concrete model, the shape of the aggregate is the most important parameter to obtain a reliable simulation result. We propose image analysis and functional data clustering for random shape aggregate models (IFAM). This novel technique learns the morphological characteristics of aggregates using images of real aggregates as inputs. IFAM provides random aggregates across a broad range of heterogeneous shapes using samples drawn from the estimated functional mixture model as outputs. Our learning algorithm is fully automated and allows flexible learning of the complex characteristics. Therefore, unlike similar studies, IFAM does not require users to perform time-consuming tuning on their model to provide realistic aggregate morphology. Using comparative studies, we demonstrate the random aggregate structures constructed by IFAM achieve close similarities to real aggregates in an inhomogeneous concrete medium. Thanks to our fully data-driven method, users can choose their own libraries of real aggregates for the training of the model and generate random aggregates with high similarities to the target libraries.

Linear Hypothesis Testing With Functional Data

Technometrics ◽  
2018 ◽  
Vol 61 (1) ◽  
pp. 99-110 ◽  
Author(s):  
Łukasz Smaga ◽  
Jin-Ting Zhang
Keyword(s):  
Hypothesis Testing ◽  
Functional Data ◽  
Linear Hypothesis

Pseudo-quantile functional data clustering

2020 ◽  
Vol 178 ◽  
pp. 104626 ◽  
Author(s):  
Joonpyo Kim ◽  
Hee-Seok Oh
Keyword(s):  
Functional Data ◽  
Data Clustering

Linear hypothesis testing for weighted functional data with applications

2019 ◽  
Vol 47 (2) ◽  
pp. 493-515
Author(s):  
Łukasz Smaga ◽  
Jin‐Ting Zhang
Keyword(s):  
Hypothesis Testing ◽  
Functional Data ◽  
Linear Hypothesis

Hypothesis Testing for Multiple Mean and Correlation Curves with Functional Data

2020 ◽  
Author(s):  
Ao Yuan ◽  
Hong-Bin Fang ◽  
Haiou Li ◽  
Colin O. Wu ◽  
Ming T. Tan
Keyword(s):  
Hypothesis Testing ◽  
Functional Data

New insights on permutation approach for hypothesis testing on functional data

2014 ◽  
Vol 8 (3) ◽  
pp. 339-356 ◽  
Author(s):  
Livio Corain ◽  
Viatcheslav B. Melas ◽  
Andrey Pepelyshev ◽  
Luigi Salmaso
Keyword(s):  
Hypothesis Testing ◽  
Functional Data

scholarly journals Dissimilarity for functional data clustering based on smoothing parameter commutation

2017 ◽  
Vol 27 (11) ◽  
pp. 3492-3504 ◽  
Author(s):  
ShengLi Tzeng ◽  
Christian Hennig ◽  
Yu-Fen Li ◽  
Chien-Ju Lin
Keyword(s):  
Functional Data ◽  
Data Clustering ◽  
Smoothing Splines ◽  
Smoothing Parameter ◽  
Multiple Time ◽  
Easy Method ◽  
Time Points ◽  
Methadone Dosage ◽  
Estimation Uncertainty ◽  
Smoothing Parameters

Many studies measure the same type of information longitudinally on the same subject at multiple time points, and clustering of such functional data has many important applications. We propose a novel and easy method to implement dissimilarity measure for functional data clustering based on smoothing splines and smoothing parameter commutation. This method handles data observed at regular or irregular time points in the same way. We measure the dissimilarity between subjects based on varying curve estimates with pairwise commutation of smoothing parameters. The intuition is that smoothing parameters of smoothing splines reflect the inverse of the signal-to-noise ratios and that when applying an identical smoothing parameter the smoothed curves for two similar subjects are expected to be close. Our method takes into account the estimation uncertainty using smoothing parameter commutation and is not strongly affected by outliers. It can also be used for outlier detection. The effectiveness of our proposal is shown by simulations comparing it to other dissimilarity measures and by a real application to methadone dosage maintenance levels.

Sign in / Sign up

Export Citation Format

Share Document