scholarly journals NUCLEI SHAPE ANALYSIS, A STATISTICAL APPROACH

2011 ◽  
Vol 27 (1) ◽  
pp. 1 ◽  
Author(s):  
Alberto Nettel-Aguirre
Keyword(s):  
Data Analysis ◽  
Shape Analysis ◽  
Functional Data Analysis ◽  
Principal Components ◽  
Functional Data ◽  
Statistical Approach ◽  
Second Derivatives

The method presented in our paper suggests the use of Functional Data Analysis (FDA) techniques in an attempt to characterise the nuclei of two types of cells: Cancer and non-cancer, based on their 2 dimensional profiles. The characteristics of the profile itself, as traced by its X and Y coordinates, their first and second derivatives, their variability and use in characterization are the main focus of this approach which is not constrained to star shaped nuclei. Findings: Principal components created from the coordinates relate to shape with significant differences between nuclei type. Characterisations for each type of profile were found.

Prosodic effects on speech gestures: A shape analysis based on functional data analysis

2013 ◽  
Vol 133 (5) ◽  
pp. 3565-3565
Author(s):  
Christine Mooshammer ◽  
Lasse Bombien ◽  
Jelena Krivokapic
Keyword(s):  
Data Analysis ◽  
Shape Analysis ◽  
Functional Data Analysis ◽  
Functional Data

scholarly journals An Animated Functional Data Analysis Interface to Cluster Rapid Lung Function Decline and Enhance Center-Level Care in Cystic Fibrosis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jesse Pratt ◽  
Weiji Su ◽  
Don Hayes ◽  
John P. Clancy ◽  
Rhonda D. Szczesniak
Keyword(s):  
Cystic Fibrosis ◽  
Data Analysis ◽  
Lung Function ◽  
Functional Data Analysis ◽  
Principal Components ◽  
Functional Data ◽  
Rapid Decline ◽  
Components Analysis ◽  
Functional Principal Components Analysis ◽  
Functional Principal Components

Identifying disease progression through enhanced decision support tools is key to chronic management in cystic fibrosis at both the patient and care center level. Rapid decline in lung function relative to patient level and center norms is an important predictor of outcomes. Our objectives were to construct and utilize center-level classification of rapid decliners to develop an animated dashboard for comparisons within patients over time, multiple patients within centers, or between centers. A functional data analysis technique known as functional principal components analysis was applied to lung function trajectories from 18,387 patients across 247 accredited centers followed through the United States Cystic Fibrosis Foundation Patient Registry, in order to cluster patients into rapid decline phenotypes. Smaller centers (<30 patients) had older patients with lower baseline lung function and less severe rates of decline and had maximal decline later, compared to medium (30–150 patients) or large (>150 patients) centers. Small centers also had the lowest prevalence of early rapid decliners (17.7%, versus 24% and 25.7% for medium and large centers, resp.). The animated functional data analysis dashboard illustrated clustering and center-specific summaries of the rapid decline phenotypes. Clinical scenarios and utility of the center-level functional principal components analysis (FPCA) approach are considered and discussed.

scholarly journals Functional data analysis in shape analysis

2011 ◽  
Vol 55 (9) ◽  
pp. 2758-2773 ◽  
Author(s):  
Irene Epifanio ◽  
Noelia Ventura-Campos
Keyword(s):  
Data Analysis ◽  
Shape Analysis ◽  
Functional Data Analysis ◽  
Functional Data

scholarly journals Basis Expansions for Functional Snippets

Biometrika ◽  
2020 ◽  
Author(s):  
Zhenhua Lin ◽  
Jane-Ling Wang ◽  
Qixian Zhong
Keyword(s):  
Data Analysis ◽  
Convergence Rate ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Covariance Function ◽  
Covariance Estimation ◽  
Simulation Studies ◽  
Finite Sample ◽  
Covariance Functions ◽  
The Mean

Summary Estimation of mean and covariance functions is fundamental for functional data analysis. While this topic has been studied extensively in the literature, a key assumption is that there are enough data in the domain of interest to estimate both the mean and covariance functions. In this paper, we investigate mean and covariance estimation for functional snippets in which observations from a subject are available only in an interval of length strictly (and often much) shorter than the length of the whole interval of interest. For such a sampling plan, no data is available for direct estimation of the off-diagonal region of the covariance function. We tackle this challenge via a basis representation of the covariance function. The proposed estimator enjoys a convergence rate that is adaptive to the smoothness of the underlying covariance function, and has superior finite-sample performance in simulation studies.

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