Model-free data condensation

1991 ◽  
Vol 335 (1636) ◽  
pp. 1-50
Keyword(s):  
Data Analysis ◽  
Statistical Inference ◽  
Model Free ◽  
Classical Statistics ◽  
Free Data

One aim of data analysis is its condensation, namely capturing its gist in an apposite way. This paper addresses the problem of constructing and assessing such condensations without reference to mechanisms which might have generated the data. The results obtained lead to non-probabilistic interpretations of some well-known inferential procedures of classical statistics and thereby shed new light on the structure of statistical inference and the theory of probability.

Optimal Model-Free Approach Based on MDL and CHL for Active Brain Identification in fMRI Data Analysis

2020 ◽  
Vol 16 ◽  
Author(s):  
Hussain A. Jaber ◽  
Ilyas Çankaya ◽  
Hadeel K. Aljobouri ◽  
Orhan M. Koçak ◽  
Oktay Algin
Keyword(s):  
Data Analysis ◽  
Time Course ◽  
Extreme Values ◽  
Statistical Parametric Mapping ◽  
Fmri Data ◽  
Cluster Center ◽  
Jaccard Coefficient ◽  
Neural Gas ◽  
Model Free ◽  
Clustering Approach

Background: Cluster analysis is a robust tool for exploring the underlining structures in data and grouping them with similar objects. In the researches of Functional Magnetic Resonance Imaging (fMRI), clustering approaches attempt to classify voxels depending on their time-course signals into a similar hemodynamic response over time. Objective: In this work, a novel unsupervised learning approach is proposed that relies on using Enhanced Neural Gas (ENG) algorithm in fMRI data for comparison with Neural Gas (NG) method, which has yet to be utilized for that aim. The ENG algorithm depends on the network structure of the NG and concentrates on an efficacious prototype-based clustering approach. Methods: The comparison outcomes on real auditory fMRI data show that ENG outperforms the NG and statistical parametric mapping (SPM) methods due to its insensitivity to the ordering of input data sequence, various initializations for selecting a set of neurons, and the existence of extreme values (outliers). The findings also prove its capability to discover the exact and real values of a cluster number effectively. Results: Four validation indices are applied to evaluate the performance of the proposed ENG method with fMRI and compare it with a clustering approach (NG algorithm) and model-based data analysis (SPM). These validation indices include the Jaccard Coefficient (JC), Receiver Operating Characteristic (ROC), Minimum Description Length (MDL) value, and Minimum Square Error (MSE). Conclusion: The ENG technique can tackle all shortcomings of NG application with fMRI data, identify the active area of the human brain effectively, and determine the locations of the cluster center based on the MDL value during the process of network learning.

scholarly journals Model-free data interpretation for continuous monitoring of complex structures

2008 ◽  
Vol 22 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Daniele Posenato ◽  
Francesca Lanata ◽  
Daniele Inaudi ◽  
Ian F.C. Smith
Keyword(s):  
Continuous Monitoring ◽  
Data Interpretation ◽  
Complex Structures ◽  
Model Free ◽  
Free Data

Propagative broad learning for nonparametric modeling of ambient effects on structural health indicators

2020 ◽  
pp. 147592172091692 ◽  
Author(s):  
Sin-Chi Kuok ◽  
Ka-Veng Yuen ◽  
Stephen Roberts ◽  
Mark A Girolami
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Health Indicators ◽  
Operating Conditions ◽  
Learning Approach ◽  
Nonparametric Modeling ◽  
Structural Health ◽  
Model Free ◽  
Free Data

In this article, a novel propagative broad learning approach is proposed for nonparametric modeling of the ambient effects on structural health indicators. Structural health indicators interpret the structural health condition of the underlying dynamical system. Long-term structural health monitoring on in-service civil engineering infrastructures has demonstrated that commonly used structural health indicators, such as modal frequencies, depend on the ambient conditions. Therefore, it is crucial to detrend the ambient effects on the structural health indicators for reliable judgment on the variation of structural integrity. However, two major challenging problems are encountered. First, it is not trivial to formulate an appropriate parametric expression for the complicated relationship between the operating conditions and the structural health indicators. Second, since continuous data stream is generated during long-term structural health monitoring, it is required to handle the growing data efficiently. The proposed propagative broad learning provides an effective tool to address these problems. In particular, it is a model-free data-driven machine learning approach for nonparametric modeling of the ambient-influenced structural health indicators. Moreover, the learning network can be updated and reconfigured incrementally to adapt newly available data as well as network architecture modifications. The proposed approach is applied to develop the ambient-influenced structural health indicator model based on the measurements of 3-year full-scale continuous monitoring on a reinforced concrete building.

Why Data Analysis?

1990 ◽  
Vol 83 (2) ◽  
pp. 90-93
Author(s):  
Richard L. Scheaffer
Keyword(s):  
Data Analysis ◽  
Empirical Data ◽  
Exploratory Data Analysis ◽  
Applied Statistics ◽  
Statistical Education ◽  
The Past ◽  
Classical Statistics ◽  
Exploratory Data ◽  
To Come ◽  
Oriented Approach

Recent years have witnessed a strong movement away from what might be termed classical statistics to a more empirical, data-oriented approach to statistics, sometimes termed exploratory data analysis, or EDA. This movement has been active among professional statisticians for twenty or twenty-five years but has begun permeating the area of statistical education for nonstatisticians only in the past five to ten years. At this point, there seems to be little doubt that EDA approaches to applied statistics will gain support over classical approaches in the years to come. That is not to say that classical statistics will disappear. The two approaches begin with different assumptions and have different objectives, but both are important. These differences will be outlined in this article.

Introduction to Data Analysis and Statistical Inference.

1984 ◽  
Vol 79 (385) ◽  
pp. 242
Author(s):  
William F. Taylor ◽  
Carl N. Morris ◽  
John E. Rolph
Keyword(s):  
Data Analysis ◽  
Statistical Inference

Quantified Neurophysiology with mapping: Statistical inference, Exploratory and Confirmatory data analysis

1990 ◽  
Vol 3 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Frank H. Duffy ◽  
Kenneth Jones ◽  
Peter Bartels ◽  
Marilyn Albert ◽  
Gloria B. McAnulty ◽  
...  
Keyword(s):  
Data Analysis ◽  
Statistical Inference ◽  
Confirmatory Data Analysis
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