The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Jiwon Oh - One of the best experts on this subject based on the ideXlab platform.
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an automated Statistical Technique for counting distinct multiple sclerosis lesions
American Journal of Neuroradiology, 2018Co-Authors: Jordan D Dworkin, Kristin A Linn, Ipek Oguz, Greg M Fleishman, Rohit Bakshi, G Nair, Peter A Calabresi, Roland G Henry, Jiwon OhAbstract:BACKGROUND AND PURPOSE: Lesion load is a common biomarker in multiple sclerosis, yet it has historically shown modest association with clinical outcome. Lesion count, which encapsulates the natural history of lesion formation and is thought to provide complementary information, is difficult to assess in patients with confluent (ie, spatially overlapping) lesions. We introduce a Statistical Technique for cross-sectionally counting pathologically distinct lesions. MATERIALS AND METHODS: MR imaging was used to assess the probability of a lesion at each location. The texture of this map was quantified using a novel Technique, and clusters resembling the center of a lesion were counted. Validity compared with a criterion standard count was demonstrated in 60 subjects observed longitudinally, and reliability was determined using 14 scans of a clinically stable subject acquired at 7 sites. RESULTS: The proposed count and the criterion standard count were highly correlated ( r = 0.97, P 59 = −.83, P = .41), and the variability of the proposed count across repeat scans was equivalent to that of lesion load. After accounting for lesion load and age, lesion count was negatively associated ( t 58 = −2.73, P r = 0.35, P r = 0.10, P = .44) or lesion count ( r = −.12, P = .36) alone. CONCLUSIONS: This study introduces a novel Technique for counting pathologically distinct lesions using cross-sectional data and demonstrates its ability to recover obscured longitudinal information. The proposed count allows more accurate estimation of lesion size, which correlated more closely with disability scores than either lesion load or lesion count alone.
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an automated Statistical Technique for counting distinct multiple sclerosis lesions can recover aspects of lesion history and provide relevant disease information
bioRxiv, 2017Co-Authors: Jordan D Dworkin, Kristin A Linn, Ipek Oguz, Greg M Fleishman, Rohit Bakshi, G Nair, Peter A Calabresi, Roland G Henry, Jiwon Oh, Nico PapinuttoAbstract:Abstract Background Lesion load is a common biomarker in multiple sclerosis, yet it has historically shown modest associations with clinical outcomes. Lesion count, which encapsulates the natural history of lesion formation and is thought to provide complementary information, is difficult to assess in patients with confluent (i.e. spatially overlapping) lesions. We introduce a Statistical Technique for cross-sectionally counting pathologically distinct lesions. Methods MRI is used to assess the probability of lesion at each location. The texture of this map is quantified using a novel Technique, and clusters resembling the center of a lesion are counted. Results Validity was demonstrated by comparing the proposed count to a gold-standard count in 60 subjects observed longitudinally. The counts were highly correlated (r = .97, p .40). Reliability was determined using 14 scans of a clinically stable subject acquired at 7 sites, and variability of lesion count was equivalent to that of lesion load. Accounting for lesion load and age, lesion count was negatively associated (t58 = −2.73, p .40) or lesion count (r = −.12, p > .30) alone. Conclusion These findings demonstrate that it is possible to recover important aspects of the natural history of lesion formation without longitudinal data, and suggest that lesion size provides complementary information about disease. Grant Support The project described was supported in part by the NIH grants R01 NS085211, R21 NS093349, and R01 NS094456 from the National Institute of Neurological Disorders and Stroke (NINDS). The study was also supported by the Intramural Research Program of NINDS and the Race to Erase MS Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
Khaldoon Nusair - One of the best experts on this subject based on the ideXlab platform.
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comparative assessment of structural equation modeling and multiple regression research methodologies e commerce context
Tourism Management, 2010Co-Authors: Khaldoon NusairAbstract:Structural equation modeling (SEM) is a powerful Statistical Technique that establishes measurement models and structural models. On the other hand, multiple regression (MR) is considered a sophisticated and well-developed modeling approach to data analysis with a history of more than 100 years. This paper empirically compares SEM and MR by testing a model of commitment in a B-to-C e-commerce travel context, shedding light on applications of these two popular methods in tourism research. The findings indicate that only two significant relationships are justified by MR. In comparison, SEM results reveal more Statistically significant relationships after the “best-fitting” measurement model with model D being the “best-fitting” model. The findings support some key empirical limitations of MR as a widely used Statistical Technique in the tourism research.
Roman Borisyuk - One of the best experts on this subject based on the ideXlab platform.
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Statistical Technique for analysing functional connectivity of multiple spike trains
Journal of Neuroscience Methods, 2011Co-Authors: Mohammad Shahed Masud, Roman BorisyukAbstract:Abstract A new Statistical Technique, the Cox method, used for analysing functional connectivity of simultaneously recorded multiple spike trains is presented. This method is based on the theory of modulated renewal processes and it estimates a vector of influence strengths from multiple spike trains (called reference trains) to the selected (target) spike train. Selecting another target spike train and repeating the calculation of the influence strengths from the reference spike trains enables researchers to find all functional connections among multiple spike trains. In order to study functional connectivity an “influence function” is identified. This function recognises the specificity of neuronal interactions and reflects the dynamics of postsynaptic potential. In comparison to existing Techniques, the Cox method has the following advantages: it does not use bins (binless method); it is applicable to cases where the sample size is small; it is sufficiently sensitive such that it estimates weak influences; it supports the simultaneous analysis of multiple influences; it is able to identify a correct connectivity scheme in difficult cases of “common source” or “indirect” connectivity. The Cox method has been thoroughly tested using multiple sets of data generated by the neural network model of the leaky integrate and fire neurons with a prescribed architecture of connections. The results suggest that this method is highly successful for analysing functional connectivity of simultaneously recorded multiple spike trains.
Julie A Van Dyke - One of the best experts on this subject based on the ideXlab platform.
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the random forests Statistical Technique an examination of its value for the study of reading
Scientific Studies of Reading, 2016Co-Authors: Kazunaga Matsuki, Victor Kuperman, Julie A Van DykeAbstract:Studies investigating individual differences in reading ability often involve data sets containing a large number of collinear predictors and a small number of observations. In this paper, we discuss the method of Random Forests and demonstrate its suitability for addressing the Statistical concerns raised by such datasets. The method is contrasted with other methods of estimating relative variable importance, especially Dominance Analysis and Multimodel Inference. All methods were applied to a dataset that gauged eye-movements during reading and offline comprehension in the context of multiple ability measures with high collinearity due to their shared verbal core. We demonstrate that the Random Forests method surpasses other methods in its ability to handle model overfitting, and accounts for a comparable or larger amount of variance in reading measures relative to other methods.
Mohamed Nounou - One of the best experts on this subject based on the ideXlab platform.
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monitoring of wastewater treatment plants using improved univariate Statistical Technique
Process Safety and Environmental Protection, 2018Co-Authors: Imen Baklouti, Majdi Mansouri, Ahmed Ben Hamida, Hazem Nounou, Mohamed NounouAbstract:Abstract Proper operation of the wastewater treatment plants (WWTPs) is crucial in order to maintain the sought effectiveness and desirable water quality. Therefore, the objective of this paper is to develop univariate Statistical Technique that aims at enhancing the monitoring of wastewater treatment plants using an improved particle filtering (IPF)-based multiscale optimized exponentially weighted moving average chart (MS-OEWMA). The advantages of the developed Technique are fivefold: (i) estimate a nonlinear state variables of WWTPs using IPF Technique. The IPF method yields an optimum choice of the sampling distribution, which also accounts for the observed data; (ii) use the dynamical multiscale representation to extract accurate deterministic features and decorrelate autocorrelated measurements. (iii) Develop an optimized EWMA (OEWMA) based on the best selection of smoothing parameter (λ) and control width L; (iv) combine the advantages of state estimation Technique with MS-OEWMA chart to improve the fault detection in WWTP systems; and (v) investigate the effect of fault types (offset or bias, variance and drift) and fault sizes on the fault detection performances. The developed Technique is validated using simulated COST wastewater treatment BSM1 model. The BSM1, provided by the IWA Task Group on Benchmarking of Control Strategies, is a simulation platform that allows for creating sensor faults disturbances in a wastewater treatment plant. The detection results are evaluated using three fault detection criteria: missed detection rate (MDR), false alarm rate (FAR) and average run length (ARL1).
