Search results for "Parametric statistics"
showing 10 items of 354 documents
A Novel Intelligent Technique for Product Acceptance Process Optimization on the Basis of Misclassification Probability in the Case of Log-Location-S…
2019
In this paper, to determine the optimal parameters of the product acceptance process under parametric uncertainty of underlying models, a new intelligent technique for optimization of product acceptance process on the basis of misclassification probability is proposed. It allows one to take into account all possible situations that may occur when it is necessary to optimize the product acceptance process. The technique is based on the pivotal quantity averaging approach (PQAA) which allows one to eliminate the unknown parameters from the problem and to use available statistical information as completely as possible. It is conceptually simple and easy to use. One of the most important featur…
Intelligent Constructing Exact Tolerance Limits for Prediction of Future Outcomes Under Parametric Uncertainty
2021
The problem of constructing one-sided exact statistical tolerance limits on the kth order statistic in a future sample of m observations from a distribution of log-location-scale family on the basis of an observed sample from the same distribution is considered. The new technique proposed here emphasizes pivotal quantities relevant for obtaining tolerance factors and is applicable whenever the statistical problem is invariant under a group of transformations that acts transitively on the parameter space. The exact tolerance limits on order statistics associated with sampling from underlying distributions can be found easily and quickly making tables, simulation, Monte Carlo estimated percen…
Non-parametric approaches to the impact of Holstein heifer growth from birth to insemination on their dairy performance at lactation one
2012
SUMMARYParametric approaches have been used widely to model animal growth and study the impact of growth profile on performance. Individual variation is often not considered in such approaches. However, non-parametric modelling allows this. Such an approach, based on spline functions, was used to study the importance of growth profiles from age 0 to 15 months (i.e. insemination) on milk yield and composition in primiparous cows. A dataset of 447 heifers was used for analysis of growth performance; 296 of them were also used to study impact on lactation. All of them originated from a French experimental herd and were born between 1986 and 2006. Clustering methods were also tested. Comparison…
Automated Valuation Methods in Atypical Real Estate Markets Using the Mono-parametric Approach
2017
The appraisal objectivity depends on the possibility to quickly and easily access to reliable real estate data in order to apply appropriate appraisal approaches. In order to ensure the objectivity of the real estate appraisals, in recent years Automated Valuation Methods (AVM) have been developed, integrating computerized real estate databases and programming languages. The Automated Valuation Methods proposed at international level usually recur to regression models, aimed to return appraisal equations based on reliable real estate databases. This approach is not applicable in some markets where lack of data does not allow the implementation of regression models. This paper proposes to im…
Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions
2020
Abstract Nitrogen (N) is considered as one of the most important plant macronutrients and proper management of N therefore is a pre-requisite for modern agriculture. Continuous satellite-based monitoring of this key plant trait would help to understand individual crop N use efficiency and thus would enable site-specific N management. Since hyperspectral imaging sensors could provide detailed measurements of spectral signatures corresponding to the optical activity of chemical constituents, they have a theoretical advantage over multi-spectral sensing for the detection of crop N. The current study aims to provide a state-of-the-art overview of crop N retrieval methods from hyperspectral data…
Dynamic image denoising for voxel-wise quantification with Statistical Parametric Mapping in molecular neuroimaging.
2018
Purpose PET and SPECT voxel kinetics are highly noised. To our knowledge, no study has determined the effect of denoising on the ability to detect differences in binding at the voxel level using Statistical Parametric Mapping (SPM). Methods In the present study, groups of subject-images with a 10%- and 20%- difference in binding of [123I]iomazenil (IMZ) were simulated. They were denoised with Factor Analysis (FA). Parametric images of binding potential (BPND) were produced with the simplified reference tissue model (SRTM) and the Logan non-invasive graphical analysis (LNIGA) and analyzed using SPM to detect group differences. FA was also applied to [123I]IMZ and [11C]flumazenil (FMZ) clinic…
Epoch versus impulse models in the analysis of parametric fMRI studies
2013
Abstract Objective In parametric fMRI studies the relationship between the amplitude of the hemodynamic response and electrophysiological or behavioral parameters is commonly analyzed using the general linear model (GLM). We examined ways of using single-trial response time (RT) in the analysis of a decision-making task to better isolate task-specific activation. Methods fMRI and RT data were recorded in twenty-one subjects performing a visual-oddball-task. Four explanatory variables (EVs) were generated for the GLM-analysis: A conventional (constant impulse) EV, a constant epoch EV informed using subjects’ average RT, a variable impulse EV and a variable epoch EV both informed using single…
Liebowitz Social Anxiety Scale (LSAS): Optimal cut points for remission and response in a German sample
2017
The Liebowitz Social Anxiety Scale (LSAS) is the most frequently used instrument to assess social anxiety disorder (SAD) in clinical research and practice. Both a self-reported (LSAS-SR) and a clinician-administered (LSAS-CA) version are available. The aim of the present study was to define optimal cut-off (OC) scores for remission and response to treatment for the LSAS in a German sample. Data of N = 311 patients with SAD were used who had completed psychotherapeutic treatment within a multicentre randomized controlled trial. Diagnosis of SAD and reduction in symptom severity according to the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th editi…
Macrostructural EEG characterization based on nonparametric change point segmentation: application to sleep analysis
2001
In the present investigation a new methodology for macrostructural EEG characterization based on automatic segmentation has been applied to sleep analysis. A nonparametric statistical approach for EEG segmentation was chosen, because it minimizes the need for a priori information about a signal. The method provides the detection of change-points i.e. boundaries between quasi-stationary EEG segments based on the EEG characteristics within four fundamental frequency bands (delta, theta, alpha and beta). Polysomnographic data of 18 healthy subjects were analyzed. Our findings show that nonparametric change-point segmentation in combination with cluster analysis enables us to obtain a clear pic…
The road to gold: Training and peaking characteristics in the year prior to a gold medal endurance performance
2014
Published version of an article in the journal PLoS ONE. Also available from the publisher at: http://dx.doi.org/10.1371/journal.pone.0101796 Open Access Purpose: To describe training variations across the annual cycle in Olympic and World Champion endurance athletes, and determine whether these athletes used tapering strategies in line with recommendations in the literature. Methods: Eleven elite XC skiers and biathletes (4 male; 28±1 yr, 85±5 mL. min-1. kg-1 V̇O2max, 7 female, 25±4 yr, 73±3 mL. min-1. kg-1 V̇O2max) reported one year of day-to-day training leading up to the most successful competition of their career. Training data were divided into periodization and peaking phases and dis…