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simple kernel density estimator  (Statistical Sciences Inc)

 
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    Statistical Sciences Inc simple kernel density estimator
    Simple Kernel Density Estimator, supplied by Statistical Sciences Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/kernel+density+estimation+analysis/simple+kernel+density+estimator/10__2307_slash_3147159-100-31-48
    Average 90 stars, based on 1 article reviews
    simple kernel density estimator - by Bioz Stars, 2026-10
    90/100 stars

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    Article Title: Contingent Valuation and Revealed Preference Methodologies: Comparing the Estimates for Quasi-Public Goods
    Article Snippet: INTERVAL [0.81-.96] MEDIAN = .75 N = 616z u 0.4- 0.2- 0.0- 0 1 2 3 4 5 6 RATIO OF CONTINGENT VALUATION TO REVEALED PREFERENCE STIMATE FIGURE 1 COMPLETE DATASET: NONPARAMETRIC DENSITY ESTIMATE simple kernel density estimator first proposed by Wegman (1972; see also Silverman 1986, and Statistical Sciences, Inc. 1993) with a width parameter of 0.5.



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    Intraoperative direct electrical stimulation sites . All stimulation sites for Patients 1–6 ( A – F ) are visualized on a 3D reconstruction of the anterior bank of their central sulcus and combined on the International Consortium for Brain Mapping (ICBM) 152 central sulcus template ( G ). Effector-specific sites are displayed in blue, while inter-effector sites [somato-cognitive action network (SCAN)] are displayed in red. H displays the spatial relationship between the inter-effector sites and the plis de passage (PDP) Montreal Neurological Institute (MNI) coordinates ( top ) and their probability density estimation ( bottom ). The three coloured circles in the top image represent the same PDP sites as in . ( I ) Box plot showing the probability of overlap between stimulation sites [inter-effector (red) versus single-effector (blue)] for the two subsectors (anterior, posterior) of area 4. Mann–Whitney U-test; not significant, ns: P ≥ 0.05.

    Journal: Brain

    Article Title: White matter connections within the central sulcus subserving the somato-cognitive action network

    doi: 10.1093/brain/awaf022

    Figure Lengend Snippet: Intraoperative direct electrical stimulation sites . All stimulation sites for Patients 1–6 ( A – F ) are visualized on a 3D reconstruction of the anterior bank of their central sulcus and combined on the International Consortium for Brain Mapping (ICBM) 152 central sulcus template ( G ). Effector-specific sites are displayed in blue, while inter-effector sites [somato-cognitive action network (SCAN)] are displayed in red. H displays the spatial relationship between the inter-effector sites and the plis de passage (PDP) Montreal Neurological Institute (MNI) coordinates ( top ) and their probability density estimation ( bottom ). The three coloured circles in the top image represent the same PDP sites as in . ( I ) Box plot showing the probability of overlap between stimulation sites [inter-effector (red) versus single-effector (blue)] for the two subsectors (anterior, posterior) of area 4. Mann–Whitney U-test; not significant, ns: P ≥ 0.05.

    Article Snippet: The region of highest probability of evoking MEPs was computed using a modified in-house version of probability kernel density estimation (PDE analysis) implemented in MATLAB.

    Techniques: MANN-WHITNEY

    Box plot and kernel density estimation of synergistic values of AGE and AER in Northeast China

    Journal: Environmental Science and Pollution Research International

    Article Title: Synergistic study of the green efficiency and economic resilience of agriculture from a sustainable development perspective: evidence from Northeast China

    doi: 10.1007/s11356-023-27697-5

    Figure Lengend Snippet: Box plot and kernel density estimation of synergistic values of AGE and AER in Northeast China

    Article Snippet: In this paper, the kernel density estimation analysis of the synergy values during the study period was performed using Matlab R2020b software (Fig. b).

    Techniques: