Results 71 to 80 of about 2,000,720 (315)

Spinal Cord Infarction Versus Idiopathic Transverse Myelitis: Clinical, Radiological, and Functional Insights From a Retrospective Cohort Study

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Introduction Spinal cord infarction (SCI) is a rare but devastating myelopathy, characterized by a high disability rate and an unfavorable prognosis. It has often been underdiagnosed and misdiagnosed as idiopathic transverse myelitis (ITM). This study aimed to describe the clinical features, radiological biomarkers, treatments, and functional ...
Zeqiang Ji   +13 more
wiley   +1 more source

An out-of-equilibrium model of the distributions of wealth

open access: yes, 2004
The distribution of wealth among the members of a society is herein assumed to result from two fundamental mechanisms, trade and investment. An empirical distribution of wealth shows an abrupt change between the low-medium range, that may be fitted by a ...
Picozzi, Sergio   +2 more
core   +2 more sources

Image Feature Extraction Using Symbolic Data of Cumulative Distribution Functions

open access: yesMathematics
Symbolic data analysis is an emerging field in statistics with great potential to become a standard inferential technique. This research introduces a new approach to image feature extraction using the empirical cumulative distribution function (ECDF) and
Sri Winarni   +3 more
doaj   +1 more source

Diffusion‐Weighted Imaging for the Evaluation of the Sacroiliac Joint in Pediatric Patients

open access: yesArthritis Care &Research, EarlyView.
Objective Maturational signal in the sacroiliac joint (SIJ) of skeletally immature youth is often misinterpreted as inflammation. Diagnostic tools that improve specificity are greatly needed. Apparent diffusion coefficient (ADC) values from diffusion‐weighted imaging (DWI), when used with standard imaging, may enhance diagnostic accuracy.
Michael L. Francavilla   +6 more
wiley   +1 more source

The asymptotic distribution of weighted empirical distribution functions

open access: yesStochastic Processes and their Applications, 1983
AbstractLet Gn denote the empirical distribution based on n independent uniform (0, 1) random variables. The asymptotic distribution of the supremum of weighted discrepancies between Gn(u) and u of the forms ‖wv(u)Dn(u)‖ and ‖wv(Gn(u))Dn(u)‖, where Dn(u) = Gn(u)−u, wv(u) = (u(1−u))−1+v and 0 ⩽ v < 12 is obtained.
openaire   +2 more sources

Energy Statistic-Based Goodness-of-Fit Test for the Lindley Distribution with Application to Lifetime Data

open access: yesStats
In this article, we propose a goodness-of-fit test for a one-parameter Lindley distribution based on energy statistics. The Lindley distribution has been widely used in reliability studies and survival analysis, especially in applied sciences.
Joseph Njuki, Ryan Avallone
doaj   +1 more source

Clinical Practice Guideline for Evaluation and Management of Peripheral Nervous System Manifestations in Sjögren's Disease

open access: yesArthritis Care &Research, Accepted Article.
Objectives Sjögren's disease is an autoimmune disorder that can impact multiple organ systems, including the peripheral nervous system (PNS). PNS manifestations, which can exist concurrently, include mononeuropathies, polyneuropathies, and autonomic nervous system neuropathies. To help patients and providers in the decision‐making process, we developed
Anahita Deboo   +19 more
wiley   +1 more source

Structure of shells in complex networks

open access: yes, 2009
In a network, we define shell $\ell$ as the set of nodes at distance $\ell$ with respect to a given node and define $r_\ell$ as the fraction of nodes outside shell $\ell$. In a transport process, information or disease usually diffuses from a random node
B. Bollobás   +12 more
core   +1 more source

On the Convergence of Empiric Distribution Functions

open access: yesThe Annals of Mathematical Statistics, 1955
Let $\mu$ be a probability measure on the Borel sets of $k$-dimensional Euclidean space $E_k.$ Let ${X_n}, n = 1, 2, \cdots,$ be a sequence of $k$-dimensional independent random vectors, distributed according to $\mu.$ For each $n = 1, 2, \cdots$ let $\mu_n$ be the empiric distribution function corresponding to $X_1, \cdots, X_n,$ i.e., for every Borel
openaire   +2 more sources

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