Results 51 to 60 of about 24,650 (145)

Kernels for the Tangential Cauchy-Riemann Equations [PDF]

Transactions of the American Mathematical Society, 1980
On certain codimension one and codimension two submanifolds in C n {{\textbf {C}}^n} , we can solve the tangential Cauchy-Riemann equations ∂ ¯ b
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Standard Errors of Kernel Equating: Accounting for Bandwidth Estimation. [PDF]

Appl Psychol Meas, 2022
Marcq K, Andersson B.
europepmc   +1 more source

Integral equations with contrasting kernels

Electronic Journal of Qualitative Theory of Differential Equations, 2008
Summary: We study integral equations of the form \(x(t)=a(t)-\int^t_0 C(t,s)x(s)ds\) with sharply contrasting kernels typified by \(C^*(t,s)=\ln (e+(t-s))\) and \(D^*(t,s)=[1+(t-s)]^{-1}\). The kernel assigns a weight to \(x(s)\) and these kernels have exactly opposite effects of weighting. Each type is well represented in the literature.
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IRT Observed-score Kernel Equating

, 2014
Item response theory (IRT) observed-score kernel equating is described for thenon-equivalent groups with anchor test equating design using either chain equating orpost-stratification equating. The equating function is treated in a multivariate setting andthe asymptotic covariance matrices of IRT observed-score kernel equating functions arederived ...
Andersson, Björn, Wiberg, Marie
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A Comparative Evaluation of Kernel Equating and Test Characteristic Curve Equating. [PDF]

Appl Psychol Meas, 2018
De Ayala RJ, Smith B, Norman Dvorak R.
europepmc   +1 more source

Kernel Equating Under the Non-Equivalent Groups With Covariates Design. [PDF]

Appl Psychol Meas, 2015
Wiberg M, Bränberg K.
europepmc   +1 more source

Combining Propensity Scores and Common Items for Test Score Equating. [PDF]

Appl Psychol Meas
Laukaityte I, Wallin G, Wiberg M.
europepmc   +1 more source

Enhancement of intra-cardiac flow-field data using adaptive Kernel filtering. [PDF]

Sci Rep, 2023
Banerjee S, Ghosh A, Pal P.
europepmc   +1 more source

Common Persons Design in Score Equating: A Monte Carlo Investigation. [PDF]

Educ Psychol Meas
Liu J, Jiang Z, Zheng T, Han Y, Feng S.
europepmc   +1 more source

Using Deep Learning to Choose Optimal Smoothing Values for Equating. [PDF]

Appl Psychol Meas
Liu C, Cui Z.
europepmc   +1 more source