Results 211 to 220 of about 125,587 (258)

Psychometric properties of the Questionnaire for the Use of Public Transportation and Well-Being: Bayesian reliability, factorial invariance, and structural equation modeling evidence. [PDF]

Front Sociol
Baños-Chaparro J, Caycho-Rodríguez T, Valencia-Pecho D, Sarmiento-Suarez E, Bernabel-Tarazona D, Rivera-Álvarez G.   +5 more
europepmc   +1 more source

Cross-Dataset Generalization of Deep Learning-Based Detectors for Intracranial Hemorrhage Subtype Localization on Noncontrast Head CT: A Comparative Study. [PDF]

Diagnostics (Basel)
Lee CH, Muzakky H, Juan CE, Chang CC, Li YH, Lee TY, Juan CH, Tsai MT, Juan CJ.   +8 more
europepmc   +1 more source

A Two-Stage EEG Microstate Fusion Framework for Dementia Screening and Alzheimer's Disease/Frontotemporal Dementia Differentiation. [PDF]

Biosensors (Basel)
Jiang L, Chen Y, He Y, Liang J, Zhao X, Guo X.   +5 more
europepmc   +1 more source

Validation of the Persian NDDI-E for Depression Screening in Adolescents with Epilepsy. [PDF]

Iran J Child Neurol
Haghi S, Barzegar M, Hazrati H, Baroughi M, Rezazadeh K.   +4 more
europepmc   +1 more source

Demoralization and Associated Factors in Palliative Oncology Outpatients: A Cross-Sectional Study. [PDF]

Psychooncology
Ljuslin M, Chytas V, Gaignard ME, Gorman D, Lally K, Mazzola E, Nigam K, Roxanne S, Sager Z, Yusufov M, Pautex S, Tulsky JA, Beaussant Y.   +12 more
europepmc   +1 more source

Prediction of cognitive performance by demographics, sleep, and brain morphometry: machine learning findings from ENIGMA-Sleep Working Group

Tahmasian M, Bi H, Åkerstedt T, Bülow R, Deantoni M, Drzezga A, Elman J, Elmenhorst D, Elmenhorst E, Ewert R, Fennema-Notestine C, Ferrarelli F, Frenzel S, Von Gall C, Genon S, Grabe H, Ho PTN, Hoepel S, Hoffstaedter F, Ibáñez A, Jahanshad N, Keihani A, Küppers V, Liu W, Mayeli A, Mortazavi N, Neitzel J, Nilsonne G, Panizzon M, Rupp J, Saberi A, Schmidt C, Spiegelhalder K, Stubbe B, Tamm S, Thomopoulos S, Thompson P, Valk S, Vandewalle G, Vo-Eckerle TT, Völzke H, Waite L, Wexler J, Wittfeld K, Patil K, Weihs A, Eickhoff S, Raimondo F.   +47 more
europepmc   +1 more source

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A note on generalized cross-validation with replicates

Statistics and Probability Letters, 1992
Abstract Generalized cross-validation (GCV) is a popular method for choosing the smoothing parameter in generalized spline smoothing when there are independent errors with common unknown variance. When data points are replicated, one can choose the smoothing parameter by minimizing one of three functions: the GCV score computed from the averaged ...
Chong Gu, Nancy E Heckman, Grace Wahba
exaly   +3 more sources

Generalized Cross-Validation for Large-Scale Problems

Journal of Computational and Graphical Statistics, 1997
Abstract Although generalized cross-validation is a popular tool for calculating a regularization parameter, it has been rarely applied to large-scale problems until recently. A major difficulty lies in the evaluation of the cross-validation function that requires the calculation of the trace of an inverse matrix. In the last few years stochastic trace
Gene H Golub
exaly   +2 more sources

Generalized cross-validation for covariance model selection

Mathematical Geosciences, 1995
A weighted cross-validation technique known in the spline literature as generalized cross-validation (GCV), is proposed for covariance model selection and parameter estimation. Weights for prediction errors are selected to give more importance to a cluster of points than isolated points.
Denis Marcotte, Marcotte Denis
exaly   +2 more sources

Generalized cross validation for wavelet thresholding

Signal Processing, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Maarten Jansen, Maurits Malfait, Adhemar Bultheel   +2 more
openaire   +4 more sources