Results 201 to 210 of about 248,461 (262)

Longitudinal Functional Outcomes Among Survivors of Childhood Lower Extremity Osteosarcoma. [PDF]

Cancers (Basel)
Berkman AM, Wogksch MD, Partin RE, Wilson T, Neel MD, Bishop MW, Daw NC, Navid F, Federico SM, Srivastava D, Hudson MM, Ness KK.   +11 more
europepmc   +1 more source

Machine learning-based temperature prediction across diverse ecosystems for the Boro Season in Bangladesh. [PDF]

Sci Rep
Rahman NMF, Haque N, Asadullah M, Hossain ABMZ, Hossain MS, Uddin MJ, Hassan SMQ, Baten MA.   +7 more
europepmc   +1 more source

Neural Network Technologies for Age Estimation in Children from Orthopantomograms (a Pilot Study). [PDF]

Sovrem Tekhnologii Med
Poletaeva MP, Vasilevsky YV, Valetov DK, Angelakopoulos N, Zolotenkova GV.   +4 more
europepmc   +1 more source

An ANN-based non-destructive model for age and maturity estimation of the data-deficient Goldblotch Grouper (<i>Epinephelus costae</i>): a practical tool for data-poor fisheries in conflict-affected Mediterranean coasts. [PDF]

PeerJ
Hamwi NI.
europepmc   +1 more source

Predicting the efficacy of recombinant human thrombopoietin in treating cancer therapy-related thrombocytopenia: based on stacking ensemble methods. [PDF]

Front Oncol
Hou K, Huangfu R, Guo Z, Gao Y, Lu H, Li Z, Wang Z.   +6 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Exact mean and mean squared error of the smoothed bootstrap mean integrated squared error estimator

Computational Statistics, 2000
Let \(X_1, X_2, \ldots, X_n\) be independent and identically distributed with density \(f\), and set \(X=\) \(\{ X_1, \ldots, X_n \}.\) \(\phi\) denotes the standard normal density and for \(\sigma >0\) let \(\phi(x, \sigma^2) = \sigma^{-1}\phi(x\sigma^{-1}).\) The authors consider kernel estimators for \(f\): the Gaussian kernel estimator with ...
Dominic Lee, Carey E. Priebe
openaire   +2 more sources

Mean Square Error Estimation in Thresholding

IEEE Signal Processing Letters, 2011
We present a novel approach to estimating the mean square error (MSE) associated with any given threshold level in both hard and soft thresholding. The estimate is provided by using only the data that is being thresholded. This adaptive approach provides probabilistic confidence bounds on the MSE.
Soosan Beheshti, Masoud Hashemi, Ervin Sejdic, Tom Chau   +3 more
openaire   +1 more source