Results 31 to 40 of about 1,737,261 (319)

Analysis of uncertainty measure using unified hybrid censored data with applications

Journal of Taibah University for Science, 2021
Entropy is a measure of random variable uncertainty that reflects the anticipated quantity of information. In this paper, estimation of Shannon entropy for Lomax distribution, viz unified hybrid censored data are considered.
Baria A. Helmy, Amal S. Hassan, Ahmed K. El-Kholy   +2 more
doaj   +1 more source

On estimation with balanced loss functions

Statistics & Probability Letters, 1999
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Department of Statistics, University of Connecticut, Storrs, CT 06269, USA ( host institution ), Dey, Dipak K ( author ), Ghosh, Malay ( author ), Strawderman, William E ( author )   +3 more
openaire   +2 more sources

Covariate balancing propensity score by tailored loss functions [PDF]

The Annals of Statistics, 2019
28 pages, 2 figures, 3 ...
openaire   +4 more sources

A gesture recognition algorithm based on improved YOLOv3

Journal of Hebei University of Science and Technology, 2021
In order to solve the problems of low recognition accuracy and easily affected by illumination conditions in the gesture recognition, an improved YOLOv3 gesture recognition algorithm was proposed.
Bingdong SUI, Pai ZHANG, Xiaojun WANG
doaj   +1 more source

Bayesian and Non-Bayesian Inference for the Generalized Pareto Distribution Based on Progressive Type II Censored Sample

Mathematics, 2018
In this paper, first we consider the maximum likelihood estimators for two unknown parameters, reliability and hazard functions of the generalized Pareto distribution under progressively Type II censored sample. Next, we discuss the asymptotic confidence
Xuehua Hu, Wenhao Gui
doaj   +1 more source

YOLOv3 Detection Algorithm Based on the Improved Bounding Box Regression Loss [PDF]

Jisuanji gongcheng, 2022
The bounding box regression loss function in the YOLOv3 detection algorithm is sensitive to the bounding box scale but does not have a strong correlation with the optimization of the algorithm detection effect evaluation standard Intersection over Union ...
SHEN Jiquan, CHEN Xiangjun, ZHAI Haixia
doaj   +1 more source

Minimax estimator of regression coefficient in normal distribution under balanced loss function [PDF]

, 1990
This article investigates linear minimax estimators of regression coefficient in a linear model with an assumption that the underlying distribution is a normal one with a nonnegative definite covariance matrix under a balanced loss function.
Guikai Hu, Qingguo Li, Ping Peng, Alam, Cao, Efron, Giles, Gruber, Hu, Jozani, Ohtani, Ohtani, Ohtani, Rao, Rodrigues, Wu, Xu, Xu, Yu, Yu, Zellner   +20 more
core   +2 more sources

NeighborLoss: A Loss Function Considering Spatial Correlation for Semantic Segmentation of Remote Sensing Image

IEEE Access, 2021
House segmentation of remote sensing image based on deep learning has become the main segmentation method because it can automatically extract features.
Wei Yuan, Wenbo Xu
doaj   +1 more source

Synaptic dysfunction, memory deficits and hippocampal atrophy due to ablation of mitochondrial fission in adult forebrain neurons [PDF]

, 2015
Well-balanced mitochondrial fission and fusion processes are essential for nervous system development. Loss of function of the main mitochondrial fission mediator, dynamin-related protein 1 (Drp1), is lethal early during embryonic development or around ...
A Eckert, A Grimm, A Schmidt, A Sudarov, AI Nesvizhskii, AM Bliek, AY Abramov, B Oettinghaus, B Oettinghaus, C Savoia, C Tian, EC Cheung, G Erdmann, G Twig, G-L McLelland, GF Kelso, GM Cereghetti, GSB Williams, H Chen, H Li, H Otera, H Yuan, HR Waterham, HT Bui, J Bischofberger, J Grohm, J Hench, J M Schulz, J Nunnari, J Wakabayashi, JK Kern, JR Friedman, K Meuer, K Mihara, K Schmitt, KW Young, L M Restelli, L Morè, L Scorrano, M Beck, M Licci, M Tolnay, ME Ragozzino, MJ Barsoum, N Ishihara, N Ishihara, N Mizushima, N Zhang, OM De Brito, P D'Adamo, P Franken, P Verstreken, RK Dagda, S Cipolat, S Frank, S Preibisch, SW Park, T Glatter, T Koshiba, T Uo, V Costa, V Rhein, X Wang, Y Kageyama, YJ Lee, Z-H Sheng   +65 more
core   +2 more sources

Boosting Minority Class Prediction on Imbalanced Point Cloud Data

Applied Sciences, 2020
Data imbalance during the training of deep networks can cause the network to skip directly to learning minority classes. This paper presents a novel framework by which to train segmentation networks using imbalanced point cloud data.
Hsien-I Lin, Mihn Cong Nguyen
doaj   +1 more source