Results 1 to 10 of about 5,238,446 (200)
Hyperspectral imaging and robust statistics in non-melanoma skin cancer analysis. [PDF]
Biomed Opt Express, 2021 Non-Melanoma skin cancer is one of the most frequent types of cancer. Early detection is encouraged so as to ensure the best treatment, Hyperspectral imaging is a promising technique for non-invasive inspection of skin lesions, however, the optimal ...
Courtenay LA +13 more
europepmc +2 more sources
, 2011 The first example involves the real data given in Table 1 which are the results of an interlaboratory test. The boxplots are shown in Fig. 1 where the dotted line denotes the mean of the observations and the solid line the median. We note that only the results of the Laboratories 1 and 3 lie below the mean whereas all the remaining laboratories return ...
Gather, Ursula, Davies, P. Laurie
core +8 more sources
Introducing Robust Statistics in the Uncertainty Quantification of Nuclear Safeguards Measurements [PDF]
Entropy, 2022 The monitoring of nuclear safeguards measurements consists of verifying the coherence between the operator declarations and the corresponding inspector measurements on the same nuclear items.
Andrea Cerasa
doaj +2 more sources
Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool? [PDF]
Sensors (Basel), 2019 Navigation problems are generally solved applying least-squares (LS) adjustments. Techniques based on LS can be shown to perform optimally when the system noise is Gaussian distributed and the parametric model is accurately known.
Medina D, Li H, Vilà-Valls J, Closas P.
europepmc +2 more sources
, 2018 In lieu of an abstract, here is the entry's first paragraph: Robust statistics are procedures that maintain nominal Type I error rates and statistical power in the presence of violations of the assumptions that underpin parametric inferential statistics.
Blaine, Bruce E
core +6 more sources
Anomaly Detection by Robust Statistics [PDF]
WIREs Data Mining Knowl. Discov., 2017 Real data often contain anomalous cases, also known as outliers. These may spoil the resulting analysis but they may also contain valuable information. In either case, the ability to detect such anomalies is essential.
Hubert, Mia, Rousseeuw, Peter J.
core +2 more sources
A Robust Statistics Approach to Minimum Variance Portfolio Optimization [PDF]
IEEE Transactions on Signal Processing, 2015 We study the design of portfolios under a minimum risk criterion. The performance of the optimized portfolio relies on the accuracy of the estimated covariance matrix of the portfolio asset returns.
Couillet, Romain +2 more
core +6 more sources
Robust Lifetime Estimation from HPGe Radiation-Sensor Time Series Using Pairwise Ratios and MFV Statistics [PDF]
SensorsHigh-purity germanium (HPGe) gamma-ray detectors are core instruments in nuclear physics and astrophysics experiments, where long-term stability and reliable extraction of decay parameters are essential.
Victor V. Golovko
doaj +2 more sources
Learning Robust Statistics for Simulation-based Inference under Model Misspecification [PDF]
Neural Information Processing Systems, 2023 Simulation-based inference (SBI) methods such as approximate Bayesian computation (ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating statistics to infer parameters of intractable likelihood models. However, such methods
Daolang Huang +4 more
semanticscholar +1 more source