Results 61 to 70 of about 5,741 (199)
Robust recovery of missing data in electricity distribution systems [PDF]
, 2017 The advanced operation of future electricity distribution systems is likely to require significant observability of the different parameters of interest (e.g., demand, voltages, currents, etc.). Ensuring completeness of data is, therefore, paramount.
Coca, D. +4 more
core +4 more sources
Where's the beef? The feminisation of weight‐loss dieting in Britain and Scandinavia c.1890–1925
Gender &History, EarlyView.Abstract Representations of the slim body have traditionally been at the centre of scholarly interest in dieting culture, whereas food often remains a shadowy presence compared with more persistent themes of body discipline, slenderness and anti‐fat messages.
Emma Hilborn
wiley +1 more source
Hybrid Transform Based Denoising with Block Thresholding
مجلة كلية التربية للبنات, 2019 A frequently used approach for denoising is the shrinkage of coefficients of the noisy signal representation in a transform domain. This paper proposes an algorithm based on hybrid transform (stationary wavelet transform proceeding by slantlet transform);
Iman M.G. Alwan
doaj
Generalized SURE for Exponential Families: Applications to Regularization
, 2008 Stein's unbiased risk estimate (SURE) was proposed by Stein for the independent, identically distributed (iid) Gaussian model in order to derive estimates that dominate least-squares (LS).
Eldar, Yonina C.
core +4 more sources
American Journal of Political Science, EarlyView.Abstract How can defense alliances reap the efficiency gains of working together when coordination and opportunism costs are high? Although specializing as part of a collective comes with economic and functional benefits, states must bargain over the distribution of those gains and ensure the costs of collective action are minimized.
J. Andrés Gannon
wiley +1 more source
Stein's unbiased risk estimate and Hyvärinen's score matching
Given a collection of observed signals corrupted with Gaussian noise, how can we learn to optimally denoise them? This fundamental problem arises in both empirical Bayes and generative modeling. In empirical Bayes, the predominant approach is via nonparametric maximum likelihood estimation (NPMLE), while in generative modeling, score matching (SM ...
Ghosh, Sulagna +3 more
openaire +2 more sources
Adaptive Monotone Shrinkage for Regression [PDF]
, 2015 We develop an adaptive monotone shrinkage estimator for regression models with the following characteristics: i) dense coefficients with small but important effects; ii) a priori ordering that indicates the probable predictive importance of the features.
Foster, Dean, Ma, Zhuang, Stine, Robert
core
Adaptive density estimation for stationary processes [PDF]
, 2009 We propose an algorithm to estimate the common density $s$ of a stationary process $X_1,...,X_n$. We suppose that the process is either $\beta$ or $\tau$-mixing.
C. L. Mallows +20 more
core +5 more sources
Addressing environmental misperceptions for nature recovery
Conservation Biology, EarlyView.Abstract A poorly understood and systemic challenge to global conservation agreements is shifting baseline syndrome (SBS), wherein people misperceive the extent to which nature has changed. This can diminish societal expectations for nature recovery. We broadened the conceptual framing of SBS beyond the more common elements of nature loss to include ...
Shuo Gao +3 more
wiley +1 more source
مجلة كلية التربية للبنات, 2019 NeighShrink is an efficient image denoising algorithm based on the discrete wavelet transform (DWT). Its disadvantage is to use a suboptimal universal threshold and identical neighbouring window size in all wavelet subbands.
ايمان محمد جعفر علوان
doaj