Results 71 to 80 of about 1,030 (160)
International Endodontic Journal, EarlyView.ABSTRACT Background Computer vision methods based on artificial intelligence (AI) have found numerous applications in endodontic diagnosis and treatment planning. While most current applications employ discriminative deep learning models for detection and classification tasks, the field is now witnessing the rise of generative AI (GenAI), a class of AI
Hossein Mohammad‐Rahimi +6 more
wiley +1 more source
Journal of Numerical Analysis and Approximation Theory, 2015 We present a semilocal convergence study of Newton-like methods on a generalized Banach space setting to approximate a locally unique zero of an operator.
George A. Anastassiou +1 more
doaj +2 more sources
Cubo, 2011 We provide a semilocal convergence analysis for Newton-type methods to approximate a locally unique solution of a nonlinear equation in a Banach space setting. The Frechet-derivative of the operator involved is not necessarily continuous invertible. This
Ioannis K Argyros, Saïd Hilout
doaj
Cross‐Prudence and Optimal Prevention
International Economic Review, EarlyView.ABSTRACT In this paper, we study optimal prevention in the presence of a correlated nonfinancial background risk. Under positive correlation, cross‐prudence in the nonfinancial attribute reduces optimal prevention. We establish this result in the most direct extension of the standard prevention problem with binary marginal distributions and show that ...
Jingyuan Li, Richard Peter, Lin Zhou
wiley +1 more source
Stability Results for an Age-Structured SIS Epidemic Model with Vector Population
Journal of Applied Mathematics, 2015 We formulate an age-structured SIS epidemic model with periodic parameters, which includes host population and vector population. The host population is described by two partial differential equations, and the vector population is described by a single ...
He-Long Liu +2 more
doaj +1 more source
Connectivity and Selective Rural Migration
International Economic Review, EarlyView.ABSTRACT How does infrastructure shape rural development? Using household panel data from a nationally representative sample of Chinese villages, matched to high‐resolution highway maps, we find that road expansion between 2000 and 2015 operates mainly through reallocation, with less productive farmers scaling down or exiting and more productive ...
Lin Ma +3 more
wiley +1 more source
Density‐Valued ARMA Models by Spline Mixtures
Journal of Time Series Analysis, EarlyView.ABSTRACT This paper proposes a novel framework for modeling time series of probability density functions by extending autoregressive moving average (ARMA) models to density‐valued data. The method is based on a transformation approach, wherein each density function on a compact domain [0,1]d$$ {\left[0,1\right]}^d $$ is approximated by a B‐spline ...
Yasumasa Matsuda, Rei Iwafuchi
wiley +1 more source
Sequential Outlier Detection in Nonstationary Time Series
Journal of Time Series Analysis, EarlyView.ABSTRACT A novel method for sequential outlier detection in nonstationary time series is proposed. The method tests the null hypothesis of “no outlier” at each time point, addressing the multiple testing problem by bounding the error probability of successive tests, using extreme‐value theory. The asymptotic properties of the test statistic are studied
Florian Heinrichs +2 more
wiley +1 more source
Testing Distributional Granger Causality With Entropic Optimal Transport
Journal of Time Series Analysis, EarlyView.ABSTRACT We develop a novel nonparametric test for Granger causality in distribution based on entropic optimal transport. Unlike classical mean‐based approaches, the proposed method directly compares the full conditional distributions of a response variable with and without the history of a candidate predictor.
Tao Wang
wiley +1 more source
Moving Aggregate Modified Autoregressive Copula‐Based Time Series Models (MAGMAR‐Copulas)
Journal of Time Series Analysis, EarlyView.ABSTRACT Copula‐based time series models can model univariate and stationary time series in a flexible way by decomposing the joint distribution of consecutive observations into a copula and the stationary distribution. Implicitly, this approach assumes a finite Markov order. In reality, a time series may not follow the Markov property.
Sven Pappert
wiley +1 more source