Results 51 to 60 of about 4,564 (243)

Advances in causal discovery methods for ecological time series

Biological Reviews, EarlyView.
ABSTRACT Recent advances in data collection technologies (e.g. automated sensor networks, satellite remote sensing, and high‐throughput sequencing) have greatly expanded the availability of ecological time series, enabling new opportunities for causal analyses in dynamic ecosystems.
Kenta Suzuki, Masato Yamamichi, Yutaka Osada, Masayuki Ushio, Kohei Nakajima, Hiroshi Masuya, Shohei Shimizu   +6 more
wiley   +1 more source

Vine copula knockoffs for variable selection in gene expression studies

Canadian Journal of Statistics, EarlyView.
Abstract Identifying clinical and genetic markers is essential for stratifying cancer patients by survival outcomes and guiding personalized treatment strategies. However, gene expression studies often involve high‐dimensional predictors with mixed data types and complex dependence, which complicates reliable variable selection.
José Ulises Márquez Urbina, Alejandro Román Vásquez, Graciela González Farías, Gabriel Escarela   +3 more
wiley   +1 more source

Markov Kernels Local Aggregation for Noise Vanishing Distribution Sampling

SIAM Journal on Mathematics of Data Science, 2022
A novel strategy that combines a given collection of $π$-reversible Markov kernels is proposed. At each Markov transition, one of the available kernels is selected via a state-dependent probability distribution. In contrast to random-scan type approaches that assume a constant (i.e.
Maire, Florian, Vandekerkhove, Pierre
openaire   +4 more sources

A New Implementation of Network GARCH Model for Stock Volatility and Co‐Volatility Forecasting

Journal of Forecasting, EarlyView.
ABSTRACT Volatility clustering and spillovers are key features of financial time series with many cross‐sectional assets. While network analysis links similar or correlated stocks and helps trace volatility spillovers, contemporary multivariate ARCH‐GARCH formulations struggle to represent structured network dependence and remain parsimonious.
Peiyi Zhou
wiley   +1 more source

Forecasting Duration in High‐Frequency Financial Data Using a Self‐Exciting Flexible Residual Point Process

Journal of Forecasting, EarlyView.
ABSTRACT This paper presents a method for forecasting limit order book durations using a self‐exciting flexible residual point process. High‐frequency events in modern exchanges exhibit heavy‐tailed interarrival times, posing a significant challenge for accurate prediction.
Kyungsub Lee
wiley   +1 more source

Heavy Tailed Approximate Identities and σ-stable Markov Kernels [PDF]

Potential Analysis, 2017
The aim of this paper is to present some results relating the properties of stability, concentration and approximation to the identity of convolution through not necessarily mollification type families of heavy tailed Markov kernels. A particular case is provided by the kernels $K_t$ obtained as the $t$ mollification of $L^{σ(t)}$ selected from the ...
Aimar, Hugo Alejandro, Gomez, Ivana Daniela, Morana, Federico Maximiliano   +2 more
openaire   +3 more sources

Technological Evolution in Fintech: A Decadal Scientometric and Systematic Review of Developments and Criticisms

International Journal of Finance &Economics, EarlyView.
ABSTRACT This study aims to classify pivotal fintech innovations and explore the prospects and pitfalls associated with emerging fintech services extensively discussed in the literature. We conducted a multistage systematic review of research published on fintech over the past decade from a technological perspective. Using the Preferred Reporting Items
Muhammad Imran Qureshi, Nohman Khan
wiley   +1 more source

Industry Portfolio Volatility Connections and Industry Portfolio Returns

International Journal of Finance &Economics, EarlyView.
ABSTRACT This paper tracks dynamic connections that form among daily US industry portfolio return volatilities using a Bayesian time‐varying parameter VAR model. Market participants often focus on sectors to filter vast amounts of information, and this focus results in cross‐industry return predictability. We characterise connections that form over the
Michael Ellington, Michalis Stamatogiannis, Yawen Zheng   +2 more
wiley   +1 more source

Artificial intelligence driven diet‐intestinal microbiota‐host health integration: A four‐dimensional paradigm for advancing host wellness research

iMetaOmics, EarlyView.
This graphical abstract presents a novel Diet‐Artificial Intelligence (AI)‐intestinal Microbiota–Host Health paradigm integrating microbiota‐centric pathways and an AI‐driven multi‐omics workflow. Diet shapes gut bacteria that regulate intestinal mucosal integrity, immune signaling, and gut–brain neurotransmitters to influence host health.
Tianle He, Shuobo Zhang, Jiaxin Chen, Xiaoling Zheng, Jixin Zhao, Huifeng Li, Jiani Mao, Ju Luo, Jundan Zheng, Dengjun Ma, Lihong Wang, Junhui Liu, Wen Tian, Shuangming Yang, Chengli Liu, Ke Tian, Zhenguo Yang   +16 more
wiley   +1 more source

A Hybrid Large-Kernel CNN and Markov Feature Framework for Remaining Useful Life Prediction

Machines
Remaining Useful Life (RUL) prediction has become a crucial component in predictive maintenance and condition-based operation with the rapid advancement of industrial automation and the increasing complexity of mechanical systems.
Yuke Wang, Che Su, Peng Wang, Junquan Zhen, Dong Wang   +4 more
doaj   +1 more source