242 research outputs found
Structure Identification in Panel Data Analysis
Panel data analysis is an important topic in statistics and econometrics. In such analysis, it is very common to assume the impact of a covariate on the response variable remains constant across all individuals. While the modelling based on this assumption is reasonable when only the global effect is of interest, in general, it may overlook some individual/subgroup attributes of the true covariate impact. In this paper, we propose a data driven approach to identify the groups in panel data with interactive effects induced by latent variables. It is assumed that the impact of a covariate is the same within each group, but different between the groups. An EM based algorithm is proposed to estimate the unknown parameters, and a binary segmentation based algorithm is proposed to detect the grouping. We then establish asymptotic theories to justify the proposed estimation, grouping method, and the modelling idea. Simulation studies are also conducted to compare the proposed method with the existing approaches, and the results obtained favour our method. Finally, the proposed method is applied to analyse a data set about income dynamics, which leads to some interesting findings
Anisotropic flow and the valence quark skeleton of hadrons
We study transverse momentum anisotropies, in particular, the elliptic flow
due to the interference effect sourced by valence quarks in high-energy
hadron-hadron collisions. Our main formula is derived as the high-energy
(eikonal) limit of the impact-parameter dependent cross section in quantum
field theory, which agrees with that in terms of the impact parameter in the
classical picture. As a quantitative assessment of the interference effect, we
calculate in the azimuthal distribution of gluons at a comprehensive
coverage of the impact parameter and the transverse momentum in high-energy
pion-pion collisions. In a broad range of the impact parameter, a sizable
amount of , comparable with that produced due to saturated dense gluons or
final-state interactions, is found to develop. In our calculations, the valence
sector of the pion wave function is obtained numerically from the Basis
Light-Front Quantization, a non-perturbative light-front Hamiltonian approach.
And our formalism is generic and can be applied to other small collision
systems like proton-proton collisions
Multi-step prediction of chlorophyll concentration based on Adaptive Graph-Temporal Convolutional Network with Series Decomposition
Chlorophyll concentration can well reflect the nutritional status and algal
blooms of water bodies, and is an important indicator for evaluating water
quality. The prediction of chlorophyll concentration change trend is of great
significance to environmental protection and aquaculture. However, there is a
complex and indistinguishable nonlinear relationship between many factors
affecting chlorophyll concentration. In order to effectively mine the nonlinear
features contained in the data. This paper proposes a time-series decomposition
adaptive graph-time convolutional network ( AGTCNSD ) prediction model.
Firstly, the original sequence is decomposed into trend component and periodic
component by moving average method. Secondly, based on the graph convolutional
neural network, the water quality parameter data is modeled, and a parameter
embedding matrix is defined. The idea of matrix decomposition is used to assign
weight parameters to each node. The adaptive graph convolution learns the
relationship between different water quality parameters, updates the state
information of each parameter, and improves the learning ability of the update
relationship between nodes. Finally, time dependence is captured by time
convolution to achieve multi-step prediction of chlorophyll concentration. The
validity of the model is verified by the water quality data of the coastal city
Beihai. The results show that the prediction effect of this method is better
than other methods. It can be used as a scientific resource for environmental
management decision-making.Comment: 12 pages, 10 figures, 3 tables, 45 reference
Investigation of uncoordinated coexisting IEEE 802.15.4 networks with sleep mode for machine-to-machine communications
The low-energy consumption of IEEE 802.15.4 networks makes it a strong candidate for machine-to-machine (M2M) communications. As multiple M2M applications with 802.15.4 networks may be deployed closely and independently in residential or enterprise areas, supporting reliable and timely M2M communications can be a big challenge especially when potential hidden terminals appear. In this paper, we investigate two scenarios of 802.15.4 network-based M2M communication. An analytic model is proposed to understand the performance of uncoordinated coexisting 802.15.4 networks. Sleep mode operations of the networks are taken into account. Simulations verified the analytic model. It is observed that reducing sleep time and overlap ratio can increase the performance of M2M communications. When the networks are uncoordinated, reducing the overlap ratio can effectively improve the network performance
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