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  <title>BURA Collection:</title>
  <link rel="alternate" href="http://bura.brunel.ac.uk/handle/2438/13038" />
  <subtitle />
  <id>http://bura.brunel.ac.uk/handle/2438/13038</id>
  <updated>2026-04-18T12:22:34Z</updated>
  <dc:date>2026-04-18T12:22:34Z</dc:date>
  <entry>
    <title>Jiangfeng Wang, Keming Yu and Rong Jiang's contribution to the Discussion of ‘Augmented balancing weights as linear regression’ by Bruns-Smith et al</title>
    <link rel="alternate" href="http://bura.brunel.ac.uk/handle/2438/32954" />
    <author>
      <name>Wang, J</name>
    </author>
    <author>
      <name>Yu, K</name>
    </author>
    <author>
      <name>Jiang, R</name>
    </author>
    <id>http://bura.brunel.ac.uk/handle/2438/32954</id>
    <updated>2026-03-11T03:01:05Z</updated>
    <published>2026-01-13T00:00:00Z</published>
    <summary type="text">Title: Jiangfeng Wang, Keming Yu and Rong Jiang's contribution to the Discussion of ‘Augmented balancing weights as linear regression’ by Bruns-Smith et al
Authors: Wang, J; Yu, K; Jiang, R
Abstract: This is an interesting and well-executed paper that makes a substantial contribution to the literature on semiparametric causal inference, elegantly bridging the seemingly distinct ﬁelds of balancing weights and regression adjustment. ...
Description: Discussion Paper Contribution.</summary>
    <dc:date>2026-01-13T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>A communication-efficient distributed Retire with application to the analysis of multi-site air-quality distributed data</title>
    <link rel="alternate" href="http://bura.brunel.ac.uk/handle/2438/32718" />
    <author>
      <name>Yu, K</name>
    </author>
    <author>
      <name>Jiang, R</name>
    </author>
    <author>
      <name>Wang, J</name>
    </author>
    <id>http://bura.brunel.ac.uk/handle/2438/32718</id>
    <updated>2026-03-09T18:53:16Z</updated>
    <published>2026-02-18T00:00:00Z</published>
    <summary type="text">Title: A communication-efficient distributed Retire with application to the analysis of multi-site air-quality distributed data
Authors: Yu, K; Jiang, R; Wang, J
Abstract: A multi-site city air-quality dataset should be considered distributed data as it is generated from multiple geographically dispersed sources, such as air quality sensors or monitoring stations. In various fields, distributed systems are increasingly employed to handle data collected from diverse sources, often resulting in datasets that are heavy-tailed, asymmetric, or heterogeneous. Robust expectile regression combines the computational efficiency of expectile regression with its robustness in handling heavy-tailed response distributions and outliers. This paper extends robust expectile regression to communication-efficient distributed systems and applies it to the analysis of multi-site air-quality datasets. The proposed distributed estimators achieve both computational and communication efficiency while delivering statistical performance comparable to global estimators, as demonstrated through both theoretical analysis and numerical experiments.
Description: Data availability: &#xD;
The air-quality data from the Beijing Municipal Environmental Monitoring Center is available from online site: https://archive.ics.uci.edu/dataset/501/beijing+multi+site+air+quality+data .; Supplementary material: &#xD;
Supplementary data are available online at:  https://academic.oup.com/jrsssc/advance-article/doi/10.1093/jrsssc/qlag005/8489473#supplementary-data .</summary>
    <dc:date>2026-02-18T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>A spatiotemporal marginalized zero-inflated Conway–Maxwell–Poisson regression model: application to international population outmigration within Asia</title>
    <link rel="alternate" href="http://bura.brunel.ac.uk/handle/2438/32671" />
    <author>
      <name>Zhang, L</name>
    </author>
    <author>
      <name>Tian, M</name>
    </author>
    <author>
      <name>Yu, K</name>
    </author>
    <author>
      <name>Zhou, M</name>
    </author>
    <id>http://bura.brunel.ac.uk/handle/2438/32671</id>
    <updated>2026-03-09T18:22:43Z</updated>
    <published>2026-02-05T00:00:00Z</published>
    <summary type="text">Title: A spatiotemporal marginalized zero-inflated Conway–Maxwell–Poisson regression model: application to international population outmigration within Asia
Authors: Zhang, L; Tian, M; Yu, K; Zhou, M
Abstract: Asia is a principal source of global migration, and its intra-regional movements profoundly reshape the political, economic, and ecological landscapes of Asian nations. To address the spatiotemporal zero-inflated and dispersion present in migration data, as well as the need for interpretable inference on the overall mean, we develop a spatiotemporal marginalized zero-inflated Conway–Maxwell–Poisson (MZICMP) regression model. This model transcends the limitations of conventional zero-inflated approaches by employing a dispersion parameter that accommodates equidispersion, overdispersion, and under dispersion, and by jointly modelling excess zeros and the marginal mean through the inclusion of country-level covariates, smooth temporal effects, and spatial random effects. For parameter estimation, we implement a Bayesian Markov Chain Monte Carlo algorithm that combines Gibbs sampling with Metropolis–Hastings steps. Simulation demonstrates the model's efficacy in capturing both temporal autocorrelation and spatial zero-inflation patterns, and an empirical application to 1990–2020 intra-Asian out-migration reveals: (1) the share of secondary industry and the share of tertiary industry both show significant negative correlations with out-migration flows, whereas battle-related deaths and the total volume of bilateral trade exhibit positive correlations; (2) the average outmigration trend among Asian countries was relatively high during the period 2005–2010, then declined in 2015–2020; the model results indicate a satisfactory capture of this temporal pattern.
Description: Data availability: &#xD;
The data were obtained primarily from the United Nations Population Division (https://population.un.org), the World Bank (https://data.worldbank.org.cn/indicator), and the UCDP database (https://www.pcr.uu.se/research/ucdp). All datasets and codes in this study are available from the corresponding author upon reasonable request.; Supplementary material: &#xD;
Supplementary data are available online at: https://academic.oup.com/jrsssa/advance-article/doi/10.1093/jrsssa/qnag009/8462573?login=true&amp;guestAccessKey=#supplementary-data .</summary>
    <dc:date>2026-02-05T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Sequential optimality conditions and solution existence for nonsmooth multiobjective optimization problems</title>
    <link rel="alternate" href="http://bura.brunel.ac.uk/handle/2438/32649" />
    <author>
      <name>Anh, NLH</name>
    </author>
    <author>
      <name>Hung, NC</name>
    </author>
    <author>
      <name>Chuong, TD</name>
    </author>
    <id>http://bura.brunel.ac.uk/handle/2438/32649</id>
    <updated>2026-01-16T03:00:49Z</updated>
    <published>2025-12-23T00:00:00Z</published>
    <summary type="text">Title: Sequential optimality conditions and solution existence for nonsmooth multiobjective optimization problems
Authors: Anh, NLH; Hung, NC; Chuong, TD
Abstract: In this paper, we consider a multiobjective optimization problem, where its feasibility set is unbounded and the related functions are nonsmooth and nonconvex. We first present new sequential necessary/sufficient optimality conditions that are established in terms of asymptotic value sets for weak Pareto solutions and weak Pareto values of the considered problem. We then examine how asymptotic values and Karush–Kuhn–Tucker values guarantee the existence of different types of Pareto solutions of the underlying problem. To achieve these goals, we utilize the tool of variational analysis to define sets of asymptotic values and Karush–Kuhn–Tucker values of the multiobjective optimization problem.</summary>
    <dc:date>2025-12-23T00:00:00Z</dc:date>
  </entry>
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