Genya Kobayashi

696 total citations
17 papers, 431 citations indexed

About

Genya Kobayashi is a scholar working on Statistics and Probability, Artificial Intelligence and Economics and Econometrics. According to data from OpenAlex, Genya Kobayashi has authored 17 papers receiving a total of 431 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Statistics and Probability, 7 papers in Artificial Intelligence and 5 papers in Economics and Econometrics. Recurrent topics in Genya Kobayashi's work include Statistical Methods and Inference (7 papers), Bayesian Methods and Mixture Models (5 papers) and Statistical Methods and Bayesian Inference (3 papers). Genya Kobayashi is often cited by papers focused on Statistical Methods and Inference (7 papers), Bayesian Methods and Mixture Models (5 papers) and Statistical Methods and Bayesian Inference (3 papers). Genya Kobayashi collaborates with scholars based in Japan, South Korea and United States. Genya Kobayashi's co-authors include Hideo Kozumi, Shonosuke Sugasawa, David M. Sasaki, C. C. Christensen, Yuki Kawakubo, Taeryon Choi and Minhyeok Kim and has published in prestigious journals such as Journal of Business and Economic Statistics, Computational Statistics & Data Analysis and Statistics and Computing.

In The Last Decade

Genya Kobayashi

16 papers receiving 419 citations

Peers

Genya Kobayashi
Genya Kobayashi
Citations per year, relative to Genya Kobayashi Genya Kobayashi (= 1×) peers Maozai Tian

Countries citing papers authored by Genya Kobayashi

Since Specialization
Citations

This map shows the geographic impact of Genya Kobayashi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Genya Kobayashi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Genya Kobayashi more than expected).

Fields of papers citing papers by Genya Kobayashi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Genya Kobayashi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Genya Kobayashi. The network helps show where Genya Kobayashi may publish in the future.

Co-authorship network of co-authors of Genya Kobayashi

This figure shows the co-authorship network connecting the top 25 collaborators of Genya Kobayashi. A scholar is included among the top collaborators of Genya Kobayashi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Genya Kobayashi. Genya Kobayashi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Kobayashi, Genya, et al.. (2024). Predicting COVID-19 hospitalisation using a mixture of Bayesian predictive syntheses. The Annals of Applied Statistics. 18(4).
2.
Kim, Minhyeok, et al.. (2024). Sequential Monte Carlo ABC: an overview with application to COVID-19 data. Journal of the Korean Statistical Society. 54(1). 248–283. 1 indexed citations
3.
Kawakubo, Yuki & Genya Kobayashi. (2023). Small area estimation of general finite-population parameters based on grouped data. Computational Statistics & Data Analysis. 184. 107741–107741. 1 indexed citations
4.
Sugasawa, Shonosuke & Genya Kobayashi. (2022). Robust fitting of mixture models using weighted complete estimating equations. Computational Statistics & Data Analysis. 174. 107526–107526. 2 indexed citations
5.
Kobayashi, Genya, et al.. (2021). Bayesian Approach to Lorenz Curve Using Time Series Grouped Data. Journal of Business and Economic Statistics. 40(2). 897–912. 3 indexed citations
6.
Kobayashi, Genya, et al.. (2020). Predicting intervention effect for COVID-19 in Japan: state space modeling approach. BioScience Trends. 14(3). 174–181. 30 indexed citations
7.
Kobayashi, Genya, et al.. (2020). Flexible Bayesian quantile curve fitting with shape restrictions under the Dirichlet process mixture of the generalized asymmetric Laplace distribution. Canadian Journal of Statistics. 49(3). 698–730. 6 indexed citations
8.
Sugasawa, Shonosuke, Genya Kobayashi, & Yuki Kawakubo. (2019). Estimation and inference for area-wise spatial income distributions from grouped data. Computational Statistics & Data Analysis. 145. 106904–106904. 1 indexed citations
9.
Kobayashi, Genya, et al.. (2018). Approximate Bayesian computation for Lorenz curves from grouped data. Computational Statistics. 34(1). 253–279. 4 indexed citations
10.
Sugasawa, Shonosuke, Genya Kobayashi, & Yuki Kawakubo. (2018). Latent mixture modeling for clustered data. Statistics and Computing. 29(3). 537–548. 3 indexed citations
11.
Kobayashi, Genya, et al.. (2016). Public health improvements and mortality in interwar Tokyo: a Bayesian disease mapping approach. Cliometrica. 12(1). 1–31. 15 indexed citations
12.
Kobayashi, Genya. (2015). Skew exponential power stochastic volatility model for analysis of skewness, non-normal tails, quantiles and expectiles. Computational Statistics. 31(1). 49–88. 8 indexed citations
14.
Kobayashi, Genya, et al.. (2014). An Integrated Purchase Model Using Gaussian Copula. Behaviormetrika. 41(2). 147–167. 1 indexed citations
15.
Kozumi, Hideo & Genya Kobayashi. (2011). Gibbs sampling methods for Bayesian quantile regression. Journal of Statistical Computation and Simulation. 81(11). 1565–1578. 307 indexed citations
16.
Kobayashi, Genya & Hideo Kozumi. (2011). Bayesian analysis of quantile regression for censored dynamic panel data. Computational Statistics. 27(2). 359–380. 25 indexed citations
17.
Sasaki, David M., et al.. (1992). Rabid bat diagnosed in Hawaii.. PubMed. 51(7). 181–5. 14 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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