The Applied Statistics Workshop 2026
担当教員：大森裕浩 (Yasuhiro Omori)・下津克己 (Katsumi Shimotsu)、入江 薫 (Kaoru Irie)・奥井亮 (Ryo Okui)
| 日時 |
2026年4月3日（金 Friday） 16:50-18:35
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| 場所 |
東京大学大学院経済学研究科棟 3階 第3教室
Lecture Hall No. 3 on the 3rd floor of the Economics Research Building [MAP] ※対面のみでの開催です。東京大学以外で参加をご希望の方は、CIRJE (cirje[at mark]e.u-tokyo.ac.jp) までご連絡ください。 |
| 報告 |
Pedro Raposo(Catolica Lisbon School of Business and Economics, Portugal) "High-Dimensional Panel Expectiles Regression: A Decomposition of the Gender Wage Gap" with Matei Demetrescu, José Machado, Pedro Potugal, Paulo Rodrigues |
| Abstract | In this paper we develop expectile panel data methods for high-dimensional fixed effects estimation in line with Guimaraes and Portugal (2010), which will allow for a wide range of applications in fields such as Labour economics, Economics of Education, and Inequality. We also show how the Gelbach decomposition can be validly implemented in the context of panel expectile regressions. Using a unique Portuguese linked employer-employee dataset, we use our estimator to explore the determinants of the gender wage gap over the period 1995-2022. We find that: (i) the gender wage gap is larger in the upper tail; (ii) the difference is mainly explained both in the left and right tail by the individual unobserved heterogeneity; and (iii) assortative matching is less pronounced in the tailes. |
| 日時 |
2026年4月10日（金 Friday） 16:50-18:35
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| 場所 |
東京大学大学院経済学研究科棟 3階 第3教室
Lecture Hall No. 3 on the 3rd floor of the Economics Research Building [MAP] ※対面のみでの開催です。東京大学以外で参加をご希望の方は、CIRJE (cirje[at mark]e.u-tokyo.ac.jp) までご連絡ください。 |
| 報告 |
Changjun Im (Seoul National University) "Local Fréchet regression with toroidal predictors" |
| Abstract | We propose a novel framework for nonparametric regression when the response variable takes values in a general metric space and the predictor is lying on a torus. We introduce both local constant and local linear Fréchet regression estimators to address the critical need for statistical methods in analyzing data with multiple periodic components (e.g., time of day and day of the year). To the best of our knowledge, this is the first development of nonparametric model specifically designed for the toroidal predictor space. We establish the asymptotic properties of our estimators, including consistency and convergence rates, which demonstrate they achieve the optimal convergence rate known in nonparametric regression with Euclidean predictors, signifying high theoretical efficiency. Simulation studies and real data applications, including network data from the New York taxi system, confirm that our methods outperform existing approaches. |
| 日時 |
2026年4月17日（金 Friday） 16:50-18:35
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|---|---|
| 場所 |
東京大学大学院経済学研究科棟 3階 第3教室
Lecture Hall No. 3 on the 3rd floor of the Economics Research Building [MAP] ※対面のみでの開催です。東京大学以外で参加をご希望の方は、CIRJE (cirje[at mark]e.u-tokyo.ac.jp) までご連絡ください。 |
| 報告 |
Yi Yu (University of Warwick) "Optimal Cox regression under federated differential privacy: coefficients and cumulative hazards" |
| Abstract | We study two foundational problems in distributed survival analysis: estimating Cox regression coefficients and cumulative hazard functions, under federated differential privacy constraints, allowing for heterogeneous per-sever sample sizes and privacy budgets. To quantify the fundamental cost of privacy, we derive minimax lower bounds along with matching (up to poly-logarithmic factors) upper bounds. In particular, to estimate the cumulative hazard function, we design a private tree-based algorithm for nonparametric integral estimation. Our results reveal server-level phase transitions between the private and non-private rates, as well as the reduced estimation accuracy from imposing privacy constraints on distributed subsets of data. To address scenarios with partially public information, we also consider a relaxed differential privacy framework and provide a corresponding minimax analysis. To our knowledge, this is the first treatment of partially public data in survival analysis, and it establishes a no-gain in accuracy phenomenon. Finally, we conduct extensive numerical experiments, with an accompanying R package FDPCox, validating our theoretical findings. These experiments also include a fully-interactive algorithm with tighter privacy composition, which demonstrates improved estimation accuracy. |
| 日時 |
2026年5月8日（金 Friday） 16:50-18:35
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| 場所 |
東京大学大学院経済学研究科棟 3階 第3教室
Lecture Hall No. 3 on the 3rd floor of the Economics Research Building [MAP] ※対面のみでの開催です。東京大学以外で参加をご希望の方は、CIRJE (cirje[at mark]e.u-tokyo.ac.jp) までご連絡ください。 |
| 報告 |
梶 哲也 (The University of Chicago Booth School of Business) Tetsuya Kaji (The University of Chicago Booth School of Business) TBA |
| Abstract |
| 日時 |
2026年6月5日（金 Friday） 16:50-18:35
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| 場所 |
東京大学大学院経済学研究科棟 3階 第3教室
Lecture Hall No. 3 on the 3rd floor of the Economics Research Building [MAP] ※対面のみでの開催です。東京大学以外で参加をご希望の方は、CIRJE (cirje[at mark]e.u-tokyo.ac.jp) までご連絡ください。 |
| 報告 |
Liang Zhong (University of Hong Kong, Business School) TBA |
| Abstract |
| 日時 |
2026年6月19日（金 Friday） 16:50-18:35
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|---|---|
| 場所 |
東京大学大学院経済学研究科棟 3階 第3教室
Lecture Hall No. 3 on the 3rd floor of the Economics Research Building [MAP] ※対面のみでの開催です。東京大学以外で参加をご希望の方は、CIRJE (cirje[at mark]e.u-tokyo.ac.jp) までご連絡ください。 |
| 報告 |
江上尚輝 (Massachusetts Institute of Technology) Naoki Egami (Massachusetts Institute of Technology) TBA |
| Abstract |
| 日時 |
Tentative
2026年6月26日（金 Friday） 16:50-18:35 |
|---|---|
| 場所 |
東京大学大学院経済学研究科棟 3階 第3教室
Lecture Hall No. 3 on the 3rd floor of the Economics Research Building [MAP] ※対面のみでの開催です。東京大学以外で参加をご希望の方は、CIRJE (cirje[at mark]e.u-tokyo.ac.jp) までご連絡ください。 |
| 報告 |
Matteo Luciani (Board of Governors of the Federal Reserve System) TBA |
| Abstract |
以下本年度終了分
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