Institution profile

University of Pennsylvania

Academic institutionnorthamerica · us
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Research library1,679linked papers
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Selected work

Representative Papers

Selecting the number of components in PCA via random signflips.

Dec 05, 2020

Existing principal component analysis (PCA) model selection methods lack statistical guarantees for determining the number of leading components under heteroscedastic noise—where observation-wise noise variances differ—in high-dimensional settings. Method: We propose Signflip Parallel Analysis (Signflip PA), a novel parallel analysis method that generates an empirical null distribution via random sign flips and adaptively calibrates singular value thresholds. Contribution/Results: Signflip PA is the first to integrate dimension-free operator norm bounds and large-deviation theory for eigenvalues of non-homogeneous matrices into PCA model selection, ensuring consistent factor recovery. We establish its theoretical consistency under a signal-plus-heteroscedastic-noise model. Empirical studies—including simulations and real-data analyses—demonstrate that Signflip PA significantly outperforms classical approaches such as scree plots and conventional parallel analysis, overcoming the fundamental limitation wherein heteroscedasticity causes traditional methods to fail.

16 citations2 influentialRead paper

Structural Nested Mean Models Under Parallel Trends Assumptions

Apr 21, 2022

This paper addresses the disconnect between structural nested mean models (SNMMs) and dynamic difference-in-differences (DiD) in estimating time-varying treatment effects. We propose a novel SNMM framework grounded in the parallel trends assumption—departing from the conventional no-unmeasured-confounding assumption. We establish, for the first time, that SNMMs achieve nonparametric identification under parallel trends alone. The framework unifies estimation of dynamic treatment effects, sustained-intervention effects, direct effect decomposition, and optimal dynamic treatment regimes. Additionally, we develop a sensitivity analysis method to assess robustness when parallel trends are violated. Integrating dynamic causal inference with sequential decision-making modeling, our approach is validated through empirical applications—including Medicaid expansion, flood insurance adoption, and temperature impacts on crop yields—demonstrating its validity and robustness in real-world policy and environmental settings.

9 citations2 influentialRead paper

The Pursuit of Happiness

Mar 05, 2018The Epicurean Republic

Traditional rational-agent models fail to account for the dynamic interplay between individual well-being and social embeddedness in explaining cooperative behavior. Method: We develop a “homo-felix” model wherein subjective well-being is formalized as a dynamic objective function coupling individual payoff and social connectedness, incorporating a well-being feedback loop. Using 2×2 games and n-player public goods games, we apply dynamical systems analysis and equilibrium stability theory to characterize behavioral evolution. Contribution/Results: We demonstrate that prosocial behavior can induce global phase transitions: the Nash non-cooperative equilibrium is marginally unstable; a marginal increase in one agent’s cooperation propensity suffices to drive the system to a high-cooperation stable state. Crucially, we propose an endogenous preference-evolution mechanism, proving that well-being exhibits positive externalities and self-reinforcing dynamics. We identify a critical pathway whereby micro-level prosocial actions trigger macro-level institutional shifts, offering a novel paradigm for understanding the coevolution of cooperation and societal welfare.

8 citations1 influentialRead paper

Optimal Program Synthesis via Abstract Interpretation

Jan 02, 2024Proc. ACM Program. Lang.

This work addresses the problem of program synthesis in domain-specific languages (DSLs) that involve numeric constants and require optimization of quantitative objectives such as accuracy. The authors propose a provably optimal search method that constructs a search graph over program subsets and integrates A* search with a heuristic derived from abstract interpretation to efficiently prune suboptimal subtrees. The key innovation lies in the design of abstract transformers tailored to DSL components with monotonic semantics, enabling a pruning mechanism that guarantees optimality. Experimental evaluation on two real-world DSLs demonstrates that the approach substantially outperforms existing state-of-the-art synthesizers, achieving significant improvements in scalability while maintaining correctness and optimality guarantees.

7 citationsRead paper

Dynamic heterogeneous distribution regression panel models, with an application to labor income processes

Feb 08, 2022Social Science Research Network

This paper addresses the challenge of dynamic forecasting and steady-state distribution inference in panel data with cross-sectional heterogeneity in unit-specific coefficients. We propose a dynamic heterogeneous distribution regression framework that jointly estimates individual-level heterogeneous coefficients and their functional targets—including one-step-ahead forecasts, steady-state cross-sectional distributions, and quantile treatment effects. To enable uniform asymptotically valid inference on functional parameters under unknown heterogeneity, we develop a novel cross-sectional bootstrap procedure—the first of its kind for such settings. The method integrates fixed-effects estimation, distribution regression, and quantile treatment effect modeling. Empirical application to PSID data reveals that negative income shocks significantly increase right-skewness in labor income distributions and raise poverty persistence rates, while higher education mitigates these effects; moreover, income mobility exhibits systematic heterogeneity across individuals. Simulation studies confirm the method’s robustness and reliability.

4 citations1 influentialRead paper
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