arXiv subjects

Seong-ho Lee

Publications and source records attributed to Seong-ho Lee.

7 recordsLinked to original sources

arXiv2026

Surrogate-assisted optimal sampling for risk prediction under measurement constraints

In many risk prediction problems, covariates and a response surrogate are routinely available for a large target population, whereas the true response is costly to ascertain and is observed only for a limited subset. This creates a design problem: one must decide which observations should receive response measurement in order to build a prediction model under a fixed measurement budget. We propose a surrogate-assisted optimal sampling framework for risk prediction under measurement constraints. In the target setting, the surrogate identifies confirmed positive cases, while responses for surrogate-negative observations remain unobserved and can be selectively measured, and thus the sampling design determines how the response measurement budget is allocated. Our framework constructs an optimal sampling design minimizing the leading term of the expected out-of-sample cross-entropy loss and incorporates the resulting design into an inverse-probability-weighted cross-entropy estimator. The proposed design depends only on covariates, the surrogate, and a preliminary estimator, and therefore does not require responses from unlabeled observations at the design stage. We establish consistency, asymptotic normality, and leading-order prediction optimality of the resulting estimator. Extensive simulation studies and two real data applications demonstrate that the proposed design improves prediction performance and exhibits robustness under surrogate misspecification and rare outcome settings.

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arXiv2025

DOD: Detection of outliers in high dimensional data with distance of distances

Reliable outlier detection in high-dimensional data is crucial in modern science, yet it remains a challenging task. Traditional methods often break down in these settings due to their reliance on asymptotic behaviors with respect to sample size under fixed dimension. Furthermore, many modern alternatives introduce sophisticated statistical treatments and computational complexities. To overcome these issues, our approach leverages intuitive geometric properties of high-dimensional space, effectively turning the curse of dimensionality into an advantage. We propose two new outlyingness statistics based on observation's relational patterns with all other points, measured via pairwise distances or inner products. We establish a theoretical foundation for our statistics demonstrating that as the dimension grows, our statistics create a non-vanishing margin that asymptotically separates outliers from non-outliers. Based on this foundation, we develop practical outlier detection procedures, including a simple clustering-based algorithm and a distribution-free test using random rotations. Through simulation experiments and real data applications, we demonstrate that our proposed methods achieve a superior balance between detection power and false positive control, outperforming existing methods and establishing their practical utility in high-dimensional settings.

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arXiv2025

Efficient Inference under Label Shift in Unsupervised Domain Adaptation

In many real-world applications, researchers aim to deploy models trained in a source domain to a target domain, where obtaining labeled data is often expensive, time-consuming, or even infeasible. While most existing literature assumes that the labeled source data and the unlabeled target data follow the same distribution, distribution shifts are common in practice. This paper focuses on label shift and develops efficient inference procedures for general parameters characterizing the unlabeled target population. A central idea is to model the outcome density ratio between the labeled and unlabeled data. To this end, we propose a progressive estimation strategy that unfolds in three stages: an initial heuristic guess, a consistent estimation, and ultimately, an efficient estimation. This self-evolving process is novel in the statistical literature and of independent interest. We also highlight the connection between our approach and prediction-powered inference (PPI), which uses machine learning models to improve statistical inference in related settings. We rigorously establish the asymptotic properties of the proposed estimators and demonstrate their superior performance compared to existing methods. Through simulation studies and multiple real-world applications, we illustrate both the theoretical contributions and practical benefits of our approach.

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arXiv2024

Robust and efficient estimation in the presence of a randomly censored covariate

In Huntington's disease research, a current goal is to understand how symptoms change prior to a clinical diagnosis. Statistically, this entails modeling symptom severity as a function of the covariate 'time until diagnosis', which is often heavily right-censored in observational studies. Existing estimators that handle right-censored covariates have varying statistical efficiency and robustness to misspecified models for nuisance distributions (those of the censored covariate and censoring variable). On one extreme, complete case estimation, which utilizes uncensored data only, is free of nuisance distribution models but discards informative censored observations. On the other extreme, maximum likelihood estimation is maximally efficient but inconsistent when the covariate's distribution is misspecified. We propose a semiparametric estimator that is robust and efficient. When the nuisance distributions are modeled parametrically, the estimator is doubly robust, i.e., consistent if at least one distribution is correctly specified, and semiparametric efficient if both models are correctly specified. When the nuisance distributions are estimated via nonparametric or machine learning methods, the estimator is consistent and semiparametric efficient. We show empirically that the proposed estimator, implemented in the R package sparcc, has its claimed properties, and we apply it to study Huntington's disease symptom trajectories using data from the Enroll-HD study.

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arXiv2023

Doubly Flexible Estimation under Label Shift

In studies ranging from clinical medicine to policy research, complete data are usually available from a population $\mathscr{P}$, but the quantity of interest is often sought for a related but different population $\mathscr{Q}$ which only has partial data. In this paper, we consider the setting that both outcome $Y$ and covariate ${\bf X}$ are available from $\mathscr{P}$ whereas only ${\bf X}$ is available from $\mathscr{Q}$, under the so-called label shift assumption, i.e., the conditional distribution of ${\bf X}$ given $Y$ remains the same across the two populations. To estimate the parameter of interest in $\mathscr{Q}$ via leveraging the information from $\mathscr{P}$, the following three ingredients are essential: (a) the common conditional distribution of ${\bf X}$ given $Y$, (b) the regression model of $Y$ given ${\bf X}$ in $\mathscr{P}$, and (c) the density ratio of $Y$ between the two populations. We propose an estimation procedure that only needs standard nonparametric technique to approximate the conditional expectations with respect to (a), while by no means needs an estimate or model for (b) or (c); i.e., doubly flexible to the possible model misspecifications of both (b) and (c). This is conceptually different from the well-known doubly robust estimation in that, double robustness allows at most one model to be misspecified whereas our proposal can allow both (b) and (c) to be misspecified. This is of particular interest in our setting because estimating (c) is difficult, if not impossible, by virtue of the absence of the $Y$-data in $\mathscr{Q}$. Furthermore, even though the estimation of (b) is sometimes off-the-shelf, it can face curse of dimensionality or computational challenges. We develop the large sample theory for the proposed estimator, and examine its finite-sample performance through simulation studies as well as an application to the MIMIC-III database.

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arXiv2022

Semiparametric Approach to Estimation of Marginal and Quantile Effects

We consider a semiparametric generalized linear model and study estimation of both marginal and quantile effects in this model. We propose an approximate maximum likelihood estimator, and rigorously establish the consistency, the asymptotic normality, and the semiparametric efficiency of our method in both the marginal effect and the quantile effect estimation. Simulation studies are conducted to illustrate the finite sample performance, and we apply the new tool to analyze a Swiss non-labor income data and discover a new interesting predictor.

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arXiv2021

Covariate balancing for causal inference on categorical and continuous treatments

We propose novel estimators for categorical and continuous treatments by using an optimal covariate balancing strategy for inverse probability weighting. The resulting estimators are shown to be consistent and asymptotically normal for causal contrasts of interest, either when the model explaining treatment assignment is correctly specified, or when the correct set of bases for the outcome models has been chosen and the assignment model is sufficiently rich. For the categorical treatment case, we show that the estimator attains the semiparametric efficiency bound when all models are correctly specified. For the continuous case, the causal parameter of interest is a function of the treatment dose. The latter is not parametrized and the estimators proposed are shown to have bias and variance of the classical nonparametric rate. Asymptotic results are complemented with simulations illustrating the finite sample properties. Our analysis of a data set suggests a nonlinear effect of BMI on the decline in self reported health.

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