arXiv subjects

Guo Yan

Publications and source records attributed to Guo Yan.

4 recordsLinked to original sources

arXiv2026

Using SVM to Estimate and Predict Binary Choice Models

The support vector machine (SVM) has an asymptotic behavior that parallels that of the quasi-maximum likelihood estimator (QMLE) for binary outcomes generated by a binary choice model (BCM), although it is not a QMLE. We show that, under the linear conditional mean condition for covariates given the systematic component used in the QMLE slope consistency literature, the slope of the separating hyperplane given by the SVM consistently estimates the BCM slope parameter, as long as the class weight is used as required when binary outcomes are severely imbalanced. The SVM slope estimator is asymptotically equivalent to that of logistic regression in this sense. The finite-sample performance of the two estimators can be quite distinct depending on the distributions of covariates and errors, but neither dominates the other. The intercept parameter of the BCM can be consistently estimated once a consistent estimator of its slope parameter is obtained.

econ.EM↗

arXiv2025

Complete suppression of flux instabilities in ramped superconducting magnets with synchronous temperature-modulated Jc

Nonlinear multi-field coupling as an intrinsic property of complex physical systems often leads to abrupt and undesired instabilities. For current-ramped high-field Nb3Sn magnets, frequent flux jumps are observed, which easily causes premature quenches and requires prolonged and resource-intensive magnet training process. In this study, we propose a paradigm-shifting methodology framework that achieves complete suppression of thermomagnetic instabilities through synchronized temperature-modulated critical current density (Jc). Through numerical simulations of flux jumps in multifilamentary Nb3Sn wires at various temperatures, we construct thermomagnetic stability diagram in the Ha-T plane. The simulated results are in good agreement with experiments, confirming that the synchronized temperature ramp-down can fully eliminate flux jumps. We reveal the underlying mechanism of enhancing the thermomagnetic stability arises from that synchronized temperature ramp-down can continuously tune both Jc and its slope. Furthermore, we explore the thermomagnetic instabilities of current-ramped superconducting magnets through large-scale GPU-optimized algorithm. The flux jump and quench diagram in the Ia-T plane are obtained. It indicates that the temperature ramp-down can completely suppress flux jumps without compromising Jc at high magnetic fields. Importantly, this method does not require modifications to the superconducting microstructures or fabrication process, offering a practical and broadly applicable solution. The findings not only provide a robust method for stabilizing various superconducting magnet systems, including high-temperature superconducting magnets wound with second-generated (2G) coated tapes, but also suggest a generalizable strategy for controlling instability in other nonlinear non-equilibrium physical systems.

cond-mat.supr-con↗

arXiv2025

Slope Consistency of Quasi-Maximum Likelihood Estimator for Binary Choice Models

Although QMLE is generally inconsistent, logistic regression relying on the binary choice model (BCM) with logistic errors is widely used, especially in machine learning contexts with many covariates. This paper revisits the slope consistency of QMLE for BCMs. Ruud (1983) introduced a set of conditions under which QMLE may yield a constant multiple of the slope coefficient of BCMs asymptotically. However, he did not fully establish the slope consistency of QMLE, which requires the existence of a positive multiple of the true slope that maximizes the population QMLE likelihood over an appropriately restricted parameter space. We close this gap by providing a formal proof of slope consistency under the same set of conditions for BCMs identified as in Manski (1975, 1985). Our result implies that, under suitable conditions, logistic regression yields a consistent estimate of the slope coefficient for BCMs.

econ.EM↗

arXiv2024

A Kernelization-Based Approach to Nonparametric Binary Choice Models

We propose a new estimator for nonparametric binary choice models that does not impose a parametric structure on either the systematic function of covariates or the distribution of the error term. A key advantage of our approach is its computational scalability in the number of covariates. For instance, even when assuming a normal error distribution as in probit models, commonly used sieves for approximating an unknown function of covariates can lead to a large-dimensional optimization problem when the number of covariates is moderate. Our approach, motivated by kernel methods in machine learning, views certain reproducing kernel Hilbert spaces as special sieve spaces, coupled with spectral cut-off regularization for dimension reduction. We establish the consistency of the proposed estimator and asymptotic normality of the plug-in estimator for weighted average partial derivatives. Simulation studies show that, compared to parametric estimation methods, the proposed method effectively improves finite sample performance in cases of misspecification, and has a rather mild efficiency loss if the model is correctly specified. Using administrative data on the grant decisions of US asylum applications to immigration courts, along with nine case-day variables on weather and pollution, we re-examine the effect of outdoor temperature on court judges' ``mood'', and thus, their grant decisions.

econ.EM↗