A spatiotemporal negative binomial model with dynamic dispersion: An application to Tuberculosis infections
该研究通过引入具有动态分散参数的负二项空间整数值广义自回归条件异方差模型，解决了巴西圣保罗州结核病感染的空间异质性和时间波动性问题。
0 citationsRead paper
Selected work
Representative Papers
J- and MJ-Type Tests for Non-Nested Parametric Survival Models with a Cure Fraction: A Score Test Approach
This study addresses the challenge of effectively discriminating among non-nested survival models with a cure fraction when their baseline distributions differ. To this end, we propose J-type and MJ-type score tests that extend the Vuong test to the cure survival modeling framework. By incorporating information from competing models into the null log-likelihood, our approach assesses model redundancy and constructs unsigned statistics suitable for multi-model comparison. Integrating restricted maximum likelihood estimation, parametric bootstrap, and Kullback–Leibler divergence estimation, the proposed methodology supports both global hypothesis testing and model selection. Notably, the MJ statistic is capable of identifying whether at least one candidate model is correctly specified. Compared to existing approaches, this framework offers greater theoretical rigor while maintaining strong discriminative power and practical utility.
0 citationsRead paper
Statistical Inference for Gaussian Kernel Robust Regression with the gkrreg Package
This study addresses the lack of statistical inference tools for Gaussian kernel robust regression (GKRReg) by establishing, for the first time, its theoretical connection to the redescending M-estimator family through generalized M-estimation theory. Building on this foundation, the authors develop a comprehensive statistical inference framework that introduces a closed-form sandwich variance estimator—based on HC0-type heteroskedasticity-robust covariance matrices—and a paired bootstrap procedure with dynamically adjusted kernel bandwidths. Efficient computation is achieved via an iteratively reweighted least squares (IRWLS) algorithm. The resulting methodology has been implemented in the R package gkrreg, which supports diagnostic plotting, multiple γ² estimators, and benchmark datasets. Empirical evaluations on several real-world datasets demonstrate the framework’s effectiveness, substantially enhancing the practical utility and interpretability of GKRReg.
0 citationsRead paper
Quantum-classical hybrid models based on error correction for time series forecasting
This work addresses the limitations of single models and classical hybrid approaches in capturing complex temporal patterns by proposing the first quantum-classical hybrid forecasting system based on an error correction mechanism. The method leverages a quantum model to capture high-dimensional nonlinear features in time series data, while a classical model explicitly learns the residual errors from the quantum predictions, enabling complementary and synergistic interaction between the two components. By integrating quantum machine learning into a classical error correction framework for the first time, the proposed system demonstrates significant performance gains over purely classical models and classical-classical hybrid architectures across multiple time series forecasting benchmarks, thereby validating the efficacy and superiority of quantum-classical collaboration in predictive modeling.
0 citationsRead paper
Drawing Lines in Psychological Space: What K-means Clustering Reveals in Simulated and Real Psychometric Data
K-means clustering is frequently employed in psychology to identify latent subgroups; however, its reliance on geometric distance precludes validation of whether the resulting clusters correspond to genuine psychological constructs. This study systematically compares K-means performance on multidimensional Gaussian simulated data—lacking any true categorical structure—with that on the international psychometric dataset SMARVUS. The results demonstrate that K-means consistently produces stable and visually coherent clusters even in continuous latent variable spaces devoid of discrete classes. These findings suggest that such clusters may merely reflect spatial partitioning rather than meaningful psychological types, thereby challenging the foundational assumption that K-means can validly infer the existence of latent categories in psychological research.
0 citationsRead paper
Recent publications
Latest Papers
A spatiotemporal negative binomial model with dynamic dispersion: An application to Tuberculosis infections
该研究通过引入具有动态分散参数的负二项空间整数值广义自回归条件异方差模型，解决了巴西圣保罗州结核病感染的空间异质性和时间波动性问题。
0 citationsRead paper
J- and MJ-Type Tests for Non-Nested Parametric Survival Models with a Cure Fraction: A Score Test Approach
This study addresses the challenge of effectively discriminating among non-nested survival models with a cure fraction when their baseline distributions differ. To this end, we propose J-type and MJ-type score tests that extend the Vuong test to the cure survival modeling framework. By incorporating information from competing models into the null log-likelihood, our approach assesses model redundancy and constructs unsigned statistics suitable for multi-model comparison. Integrating restricted maximum likelihood estimation, parametric bootstrap, and Kullback–Leibler divergence estimation, the proposed methodology supports both global hypothesis testing and model selection. Notably, the MJ statistic is capable of identifying whether at least one candidate model is correctly specified. Compared to existing approaches, this framework offers greater theoretical rigor while maintaining strong discriminative power and practical utility.
0 citationsRead paper
Statistical Inference for Gaussian Kernel Robust Regression with the gkrreg Package
This study addresses the lack of statistical inference tools for Gaussian kernel robust regression (GKRReg) by establishing, for the first time, its theoretical connection to the redescending M-estimator family through generalized M-estimation theory. Building on this foundation, the authors develop a comprehensive statistical inference framework that introduces a closed-form sandwich variance estimator—based on HC0-type heteroskedasticity-robust covariance matrices—and a paired bootstrap procedure with dynamically adjusted kernel bandwidths. Efficient computation is achieved via an iteratively reweighted least squares (IRWLS) algorithm. The resulting methodology has been implemented in the R package gkrreg, which supports diagnostic plotting, multiple γ² estimators, and benchmark datasets. Empirical evaluations on several real-world datasets demonstrate the framework’s effectiveness, substantially enhancing the practical utility and interpretability of GKRReg.
0 citationsRead paper
Quantum-classical hybrid models based on error correction for time series forecasting
This work addresses the limitations of single models and classical hybrid approaches in capturing complex temporal patterns by proposing the first quantum-classical hybrid forecasting system based on an error correction mechanism. The method leverages a quantum model to capture high-dimensional nonlinear features in time series data, while a classical model explicitly learns the residual errors from the quantum predictions, enabling complementary and synergistic interaction between the two components. By integrating quantum machine learning into a classical error correction framework for the first time, the proposed system demonstrates significant performance gains over purely classical models and classical-classical hybrid architectures across multiple time series forecasting benchmarks, thereby validating the efficacy and superiority of quantum-classical collaboration in predictive modeling.
0 citationsRead paper
Drawing Lines in Psychological Space: What K-means Clustering Reveals in Simulated and Real Psychometric Data
K-means clustering is frequently employed in psychology to identify latent subgroups; however, its reliance on geometric distance precludes validation of whether the resulting clusters correspond to genuine psychological constructs. This study systematically compares K-means performance on multidimensional Gaussian simulated data—lacking any true categorical structure—with that on the international psychometric dataset SMARVUS. The results demonstrate that K-means consistently produces stable and visually coherent clusters even in continuous latent variable spaces devoid of discrete classes. These findings suggest that such clusters may merely reflect spatial partitioning rather than meaningful psychological types, thereby challenging the foundational assumption that K-means can validly infer the existence of latent categories in psychological research.
0 citationsRead paper