检索结果-内蒙古大学图书馆

On construction of strong orthogonal arrays and column-orthogonal strong orthogonal arrays of strength two plus

science China Mathematics 2025年第5期68卷 1219-1242页

作者： Bochuan Jiang Yaping Wang Fasheng Sun School of Mathematics and Statistics Beijing Jiaotong UniversityBeijing100044China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of EducationSchool of StatisticsEast China Normal UniversityShanghai200062China Key Laboratory for Applied Statistics of Ministry of Education and School of Mathematics and Statistics Northeast Normal UniversityChangchun130024China

Computer experiments require space-filling designs with good low-dimensional projection *** orthogonal arrays are a type of space-filling design that provides better stratifications in low dimensions than ordinary orthogonal *** this paper,we address the problem of constructing strong orthogonal arrays and column-orthogonal strong orthogonal arrays of strength two *** methods typically rely on regular designs or specific nonregular designs as base orthogonal arrays,limiting the sizes of the final ***,we propose two general methods that are easy to implement and applicable to a wide range of base orthogonal *** methods produce space-filling designs that can accommodate a large number of factors,provide significant flexibility in terms of run sizes,and possess appealing low-dimensional projection ***,these designs are ideal for computer experiments.

关键词： computer experiment difference scheme strong orthogonal array column-orthogonality space-filling property

来源：评论

学校读者我要写书评

暂无评论

Developing a Customer Churn Prediction Framework Using LFMCP Value Segmentation: Insights from the Thai Insurance Sector 11th

Developing a Customer Churn Prediction Framework Using LFMCP...

引用

11th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2025

作者： Pongsathornwiwat, Akkaranan Kulmanochwong, Pattaraporn Department of Logistics Management Graduate School of Applied Statistics National Institute of Development Administration Bangkok Thailand Department of Business Analytics and Data Science Graduate School of Applied Statistics National Institute of Development Administration Bangkok Thailand

ISBN: (纸本)9789819646029

This study explores the application of the Length, Frequency, Monetary, Claim, and Profit (LFMCP) customer value model to predict customer churn in a Thai general insurance company with a primary focus on B2B clients. The research provides valuable insights into the insurance industry, particularly for organizations aiming to enhance customer retention strategies. The churn prediction model is developed in two phases. In the first phase, LFMCP variables are used to distinguish between valued and non-valued customers. In the second phase, the classification performance of five predictive techniques—k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Decision Tree, Random Forest (RF), and XGBoost (XGB)—is compared. Results show that LFMCP significantly improves churn classification, with XGBoost emerging as the best-performing method. The study also investigates the importance of LFMCP variables using the Analytical Hierarchy Process (AHP). While the analysis explores varying weight configurations, findings suggest that equal weighting of the LFMCP variables results in superior predictive performance for churn classification in the insurance context. By incorporating additional predictors beyond the traditional RFM (Recency-Frequency-Monetary) model, the study introduces a novel and effective framework for churn prediction in the insurance industry. This expanded model, combined with insights from the AHP evaluation, forms the key contribution of the research, offering practical value for enhancing decision-making and customer retention strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Nonlinear Functional Sufficient Dimension Reduction via Principal Fitted Components

引用

statistics and Computing 2025年第4期35卷 1-19页

作者： Kim, Minjee Park, Yujin Kim, Kyongwon Yoo, Jae Keun Department of Statistics Ewha Womans University 52 Ewhayeodae-gil Seodaemun-gu Seoul 03760 South Korea Department of Applied Statistics Department of Statistics and Data Science Yonsei University 50 Yonsei-ro Seodaemun-gu Seoul 03722 South Korea

In this paper, we propose a novel functional nonlinear sufficient dimension reduction method based on the principal fitted component model. Our approach extends the concept of principal fitted components to functional data, covering the case where both the predictors and responses are functions. We consider a general framework in which the predictor and response can each be viewed as elements of potentially infinite dimensional Hilbert spaces. This includes the important scalar on function and function on function cases as special instances. We generalize a nonlinear principal fitted component model within the framework of reproducing kernel Hilbert space, leveraging the nested Hilbert spaces theory to characterize nonlinear structures in functional data. The first space accommodates functions of random curves and the second space captures their nonlinear relationships. To establish the theoretical validity of our approach, we develop an asymptotic theory that characterizes the convergence behavior of the proposed estimator under mild regularity conditions. Extensive simulation studies demonstrate that our method outperforms existing functional sufficient dimension reduction methods, particularly in scenarios with complex nonlinear dependencies. The effectiveness of the proposed method is further validated through real data analysis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Functional data analysis Kernel methods Principal fitted component model Reproducing kernel Hilbert space Sufficient dimension reduction

来源：评论

学校读者我要写书评

暂无评论

Stochastic Explicit Calibration Algorithm for Survival Models

引用

IEEE Access 2025年 13卷 78679-78706页

作者： Park, Jeongho Kang, Sangwook Kim, Gwangsu Yonsei University Department of Statistics and Data Science Seoul03722 Korea Republic of Yonsei University Department of Applied Statistics Seoul03722 Korea Republic of Jeonbuk National University Department of Statistics and Research Institute for Materials and Energy Sciences Jeonju54896 Korea Republic of

Calibration is essential for risk evaluation in various fields;including medicine, finance, and reliability analysis. Although extensive research has focused on calibration in classification and regression tasks using deep neural networks, survival analysis remains relatively underexplored, resulting in the lack of improved calibration methods. In particular, while previous work has proposed a calibration method for survival analysis, it relies on fixed bins, which can lead to biased calibration assessments and substantial loss of predictive accuracy in pursuit of calibration. This gap can hinder an accurate assessment of survival functions, leading to increased risk management costs. In this study, we introduce Stochastic Explicit Calibration (S-cal), an algorithm that employs random intervals instead of fixed bins, thereby advancing the calibration methods used in deep networks. The calibration performance of S-cal is evaluated using metrics specifically designed for handling censored data, such as D-calibration and the Kolmogorov-Smirnov metric. Extensive experiments on synthetic and real-world datasets demonstrate that S-cal consistently outperforms existing methods in terms of calibration accuracy. In addition, we highlight how improved calibration can improve downstream tasks, including optimizing resource allocation and improving patient care decisions. This work presents a significant advancement in the study of calibration for survival analysis, offering valuable information for more reliable risk assessment models. © 2013 IEEE.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

Expressivity of deterministic quantum computation with one qubit

引用

Physical Review A 2025年第2期111卷 022429-022429页

作者： Yujin Kim Daniel K. Park Department of Statistics and Data Science Yonsei University Seoul 03722 Republic of Korea Department of Applied Statistics Yonsei University Seoul 03722 Republic of Korea

Deterministic quantum computation with one qubit (DQC1) is of significant theoretical and practical interest due to its computational advantages in certain problems, despite its subuniversality with limited quantum resources. In this work, we introduce parametrized DQC1 as a quantum machine learning model. We demonstrate that the gradient of the measurement outcome of a DQC1 circuit with respect to its gate parameters can be computed directly using the DQC1 protocol. This allows for gradient-based optimization of DQC1 circuits, positioning DQC1 as a quantum protocol for both training and inference. We then analyze the expressivity of the parametrized DQC1 circuits characterizing the set of learnable functions, and show that DQC1-based machine learning (ML) is as powerful as quantum neural networks based on universal computation. Our findings highlight the potential of DQC1 as a practical and versatile platform for ML, capable of rivaling more complex quantum computing models while utilizing simpler quantum resources.

关键词： Qubits

来源：评论

学校读者我要写书评

暂无评论

Selective Audio Perturbations for Targeting Specific Phrases in Speech Recognition Systems

引用

International Journal of Computational Intelligence Systems 2025年第1期18卷 1-24页

作者： Ko, Kyoungmin Kim, SungHwan Kwon, Hyun Department of Applied Statistics Konkuk University 120 Neungdong-ro Gwangjin-gu Seoul Korea Republic of Department of Artificial Intelligence and Data Science Korea Military Academy 574 Hwarang-ro Nowon-gu Seoul01819 Korea Republic of

In this paper, we propose a novel approach for creating audio adversarial examples that specifically target speech recognition systems. The proposed method involves adding optimized noise to a particular region of an audio sample that corresponds to a specified word or phrase. By doing so, the generated adversarial example is designed to deceive the targeted model into interpreting it as a modified sentence, where the specified phrase has been altered. This method offers advantages compared to existing techniques, including reduced distortion since noise is only added to the targeted area, and the ability for the attacker to selectively modify or add specific words as desired. The experimental evaluation utilized the Mozilla Common Voice dataset. The results demonstrate that the adversarial examples generated using the proposed method, which only transform the specified word or phrase by adding noise to that specific region, can successfully fool the speech recognition system into misclassifying them as the intended target sentence. © The Author(s) 2025.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Advances in Temporal Point Processes: Bayesian, Deep, and LLM Approaches

arXiv

引用

arXiv 2025年

作者： Zhou, Feng Kong, Quyu Zhang, Yixuan Center for Applied Statistics School of Statistics Renmin University of China China Alibaba Cloud China School of Statistics and Data Science Southeast University China

Temporal point processes (TPPs) are stochastic process models used to characterize event sequences occurring in continuous time. Traditional statistical TPPs have a long-standing history, with numerous models proposed and successfully applied across diverse domains. In recent years, advances in deep learning have spurred the development of neural TPPs, enabling greater flexibility and expressiveness in capturing complex temporal dynamics. The emergence of large language models (LLMs) has further sparked excitement, offering new possibilities for modeling and analyzing event sequences by leveraging their rich contextual understanding. This survey presents a comprehensive review of recent research on TPPs from three perspectives: Bayesian, deep learning, and LLM approaches. We begin with a review of the fundamental concepts of TPPs, followed by an in-depth discussion of model design and parameter estimation techniques in these three frameworks. We also revisit classic application areas of TPPs to highlight their practical relevance. Finally, we outline challenges and promising directions for future research. © 2025, CC BY.

关键词： Stochastic models

来源：评论

学校读者我要写书评

暂无评论

Bayesian analysis of nonlinear structured latent factor models using a Gaussian Process Prior

arXiv

引用

arXiv 2025年

作者： Zhang, Yimang Wang, Xiaorui Shi, Jian Qing Department of Statistics and Data Science Southern University of Science and Technology China National Center for Applied Mathematics Shenzhen China

Factor analysis models are widely utilized in social and behavioral sciences, such as psychology, education, and marketing, to measure unobservable latent traits. In this article, we introduce a nonlinear structured latent factor analysis model which is more flexible to characterize the relationship between manifest variables and latent factors. The confirmatory identifiability of the latent factor is discussed, ensuring the substantive interpretation of the latent factors. A Bayesian approach with a Gaussian process prior is proposed to estimate the unknown nonlinear function and the unknown parameters. Asymptotic results are established, including structural identifiability of the latent factors, consistency of the estimates of the unknown parameters and the unknown nonlinear function. Simulation studies and a real data analysis are conducted to investigate the performance of the proposed method. Simulation studies show our proposed method performs well in handling nonlinear model and successfully identifies the latent factors. Our analysis incorporates oil flow data, allowing us to uncover the underlying structure of latent nonlinear patterns. © 2025, CC BY-NC-ND.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

ON STOCHASTIC CONTROL PROBLEMS WITH HIGHER-ORDER MOMENTS

引用

SIAM Journal on Control and Optimization 2025年第3期63卷 1560-1589页

作者： Wang, Yike Liu, Jingzhen Bensoussan, Alain Cedric Yiu, Ka-Fai Wei, Jiaqin School of Finance Chongqing Technology and Business University Chongqing China China Institute for Actuarial Science Central University of Finance and Economics Beijing China SNaveen Jindal School of Management University of Texas at Dallas DallasTX75080 United States Department of Applied Mathematics Hong Kong Polytechnic University Hong Kong Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University Shanghai China

In this paper, we focus on a class of time-inconsistent stochastic control problems, where the objective function includes the mean and several higher-order central moments of the terminal value of state. To tackle the time-inconsistency, we seek both the closed-loop and the open-loop Nash equilibrium controls as time-consistent solutions. We establish a partial differential equation (PDE) system for deriving a closed-loop Nash equilibrium control, which does not include the equilibrium value function and is different from the extended Hamilton-Jacobi-Bellman (HJB) equations as in Bj\" ork, Khapko, and Murgoci [Finance Stoch., 21 (2017), pp. 331-360]. We show that our PDE system is equivalent to the extended HJB equations that seem difficult to solve for our higher-order moment problems. In deriving an open-loop Nash equilibrium control, due to the nonseparable higher-order moments in the objective function, we make some moment estimates in addition to the standard perturbation argument for developing a maximum principle. Then, the problem is reduced to solving a flow of forward-backward stochastic differential equations. In particular, we investigate linear controlled dynamics and some objective functions affine in the mean. The closed-loop and the open-loop Nash equilibrium controls are identical, which are independent of the state value, the random path, and the preference on the odd-order central moments. By sending the highest order of central moments to infinity, we obtain the time-consistent solutions to some control problems whose objective functions include some penalty functions for deviation. © 2025 Society for Industrial and applied Mathematics.

关键词： Stochastic control systems

来源：评论

学校读者我要写书评

暂无评论

Convolution Type of Metaplectic Cohen's Distribution Time-Frequency Analysis Theory, Method and Technology

引用

IEEE Transactions on Information Theory 2025年第3期71卷 2292-2314页

作者： Cui, Manjun Zhang, Zhichao Han, Jie Chen, Yunjie Cao, Chunzheng Nanjing University of Information Science and Technology School of Mathematics and Statistics Center for Applied Mathematics of Jiangsu Province Jiangsu International Joint Laboratory on System Modeling and Data Analysis Nanjing210044 China Nanjing University of Information Science and Technology School of Mathematics and Statistics Nanjing210044 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Hainan Normal University Key Laboratory of Computational Science and Application of Hainan Province Haikou571158 China Nanjing University of Information Science and Technology School of Remote Sensing and Geomatics Engineering Nanjing210044 China

The conventional Cohen's distribution can't meet the requirement of additive noises jamming signals high-performance denoising under the condition of low signal-to-noise ratio, it is necessary to integrate the metaplectic transform for non-stationary signal fractional domain time-frequency analysis. In this paper, we embark on blending time-frequency operators and coordinate operator fractionizations to formulate the definition of the joint fractionizations metaplectic Wigner distribution (JFMWD), based on which we integrate the generalized metaplectic convolution to address the unified representation issue of the convolution type of metaplectic Cohen's distribution (CMCD), whose special cases and essential properties are also derived. We blend Wiener filter principle and fractional domain filter mechanism of the metaplectic transform to design the least-squares adaptive filter method in the JFMWD domain, giving birth to the least-squares adaptive filter-based CMCD whose kernel function can be adjusted with the input signal automatically to achieve the minimum mean-square error (MSE) denoising in Wigner distribution domain. We discuss the optimal symplectic matrices selection strategy of the proposed adaptive CMCD through the minimum MSE minimization modeling and solving. Some examples are also carried out to demonstrate that the proposed filtering method outperforms some state-of-the-arts including the classical Gaussian filter, the Cohen's distribution filtering methods with fixed kernel functions, the classical Wiener filter, the adaptive generalized linear canonical convolution-based filtering method, the adaptive Cohen's distribution filtering method, the adaptive JFMWD-based Cohen's distribution filtering method, and the adaptive generalized metaplectic convolution-based Cohen's distribution filtering method in noise suppression. © 1963-2012 IEEE.

关键词： Adaptive filtering

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：