Statistics & Probability Letters最新文献

{"title":"On the mean absorption time of multiple coalescing particles with removal at previously visited vertices","authors":"Mario A. Estrada ,&nbsp;Alex D. Ramos ,&nbsp;Pablo M. Rodriguez","doi":"10.1016/j.spl.2025.110523","DOIUrl":"10.1016/j.spl.2025.110523","url":null,"abstract":"<div><div>We study a stochastic process of multiple coalescing particles. In our process, <span><math><mi>k</mi></math></span> particles begin at an arbitrary but fixed vertex of a complete graph. Each particle performs an independent discrete-time symmetric random walk on the graph. When two or more particles meet at a given vertex, they merge into a single particle that continues the random walk through the graph. If a particle jumps to a vertex that has been previously visited by another particle, it is removed from the system. We analyze the asymptotic behavior of the absorption time of the process; i.e., the number of steps until the last particle is removed from the system.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"227 ","pages":"Article 110523"},"PeriodicalIF":0.7,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transportation cost-information inequality for non-linear time-fractional stochastic heat equation driven by space–time white noise","authors":"Ruinan Li ,&nbsp;Yumeng Li","doi":"10.1016/j.spl.2025.110519","DOIUrl":"10.1016/j.spl.2025.110519","url":null,"abstract":"<div><div>We establish transportation cost-information inequalities <span><math><mrow><msub><mrow><mi>T</mi></mrow><mrow><mi>2</mi></mrow></msub><mrow><mo>(</mo><mi>C</mi><mo>)</mo></mrow></mrow></math></span> for solutions of nonlinear stochastic partial differential equation of fractional order in both space and time variables with deterministic and bounded initial conditions: <span><span><span><math><mrow><msubsup><mrow><mi>∂</mi></mrow><mrow><mi>t</mi></mrow><mrow><mi>β</mi></mrow></msubsup><mi>u</mi><mrow><mo>(</mo><mi>t</mi><mo>,</mo><mi>x</mi><mo>)</mo></mrow><mo>+</mo><msup><mrow><mrow><mo>(</mo><mo>−</mo><mi>Δ</mi><mo>)</mo></mrow></mrow><mrow><mi>α</mi><mo>/</mo><mn>2</mn></mrow></msup><mi>u</mi><mrow><mo>(</mo><mi>t</mi><mo>,</mo><mi>x</mi><mo>)</mo></mrow><mo>=</mo><msubsup><mrow><mi>I</mi></mrow><mrow><mi>t</mi></mrow><mrow><mi>γ</mi></mrow></msubsup><mfenced><mrow><mi>σ</mi><mrow><mo>(</mo><mi>u</mi><mrow><mo>(</mo><mi>t</mi><mo>,</mo><mi>x</mi><mo>)</mo></mrow><mo>)</mo></mrow><mover><mrow><mi>W</mi></mrow><mrow><mo>̇</mo></mrow></mover><mrow><mo>(</mo><mi>t</mi><mo>,</mo><mi>x</mi><mo>)</mo></mrow></mrow></mfenced><mspace></mspace><mspace></mspace><mtext>in</mtext><mrow><mo>(</mo><mn>0</mn><mo>,</mo><mi>∞</mi><mo>)</mo></mrow><mo>×</mo><msup><mrow><mi>R</mi></mrow><mrow><mi>d</mi></mrow></msup><mo>,</mo></mrow></math></span></span></span>where <span><math><mrow><mi>α</mi><mo>&gt;</mo><mn>0</mn></mrow></math></span>, <span><math><mrow><mi>β</mi><mo>∈</mo><mrow><mo>(</mo><mn>0</mn><mo>,</mo><mn>2</mn><mo>]</mo></mrow></mrow></math></span>, <span><math><mrow><mi>γ</mi><mo>≥</mo><mn>0</mn></mrow></math></span>, <span><math><msubsup><mrow><mi>∂</mi></mrow><mrow><mi>t</mi></mrow><mrow><mi>β</mi></mrow></msubsup></math></span> is the Caputo fractional derivative, <span><math><mrow><mo>−</mo><msup><mrow><mrow><mo>(</mo><mo>−</mo><mi>Δ</mi><mo>)</mo></mrow></mrow><mrow><mi>α</mi><mo>/</mo><mn>2</mn></mrow></msup></mrow></math></span> is the fractional/power of Laplacian, <span><math><msubsup><mrow><mi>I</mi></mrow><mrow><mi>t</mi></mrow><mrow><mi>γ</mi></mrow></msubsup></math></span> is the Riemann–Liouville integral operator, <span><math><mrow><mover><mrow><mi>W</mi></mrow><mrow><mo>̇</mo></mrow></mover><mrow><mo>(</mo><mi>t</mi><mo>,</mo><mi>x</mi><mo>)</mo></mrow></mrow></math></span> is a space–time white noise, and <span><math><mrow><mi>σ</mi><mo>:</mo><mi>R</mi><mo>→</mo><mi>R</mi></mrow></math></span> is a bounded and Lipschitz function. Since the space variable is defined on the unbounded domain <span><math><msup><mrow><mi>R</mi></mrow><mrow><mi>d</mi></mrow></msup></math></span>, the inequalities are proved under a weighted <span><math><msup><mrow><mi>L</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>-norm in the spatial domain.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"227 ","pages":"Article 110519"},"PeriodicalIF":0.7,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Saddlepoint approximation for the kernel density estimator","authors":"Cyrille Joutard","doi":"10.1016/j.spl.2025.110522","DOIUrl":"10.1016/j.spl.2025.110522","url":null,"abstract":"<div><div>Assuming real and independent and identically distributed observations, we obtain a classical pointwise saddlepoint approximation for the tail probability of the Parzen–Rosenblatt density estimator. This saddlepoint approximation is similar to the one which was first obtained by Daniels (1987) for the sample mean via the method of indirect Edgeworth expansion.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"226 ","pages":"Article 110522"},"PeriodicalIF":0.7,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144828123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strong law of large numbers for random walks in weakly dependent random scenery","authors":"Sadillo Sharipov","doi":"10.1016/j.spl.2025.110521","DOIUrl":"10.1016/j.spl.2025.110521","url":null,"abstract":"<div><div>In this brief note, we study the strong law of large numbers for random walks in random scenery. Under the assumptions that the random scenery is non-stationary and satisfies weakly dependent condition with an appropriate rate, we establish strong law of large numbers for random walks in random scenery. Our results extend the known results in the literature.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"227 ","pages":"Article 110521"},"PeriodicalIF":0.7,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Testing for fractional cointegration in subsamples by allowing for structural breaks","authors":"Tom Jannik Kreye","doi":"10.1016/j.spl.2025.110518","DOIUrl":"10.1016/j.spl.2025.110518","url":null,"abstract":"<div><div>This paper proposes tests for fractional cointegration accommodating structural breaks via a time-varying memory parameter in the cointegration error. Monte Carlo simulations demonstrate that the proposed tests exhibit superior finite sample performance relative to their full-sample competitor.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"227 ","pages":"Article 110518"},"PeriodicalIF":0.7,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised outlier detection for compositional data","authors":"Fabio Divino ,&nbsp;Salme Kärkkäinen ,&nbsp;Antonello Maruotti","doi":"10.1016/j.spl.2025.110510","DOIUrl":"10.1016/j.spl.2025.110510","url":null,"abstract":"<div><div>This paper proposes an unsupervised method for outlier detection in compositional data via an appropriate transformation and modeling with a contaminated normal distribution. Parameters are estimated using an Expectation-Conditional-Maximization algorithm. Simulation studies and real-data applications demonstrate the method’s robustness and superior performance compared to three existing alternatives.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"227 ","pages":"Article 110510"},"PeriodicalIF":0.7,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144826610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dispersive and hazard rate orderings of parallel systems with exponential dependent components","authors":"Ghadah Alomani ,&nbsp;Anfal A. Alqefari ,&nbsp;Mohamed Kayid","doi":"10.1016/j.spl.2025.110514","DOIUrl":"10.1016/j.spl.2025.110514","url":null,"abstract":"<div><div>Based on dispersive and hazard rate orders, sharp lower bounds for the maximum order statistic of heterogeneous dependent exponential random variables whose joint distribution is modeled via an Archimedean copula are found. Our results extend and refine a recent contribution by Amini-Seresht, Khaledi, and Izadkhah (Statist. Probab. Lett. 215 (2024), 110242), by providing tighter bounds under convenient conditions on the generator function of the Archimedean copula.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"227 ","pages":"Article 110514"},"PeriodicalIF":0.7,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A principal mixed-order moments method for CKMS in dimension reduction","authors":"Zheng Li ,&nbsp;Yunhao Wang ,&nbsp;Wei Gao","doi":"10.1016/j.spl.2025.110506","DOIUrl":"10.1016/j.spl.2025.110506","url":null,"abstract":"<div><div>This paper presents a new Sufficient Dimension Reduction (SDR) approach, termed the Principal Mixed-order Moments (PMoM) method, for estimating the central <span><math><mi>K</mi></math></span>th moment subspace (CKMS). We develop a computational algorithm to implement PMoM and establish its consistency properties. To evaluate its effectiveness and efficiency, we conduct Monte Carlo simulations. Notably, PMoM demonstrates strong performance, especially in cases where the variance of individual components varies significantly, particularly under an elliptical distribution of predictor variables. Real data analysis in the Energy Efficiency dataset confirms the effectiveness of PMoM, highlighting its practicality in high-dimensional data reduction.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"226 ","pages":"Article 110506"},"PeriodicalIF":0.7,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rescaled Bayes factors: A class of e-variables","authors":"Thorsten Dickhaus","doi":"10.1016/j.spl.2025.110511","DOIUrl":"10.1016/j.spl.2025.110511","url":null,"abstract":"<div><div>A class of e-variables is introduced and analyzed. Some examples are presented.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"226 ","pages":"Article 110511"},"PeriodicalIF":0.7,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144770952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Confidence set for mixture order selection","authors":"Alessandro Casa,&nbsp;Davide Ferrari","doi":"10.1016/j.spl.2025.110509","DOIUrl":"10.1016/j.spl.2025.110509","url":null,"abstract":"<div><div>A fundamental challenge in approximating an unknown density using finite Gaussian mixture models is selecting the number of mixture components, also known as order. Traditional approaches choose a single best model using information criteria. However, often models with different orders yield similar fits, leading to substantial model selection uncertainty and making it challenging to identify the optimal number of components. In this paper, we introduce the Model Selection Confidence Set (MSCS) for order selection in Gaussian mixtures – a set-valued estimator that, with a predefined confidence level, includes the true mixture order across repeated samples. Rather than selecting a single model, our MSCS identifies all plausible orders by determining whether each candidate model is at least as plausible as the best-selected one, using a screening based on a penalized likelihood ratio statistic. We provide theoretical guarantees for asymptotic coverage, and demonstrate its practical advantages through simulations and real data analysis.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"226 ","pages":"Article 110509"},"PeriodicalIF":0.7,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}