Pub Date : 2025-10-17DOI: 10.1016/j.spl.2025.110576
Longxiang Fang , Yu Ruan , N. Balakrishnan
Stochastic comparisons are quite helpful in optimizing system designs by evaluating the performance of different configurations and ensuring that critical systems meet reliability standards under diverse conditions. In this paper, we discuss stochastic comparisons of lifetimes of two-series–parallel systems with independent components randomly chosen from two different batches. We assume the components from the first batch is more reliable than the components from the second batch. Then, in the case of two-series–parallel system, we prove that the reliability of the system increases in terms of the usual stochastic order, as the random number, , taking values in , of components chosen from the first batch increases in increasing concave order or the random number, , taking values in , of components chosen from the first batch decreases in increasing convex order. We also present some numerical examples to illustrate all the results established here.
{"title":"Stochastic comparisons of two-series–parallel systems with independent components randomly chosen from two batches","authors":"Longxiang Fang , Yu Ruan , N. Balakrishnan","doi":"10.1016/j.spl.2025.110576","DOIUrl":"10.1016/j.spl.2025.110576","url":null,"abstract":"<div><div>Stochastic comparisons are quite helpful in optimizing system designs by evaluating the performance of different configurations and ensuring that critical systems meet reliability standards under diverse conditions. In this paper, we discuss stochastic comparisons of lifetimes of two-series–parallel systems with <span><math><mrow><mn>2</mn><mi>n</mi></mrow></math></span> independent components randomly chosen from two different batches. We assume the <span><math><mi>n</mi></math></span> components from the first batch is more reliable than the <span><math><mi>n</mi></math></span> components from the second batch. Then, in the case of two-series–parallel system, we prove that the reliability of the system increases in terms of the usual stochastic order, as the random number, <span><math><mi>K</mi></math></span>, taking values in <span><math><mrow><mo>{</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>,</mo><mo>…</mo><mo>,</mo><mrow><mo>[</mo><mfrac><mrow><mi>n</mi><mo>+</mo><mn>1</mn></mrow><mrow><mn>2</mn></mrow></mfrac><mo>]</mo></mrow><mo>}</mo></mrow></math></span>, of components chosen from the first batch increases in increasing concave order or the random number, <span><math><mi>K</mi></math></span>, taking values in <span><math><mrow><mo>{</mo><mrow><mo>[</mo><mfrac><mrow><mi>n</mi><mo>+</mo><mn>1</mn></mrow><mrow><mn>2</mn></mrow></mfrac><mo>]</mo></mrow><mo>+</mo><mn>1</mn><mo>,</mo><mo>…</mo><mo>,</mo><mi>n</mi><mo>}</mo></mrow></math></span>, of components chosen from the first batch decreases in increasing convex order. We also present some numerical examples to illustrate all the results established here.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"229 ","pages":"Article 110576"},"PeriodicalIF":0.7,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324181","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}
Pub Date : 2025-10-11DOI: 10.1016/j.spl.2025.110575
Long-Hao Xu, Tim Friede
Continuous monitoring is becoming more popular due to its significant benefits, including reducing sample sizes and reaching earlier conclusions. In general, it involves monitoring nuisance parameters (e.g., the variance of outcomes) until a specific condition is satisfied. The blinded method, which does not require revealing group assignments, was recommended because it maintains the integrity of the experiment and mitigates potential bias. Although Friede and Miller (2012) investigated the characteristics of blinded continuous monitoring through simulation studies, its theoretical properties are not fully explored. In this paper, we aim to fill this gap by presenting the asymptotic and finite-sample properties of the blinded continuous monitoring for continuous outcomes. Furthermore, we examine the impact of using blinded versus unblinded variance estimators in the context of continuous monitoring. Simulation results are also provided to evaluate finite-sample performance and to support the theoretical findings.
{"title":"A note on blinded continuous monitoring for continuous outcomes","authors":"Long-Hao Xu, Tim Friede","doi":"10.1016/j.spl.2025.110575","DOIUrl":"10.1016/j.spl.2025.110575","url":null,"abstract":"<div><div>Continuous monitoring is becoming more popular due to its significant benefits, including reducing sample sizes and reaching earlier conclusions. In general, it involves monitoring nuisance parameters (e.g., the variance of outcomes) until a specific condition is satisfied. The blinded method, which does not require revealing group assignments, was recommended because it maintains the integrity of the experiment and mitigates potential bias. Although Friede and Miller (2012) investigated the characteristics of blinded continuous monitoring through simulation studies, its theoretical properties are not fully explored. In this paper, we aim to fill this gap by presenting the asymptotic and finite-sample properties of the blinded continuous monitoring for continuous outcomes. Furthermore, we examine the impact of using blinded versus unblinded variance estimators in the context of continuous monitoring. Simulation results are also provided to evaluate finite-sample performance and to support the theoretical findings.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"228 ","pages":"Article 110575"},"PeriodicalIF":0.7,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321939","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}
Pub Date : 2025-10-10DOI: 10.1016/j.spl.2025.110561
Chie Taguchi , Manabu Kuroki
In the context of statistical causal inference using linear structural equation models, researchers in the field of artificial intelligence and statistical science have developed several identification conditions for evaluating causal effects. However, there are some scenarios where several identification conditions can be applied simultaneously to estimate causal effects. To enhance estimation accuracy, we focus on five key identification conditions: the back-door criterion, the front-door criterion, the front-door-like criterion, the conditional instrumental variable condition, and the effect restoration condition. We then compare these five identification conditions in terms of estimation accuracy (asymptotic variance) and conclude that, in some cases, the qualitative comparison of estimation accuracy among these identification conditions can be directly assessed from the graphical structure, even before statistical data are collected.
{"title":"Variance-based difference between graphical identification conditions of causal effects in linear structural equation models","authors":"Chie Taguchi , Manabu Kuroki","doi":"10.1016/j.spl.2025.110561","DOIUrl":"10.1016/j.spl.2025.110561","url":null,"abstract":"<div><div>In the context of statistical causal inference using linear structural equation models, researchers in the field of artificial intelligence and statistical science have developed several identification conditions for evaluating causal effects. However, there are some scenarios where several identification conditions can be applied simultaneously to estimate causal effects. To enhance estimation accuracy, we focus on five key identification conditions: the back-door criterion, the front-door criterion, the front-door-like criterion, the conditional instrumental variable condition, and the effect restoration condition. We then compare these five identification conditions in terms of estimation accuracy (asymptotic variance) and conclude that, in some cases, the qualitative comparison of estimation accuracy among these identification conditions can be directly assessed from the graphical structure, even before statistical data are collected.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"228 ","pages":"Article 110561"},"PeriodicalIF":0.7,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321940","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}
Pub Date : 2025-10-04DOI: 10.1016/j.spl.2025.110573
Xin-Yi Xu , Jiang-Feng Wang , Kang Hu , Shan He , Yu Xia
This paper investigates the asymptotic properties of local linear quantile regression estimators for spatial data generated by strictly stationary and associated spatial processes . We study local linear estimators for both the conditional quantile function and its first-order partial derivatives. Under appropriate regularity conditions, we derive the Bahadur representation for these estimators, which is utilized to establish their joint asymptotic normality. To assess finite-sample performance, we conduct Monte Carlo simulations in a two-dimensional space (). The results demonstrate the applicability of the proposed estimators and confirm the theoretical asymptotic properties.
{"title":"Spatial local linear quantile regression under association","authors":"Xin-Yi Xu , Jiang-Feng Wang , Kang Hu , Shan He , Yu Xia","doi":"10.1016/j.spl.2025.110573","DOIUrl":"10.1016/j.spl.2025.110573","url":null,"abstract":"<div><div>This paper investigates the asymptotic properties of local linear quantile regression estimators for spatial data generated by strictly stationary and associated spatial processes <span><math><mrow><mo>{</mo><mrow><mo>(</mo><msub><mrow><mi>Y</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>,</mo><msub><mrow><mi>X</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>)</mo></mrow><mo>,</mo><mi>i</mi><mo>∈</mo><msup><mrow><mi>Z</mi></mrow><mrow><mi>N</mi></mrow></msup><mo>}</mo></mrow></math></span>. We study local linear estimators for both the conditional quantile function <span><math><mrow><msub><mrow><mi>q</mi></mrow><mrow><mi>p</mi></mrow></msub><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow></mrow></math></span> and its first-order partial derivatives. Under appropriate regularity conditions, we derive the Bahadur representation for these estimators, which is utilized to establish their joint asymptotic normality. To assess finite-sample performance, we conduct Monte Carlo simulations in a two-dimensional space (<span><math><mrow><mi>N</mi><mo>=</mo><mn>2</mn></mrow></math></span>). The results demonstrate the applicability of the proposed estimators and confirm the theoretical asymptotic properties.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"228 ","pages":"Article 110573"},"PeriodicalIF":0.7,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269905","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}
Pub Date : 2025-09-30DOI: 10.1016/j.spl.2025.110572
Yuri Kifer
We obtain ergodic theorems and a version of the Erdös–Rènyi law of large numbers for multiple iterated sums and integrals of the form , and where and are stationary vector stochastic processes.
{"title":"Iterated ergodic theorems and Erdös–Rényi law of large numbers","authors":"Yuri Kifer","doi":"10.1016/j.spl.2025.110572","DOIUrl":"10.1016/j.spl.2025.110572","url":null,"abstract":"<div><div>We obtain ergodic theorems and a version of the Erdös–Rènyi law of large numbers for multiple iterated sums and integrals of the form <span><math><mrow><msup><mrow><mi>Σ</mi></mrow><mrow><mrow><mo>(</mo><mi>ν</mi><mo>)</mo></mrow></mrow></msup><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>=</mo><msub><mrow><mo>∑</mo></mrow><mrow><mn>0</mn><mo>≤</mo><msub><mrow><mi>k</mi></mrow><mrow><mn>1</mn></mrow></msub><mo><</mo><mo>.</mo><mo>.</mo><mo>.</mo><mo><</mo><msub><mrow><mi>k</mi></mrow><mrow><mi>ν</mi></mrow></msub><mo>≤</mo><mi>t</mi></mrow></msub><mi>ξ</mi><mrow><mo>(</mo><msub><mrow><mi>k</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>)</mo></mrow><mo>⊗</mo><mo>⋯</mo><mo>⊗</mo><mi>ξ</mi><mrow><mo>(</mo><msub><mrow><mi>k</mi></mrow><mrow><mi>ν</mi></mrow></msub><mo>)</mo></mrow></mrow></math></span>, <span><math><mrow><mi>t</mi><mo>∈</mo><mrow><mo>[</mo><mn>0</mn><mo>,</mo><mi>T</mi><mo>]</mo></mrow></mrow></math></span> and <span><math><mrow><msup><mrow><mi>Σ</mi></mrow><mrow><mrow><mo>(</mo><mi>ν</mi><mo>)</mo></mrow></mrow></msup><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>=</mo><msub><mrow><mo>∫</mo></mrow><mrow><mn>0</mn><mo>≤</mo><msub><mrow><mi>s</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>≤</mo><mo>⋯</mo><mo>≤</mo><msub><mrow><mi>s</mi></mrow><mrow><mi>ν</mi></mrow></msub><mo>≤</mo><mi>t</mi></mrow></msub><mi>ξ</mi><mrow><mo>(</mo><msub><mrow><mi>s</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>)</mo></mrow><mo>⊗</mo><mo>⋯</mo><mo>⊗</mo><mi>ξ</mi><mrow><mo>(</mo><msub><mrow><mi>s</mi></mrow><mrow><mi>ν</mi></mrow></msub><mo>)</mo></mrow><mi>d</mi><msub><mrow><mi>s</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>⋯</mo><mi>d</mi><msub><mrow><mi>s</mi></mrow><mrow><mi>ν</mi></mrow></msub></mrow></math></span> where <span><math><msub><mrow><mrow><mo>{</mo><mi>ξ</mi><mrow><mo>(</mo><mi>k</mi><mo>)</mo></mrow><mo>}</mo></mrow></mrow><mrow><mo>−</mo><mi>∞</mi><mo><</mo><mi>k</mi><mo><</mo><mi>∞</mi></mrow></msub></math></span> and <span><math><msub><mrow><mrow><mo>{</mo><mi>ξ</mi><mrow><mo>(</mo><mi>s</mi><mo>)</mo></mrow><mo>}</mo></mrow></mrow><mrow><mo>−</mo><mi>∞</mi><mo><</mo><mi>s</mi><mo><</mo><mi>∞</mi></mrow></msub></math></span> are stationary vector stochastic processes.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"228 ","pages":"Article 110572"},"PeriodicalIF":0.7,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222580","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}
Pub Date : 2025-09-26DOI: 10.1016/j.spl.2025.110564
Daniela Angulo, Susan Murray
Bivariate time-to-event data, subject to right censoring, frequently arise in medical research. This paper introduces a novel nonparametric multiple imputation (MI) procedure for analyzing censored bivariate time-to-event data. Our methodology offers a straightforward, easy-to-implement inverse transform MI method that effectively captures the joint distribution of bivariate random variables through the imputation of censored event-times.
{"title":"Multiple imputation of censored bivariate event-times via inverse transform and nonparametric Gibbs sampling","authors":"Daniela Angulo, Susan Murray","doi":"10.1016/j.spl.2025.110564","DOIUrl":"10.1016/j.spl.2025.110564","url":null,"abstract":"<div><div>Bivariate time-to-event data, subject to right censoring, frequently arise in medical research. This paper introduces a novel nonparametric multiple imputation (MI) procedure for analyzing censored bivariate time-to-event data. Our methodology offers a straightforward, easy-to-implement inverse transform MI method that effectively captures the joint distribution of bivariate random variables through the imputation of censored event-times.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"228 ","pages":"Article 110564"},"PeriodicalIF":0.7,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269461","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}
Pub Date : 2025-09-25DOI: 10.1016/j.spl.2025.110563
Haiyue Su , Zhiming Xia , Wenyuan Shang , Meili Shi
For high-throughput low-rank data, CANDECOMP/PARAFAC () decomposition is frequently employed to reduce the dimensionality to a manageable level. In this article, we consider a Vector-Tensor linear regression model, where the low-rank structure is expressed through CP decomposition, and the change-point structure is incorporated into the multi-array coefficients. A novel procedure is proposed to jointly detect the change-point and estimate the tensor structure by minimizing the sum of squared residuals. The associated algorithm is developed based on Alternating Least Squares (ALS) algorithm, and is computationally efficient and scalable. Furthermore, we establish the consistency of the change-point estimator under a set of general conditions. Simulations and empirical studies illustrate the validity and effectiveness.
{"title":"Change-point detection in Vector-Tensor linear model","authors":"Haiyue Su , Zhiming Xia , Wenyuan Shang , Meili Shi","doi":"10.1016/j.spl.2025.110563","DOIUrl":"10.1016/j.spl.2025.110563","url":null,"abstract":"<div><div>For high-throughput low-rank data, CANDECOMP/PARAFAC (<span><math><mi>CP</mi></math></span>) decomposition is frequently employed to reduce the dimensionality to a manageable level. In this article, we consider a Vector-Tensor linear regression model, where the low-rank structure is expressed through CP decomposition, and the change-point structure is incorporated into the multi-array coefficients. A novel procedure is proposed to jointly detect the change-point and estimate the tensor structure by minimizing the sum of squared residuals. The associated algorithm is developed based on Alternating Least Squares (ALS) algorithm, and is computationally efficient and scalable. Furthermore, we establish the consistency of the change-point estimator under a set of general conditions. Simulations and empirical studies illustrate the validity and effectiveness.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"228 ","pages":"Article 110563"},"PeriodicalIF":0.7,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222582","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}
Pub Date : 2025-09-25DOI: 10.1016/j.spl.2025.110562
Blair Robertson, Chris Price, Marco Reale
Doubly balanced samples from spatial populations have approximate balance on auxiliary variables and spread over spatial coordinates. This article shows that doubly balanced sampling is also efficient on non-spatial populations when we balance on auxiliary variables and spread over the space spanned by them. Numerical results on three example applications show that our extension of doubly balanced sampling works well in practice.
{"title":"Double dipping with balanced sampling","authors":"Blair Robertson, Chris Price, Marco Reale","doi":"10.1016/j.spl.2025.110562","DOIUrl":"10.1016/j.spl.2025.110562","url":null,"abstract":"<div><div>Doubly balanced samples from spatial populations have approximate balance on auxiliary variables and spread over spatial coordinates. This article shows that doubly balanced sampling is also efficient on non-spatial populations when we balance on auxiliary variables and spread over the space spanned by them. Numerical results on three example applications show that our extension of doubly balanced sampling works well in practice.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"228 ","pages":"Article 110562"},"PeriodicalIF":0.7,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160357","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}
Pub Date : 2025-09-24DOI: 10.1016/j.spl.2025.110558
Eugenio Clerico , Hamish E. Flynn , Patrick Rebeschini
We consider the problem of deriving uniform confidence bands for the mean of a monotonic stochastic process, such as the cumulative distribution function (CDF) of a random variable, based on a sequence of i.i.d. observations. Our approach leverages the coin-betting framework, and inherits several favourable characteristics of coin-betting methods. In particular, for each point in the domain of the mean function, we obtain anytime-valid confidence intervals that are numerically tight and adapt to the variance of the observations. To derive uniform confidence bands, we employ a continuous union bound that crucially leverages monotonicity. In the case of CDF estimation, we also exploit the fact that the empirical CDF is piece-wise constant to obtain simple confidence bands that can be easily computed. In simulations, we find that our confidence bands for the CDF achieve state-of-the-art performance.
{"title":"Uniform mean estimation for monotonic processes","authors":"Eugenio Clerico , Hamish E. Flynn , Patrick Rebeschini","doi":"10.1016/j.spl.2025.110558","DOIUrl":"10.1016/j.spl.2025.110558","url":null,"abstract":"<div><div>We consider the problem of deriving uniform confidence bands for the mean of a monotonic stochastic process, such as the cumulative distribution function (CDF) of a random variable, based on a sequence of i.i.d. observations. Our approach leverages the coin-betting framework, and inherits several favourable characteristics of coin-betting methods. In particular, for each point in the domain of the mean function, we obtain anytime-valid confidence intervals that are numerically tight and adapt to the variance of the observations. To derive uniform confidence bands, we employ a continuous union bound that crucially leverages monotonicity. In the case of CDF estimation, we also exploit the fact that the empirical CDF is piece-wise constant to obtain simple confidence bands that can be easily computed. In simulations, we find that our confidence bands for the CDF achieve state-of-the-art performance.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"228 ","pages":"Article 110558"},"PeriodicalIF":0.7,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160353","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}
Pub Date : 2025-09-24DOI: 10.1016/j.spl.2025.110560
M.C. Jones , Arthur Pewsey
Rosco et al. (2011) introduced and studied the sinh-arcsinh (SAS-) distribution. In this article, we introduce a modified version of that distribution which we call the eschewed sinh-arcsinh (ESAS-) distribution. The new proposal proves to be somewhat simpler than the former and, on balance, given the pros and cons listed in the article, we now recommend the ESAS- distribution over the SAS- distribution as the preferable version of a sinh-arcsinh distribution.
Rosco et al.（2011）介绍并研究了sinh-arcsinh t （SAS-t）分布。在本文中，我们将介绍该分布的一个修改版本，我们将其称为回避的sinh-arcsinh t （esa -t）分布。事实证明，新提案比前一个提案要简单一些，总的来说，考虑到本文中列出的优点和缺点，我们现在推荐ESAS-t发行版，而不是SAS-t发行版，作为sinh-arcsinh -t发行版的首选版本。
{"title":"The eschewed sinh-arcsinh t distribution","authors":"M.C. Jones , Arthur Pewsey","doi":"10.1016/j.spl.2025.110560","DOIUrl":"10.1016/j.spl.2025.110560","url":null,"abstract":"<div><div>Rosco et al. (2011) introduced and studied the sinh-arcsinh <span><math><mi>t</mi></math></span> (SAS-<span><math><mi>t</mi></math></span>) distribution. In this article, we introduce a modified version of that distribution which we call the eschewed sinh-arcsinh <span><math><mi>t</mi></math></span> (ESAS-<span><math><mi>t</mi></math></span>) distribution. The new proposal proves to be somewhat simpler than the former and, on balance, given the pros and cons listed in the article, we now recommend the ESAS-<span><math><mi>t</mi></math></span> distribution over the SAS-<span><math><mi>t</mi></math></span> distribution as the preferable version of a sinh-arcsinh <span><math><mi>t</mi></math></span> distribution.</div></div>","PeriodicalId":49475,"journal":{"name":"Statistics & Probability Letters","volume":"228 ","pages":"Article 110560"},"PeriodicalIF":0.7,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222581","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}
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