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Comparison and equality of generalized (psi )-estimators 广义(psi ) -估计量的比较与等价
IF 0.8 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2024-12-04 DOI: 10.1007/s10463-024-00916-7
Mátyás Barczy, Zsolt Páles
{"title":"Comparison and equality of generalized (psi )-estimators","authors":"Mátyás Barczy,&nbsp;Zsolt Páles","doi":"10.1007/s10463-024-00916-7","DOIUrl":"10.1007/s10463-024-00916-7","url":null,"abstract":"<div><p>We solve the comparison problem for generalized <span>(psi )</span>-estimators introduced by Barczy and Páles (<i>arXiv</i>: 2211.06026, 2022). Namely, we derive several necessary and sufficient conditions under which a generalized <span>(psi )</span>-estimator less than or equal to another <span>(psi )</span>-estimator for any sample. We also solve the corresponding equality problem for generalized <span>(psi )</span>-estimators. We also apply our results for some known statistical estimators such as for empirical expectiles and Mathieu-type estimators and for solutions of likelihood equations in case of normal, a Beta-type, Gamma, Lomax (Pareto type II), lognormal and Laplace distributions.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 2","pages":"217 - 250"},"PeriodicalIF":0.8,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143513149","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}
引用次数: 0
Large-sample properties of multiple imputation estimators for parameters of logistic regression with covariates missing at random separately or simultaneously 单独或同时随机缺失协变量的逻辑回归参数的多重估计量的大样本性质
IF 0.8 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2024-12-02 DOI: 10.1007/s10463-024-00914-9
Phuoc-Loc Tran, Shen-Ming Lee, Truong-Nhat Le, Chin-Shang Li
{"title":"Large-sample properties of multiple imputation estimators for parameters of logistic regression with covariates missing at random separately or simultaneously","authors":"Phuoc-Loc Tran,&nbsp;Shen-Ming Lee,&nbsp;Truong-Nhat Le,&nbsp;Chin-Shang Li","doi":"10.1007/s10463-024-00914-9","DOIUrl":"10.1007/s10463-024-00914-9","url":null,"abstract":"<div><p>We examine the asymptotic properties of two multiple imputation (MI) estimators, given in the study of Lee et al. (<u>Computational Statistics</u>, <b>38</b>, 899–934, 2023) for the parameters of logistic regression with both sets of discrete or categorical covariates that are missing at random separately or simultaneously. The proposed estimated asymptotic variances of the two MI estimators address a limitation observed with Rubin’s estimated variances, which lead to underestimate the variances of the two MI estimators (Rubin, 1987, <u>Statistical Analysis with Missing Data</u>, New York:Wiley). Simulation results demonstrate that our two proposed MI methods outperform the complete-case, semiparametric inverse probability weighting, random forest MI using chained equations, and stochastic approximation of expectation-maximization methods. To illustrate the methodology’s practical application, we provide a real data example from a survey conducted at the Feng Chia night market in Taichung City, Taiwan.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 2","pages":"251 - 287"},"PeriodicalIF":0.8,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143513126","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}
引用次数: 0
Random mixture Cox point processes 随机混合Cox点过程
IF 0.8 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2024-11-22 DOI: 10.1007/s10463-024-00915-8
A. C. Micheas
{"title":"Random mixture Cox point processes","authors":"A. C. Micheas","doi":"10.1007/s10463-024-00915-8","DOIUrl":"10.1007/s10463-024-00915-8","url":null,"abstract":"<div><p>We introduce and study a new class of Cox point processes, based on random mixture models of exponential family components for the intensity function of the underlying Poisson process. We investigate theoretical properties of the proposed probability distributions of the point process, as well as provide procedures for parameter estimation using a classical and Bayesian approach. We illustrate the richness of the new models through examples, simulations and real data applications.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 2","pages":"289 - 330"},"PeriodicalIF":0.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143513303","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}
引用次数: 0
Non-explicit formula of boundary crossing probabilities by the Girsanov theorem 用格萨诺夫定理求边界穿越概率的非显式公式
IF 0.8 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2024-11-05 DOI: 10.1007/s10463-024-00917-6
Yoann Potiron
{"title":"Non-explicit formula of boundary crossing probabilities by the Girsanov theorem","authors":"Yoann Potiron","doi":"10.1007/s10463-024-00917-6","DOIUrl":"10.1007/s10463-024-00917-6","url":null,"abstract":"<div><p>This paper derives several formulae for the probability that a Wiener process, which has a stochastic drift and random variance, crosses a one-sided stochastic boundary within a finite time interval. A non-explicit formula is first obtained by the Girsanov theorem when considering an equivalent probability measure in which the boundary is constant and equal to its starting value. A more explicit formula is then achieved by decomposing the Radon–Nikodym derivative inverse. This decomposition expresses it as the product of a random variable, which is measurable with respect to the Wiener process’s final value, and an independent random variable. We also provide an explicit formula based on a strong theoretical assumption. To apply the Girsanov theorem, we assume that the difference between the drift increment and the boundary increment, divided by the standard deviation, is absolutely continuous. Additionally, we assume that its derivative satisfies Novikov’s condition.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 3","pages":"353 - 385"},"PeriodicalIF":0.8,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879622","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}
引用次数: 0
Model free feature screening for large scale and ultrahigh dimensional survival data 大规模和超高维生存数据的无模型特征筛选
IF 0.8 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2024-10-19 DOI: 10.1007/s10463-024-00912-x
Yingli Pan, Haoyu Wang, Zhan Liu
{"title":"Model free feature screening for large scale and ultrahigh dimensional survival data","authors":"Yingli Pan,&nbsp;Haoyu Wang,&nbsp;Zhan Liu","doi":"10.1007/s10463-024-00912-x","DOIUrl":"10.1007/s10463-024-00912-x","url":null,"abstract":"<div><p>This paper provides a novel perspective on feature screening in the analysis of high-dimensional right-censored large-<i>p</i>-large-<i>N</i> survival data. The research introduces a distributed feature screening method known as Aggregated Distance Correlation Screening (ADCS). The proposed screening framework involves expressing the distance correlation measure as a function of multiple component parameters, each of which can be estimated in a distributed manner using a natural U-statistic from data segments. By aggregating the component estimates, a final correlation estimate is obtained, facilitating feature screening. Importantly, this approach does not necessitate any specific model specification for responses or predictors and is effective with heavy-tailed data. The study establishes the consistency of the proposed aggregated correlation estimator <span>(widetilde{omega }_{j})</span> under mild conditions and demonstrates the sure screening property of the ADCS. Empirical results from both simulated and real datasets confirm the efficacy and practicality of the ADCS approach proposed in this paper.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 1","pages":"155 - 190"},"PeriodicalIF":0.8,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912884","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}
引用次数: 0
Improved confidence intervals for nonlinear mixed-effects and nonparametric regression models 改进的非线性混合效应和非参数回归模型的置信区间
IF 0.8 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2024-09-24 DOI: 10.1007/s10463-024-00909-6
Nan Zheng, Noel Cadigan
{"title":"Improved confidence intervals for nonlinear mixed-effects and nonparametric regression models","authors":"Nan Zheng,&nbsp;Noel Cadigan","doi":"10.1007/s10463-024-00909-6","DOIUrl":"10.1007/s10463-024-00909-6","url":null,"abstract":"<div><p>Statistical inference for high-dimensional parameters (HDPs) can leverage their intrinsic correlations, as spatially or temporally close parameters tend to have similar values. This is why nonlinear mixed-effects models (NMMs) are commonly used for HDPs. Conversely, in many practical applications, the random effects (REs) in NMMs are correlated HDPs that should remain constant during repeated sampling for frequentist inference. In both scenarios, the inference should be conditional on REs, instead of marginal inference by integrating out REs. We summarize recent theory of conditional inference for NMM, and then propose a bias-corrected RE predictor and confidence interval (CI). We also extend this methodology to accommodate the case where some REs are not associated with data. Simulation studies indicate our new approach leads to substantial improvement in the conditional coverage rate of RE CIs, including CIs for smooth functions in generalized additive models, compared to the existing method based on marginal inference.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 1","pages":"105 - 126"},"PeriodicalIF":0.8,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912885","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}
引用次数: 0
Information projection approach to smoothed propensity score weighting for handling selection bias under missing at random 信息投影法平滑倾向得分加权处理随机缺失下的选择偏差
IF 0.8 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2024-09-21 DOI: 10.1007/s10463-024-00913-w
Hengfang Wang, Jae Kwang Kim
{"title":"Information projection approach to smoothed propensity score weighting for handling selection bias under missing at random","authors":"Hengfang Wang,&nbsp;Jae Kwang Kim","doi":"10.1007/s10463-024-00913-w","DOIUrl":"10.1007/s10463-024-00913-w","url":null,"abstract":"<div><p>Propensity score weighting is widely used to correct the selection bias in the sample with missing data. The propensity score function is often developed using a model for the response probability, which completely ignores the outcome regression model. In this paper, we explore an alternative approach by developing smoothed propensity score weights that provide a more efficient estimation by removing unnecessary auxiliary variables in the propensity score model. The smoothed propensity score function is obtained by applying the information projection of the original propensity score function to the space that satisfies the moment conditions on the balancing scores obtained from the outcome regression model. By including the covariates for the outcome regression models only in the density ratio model, we can achieve an efficiency gain. Penalized regression is used to identify important covariates. Some limited simulation studies are presented to compare with the existing methods.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 1","pages":"127 - 153"},"PeriodicalIF":0.8,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142913066","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}
引用次数: 0
Estimation of value-at-risk by (L^{p}) quantile regression 用 $$L^{p}$$ 量化回归估算风险价值
IF 0.8 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2024-09-19 DOI: 10.1007/s10463-024-00911-y
Peng Sun, Fuming Lin, Haiyang Xu, Kaizhi Yu
{"title":"Estimation of value-at-risk by (L^{p}) quantile regression","authors":"Peng Sun,&nbsp;Fuming Lin,&nbsp;Haiyang Xu,&nbsp;Kaizhi Yu","doi":"10.1007/s10463-024-00911-y","DOIUrl":"10.1007/s10463-024-00911-y","url":null,"abstract":"<div><p>Exploring more accurate estimates of financial value at risk (VaR) has always been an important issue in applied statistics. To this end either quantile or expectile regression methods are widely employed at present, but an accumulating body of research indicates that <span>(L^{p})</span> quantile regression outweighs both quantile and expectile regression in many aspects. In view of this, the paper extends <span>(L^{p})</span> quantile regression to a general classical nonlinear conditional autoregressive model and proposes a new model called the conditional <span>(L^{p})</span> quantile nonlinear autoregressive regression model (CAR-<span>(L^{p})</span>-quantile model for short). Limit theorems for regression estimators are proved in mild conditions, and algorithms are provided for obtaining parameter estimates and the optimal value of <i>p</i>. Simulation study of estimation’s quality is given. Then, a CLVaR method calculating VaR based on the CAR-<span>(L^{p})</span>-quantile model is elaborated. Finally, a real data analysis is conducted to illustrate virtues of our proposed methods.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 1","pages":"25 - 59"},"PeriodicalIF":0.8,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254402","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}
引用次数: 0
Simplified quasi-likelihood analysis for a locally asymptotically quadratic random field 局部渐近二次随机场的简化准概率分析
IF 0.8 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2024-09-14 DOI: 10.1007/s10463-024-00907-8
Nakahiro Yoshida
{"title":"Simplified quasi-likelihood analysis for a locally asymptotically quadratic random field","authors":"Nakahiro Yoshida","doi":"10.1007/s10463-024-00907-8","DOIUrl":"10.1007/s10463-024-00907-8","url":null,"abstract":"<div><p>The IHK program is a general framework in asymptotic decision theory, introduced by Ibragimov and Hasminskii and extended to semimartingales by Kutoyants. The quasi-likelihood analysis (QLA) asserts that a polynomial type large deviation inequality is always valid if the quasi-likelihood random field is asymptotically quadratic and if a key index reflecting the identifiability is non-degenerate. As a result, following the IHK program, the QLA gives a way to inference for various nonlinear stochastic processes. This paper provides a reformed and simplified version of the QLA and improves accessibility to the theory. As an example of the advantages of the scheme, the user can obtain asymptotic properties of the quasi-Bayesian estimator by only verifying non-degeneracy of the key index.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 1","pages":"1 - 24"},"PeriodicalIF":0.8,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254403","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}
引用次数: 0
Asymptotic expected sensitivity function and its applications to measures of monotone association 渐近预期灵敏度函数及其在单调关联测量中的应用
IF 0.8 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2024-08-17 DOI: 10.1007/s10463-024-00910-z
Qingyang Zhang
{"title":"Asymptotic expected sensitivity function and its applications to measures of monotone association","authors":"Qingyang Zhang","doi":"10.1007/s10463-024-00910-z","DOIUrl":"10.1007/s10463-024-00910-z","url":null,"abstract":"<div><p>We introduce a new type of influence function, the asymptotic expected sensitivity function, which is often equivalent to but mathematically more tractable than the traditional one based on the Gâteaux derivative. To illustrate, we study the robustness of some important measures of association, including Spearman’s rank correlation and Kendall’s concordance measure, and the recently developed Chatterjee’s correlation.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"76 5","pages":"877 - 896"},"PeriodicalIF":0.8,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199920","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}
引用次数: 0
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