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On global robustness of an adversarial risk analysis solution 论对抗性风险分析解决方案的全局稳健性
IF 1.5 3区 数学
Statistica Neerlandica Pub Date : 2024-09-09 DOI: 10.1111/stan.12361
Jinming Yang, Chaitanya Joshi, Fabrizio Ruggeri
{"title":"On global robustness of an adversarial risk analysis solution","authors":"Jinming Yang, Chaitanya Joshi, Fabrizio Ruggeri","doi":"10.1111/stan.12361","DOIUrl":"https://doi.org/10.1111/stan.12361","url":null,"abstract":"Adversarial Risk Analysis (ARA) can be a more realistic and practical alternative to traditional game theoretic or decision theoretic approaches for modeling strategic decision‐making in the presence of an adversary. ARA relies on quantifying the decision‐maker's (DM's) uncertainties about the adversary's strategic thinking, choices and utilities via probability distributions to identify the optimal solution for the DM. ARA solution will be sensitive to the choices of prior distributions used for modelling the expert beliefs. Yet, to date, no mathematical results to characterize the robustness of the ARA solution to the misspecification of one or more prior distributions exist. Prior elicitation is known to be challenging. We present the very first mathematical results on the global robustness of the ARA solution. We use the distorted band class of priors and establish the conditions under which an ordering on the ARA solution can be established when modelling the first‐price sealed‐bid auctions using the <jats:italic>nonstrategic play</jats:italic> and <jats:italic>level‐</jats:italic> thinking solution concepts. We illustrate these results using numerical examples and discuss further areas of research.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Heterogeneous dense subhypergraph detection 异构密集子超图检测
IF 1.5 3区 数学
Statistica Neerlandica Pub Date : 2024-09-03 DOI: 10.1111/stan.12360
Mingao Yuan, Zuofeng Shang
{"title":"Heterogeneous dense subhypergraph detection","authors":"Mingao Yuan, Zuofeng Shang","doi":"10.1111/stan.12360","DOIUrl":"https://doi.org/10.1111/stan.12360","url":null,"abstract":"We study the problem of testing the existence of a heterogeneous dense subhypergraph. The null hypothesis corresponds to a heterogeneous Erdös–Rényi uniform random hypergraph and the alternative hypothesis corresponds to a heterogeneous uniform random hypergraph that contains a dense subhypergraph. We establish detection boundaries when the edge probabilities are known and construct an asymptotically powerful test for distinguishing the hypotheses. We also construct an adaptive test which does not involve edge probabilities, and hence, is more practically useful.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
General adapted‐threshold monitoring in discrete environments and rules for imbalanced classes 离散环境中的一般自适应阈值监测和不平衡类规则
IF 1.5 3区 数学
Statistica Neerlandica Pub Date : 2024-08-22 DOI: 10.1111/stan.12352
Ansgar Steland, Ewaryst Rafajłowicz, Wojciech Rafajłowicz
{"title":"General adapted‐threshold monitoring in discrete environments and rules for imbalanced classes","authors":"Ansgar Steland, Ewaryst Rafajłowicz, Wojciech Rafajłowicz","doi":"10.1111/stan.12352","DOIUrl":"https://doi.org/10.1111/stan.12352","url":null,"abstract":"Having in mind applications in statistics and machine learning such as individualized care monitoring, or watermark detection in large language models, we consider the following general setting: When monitoring a sequence of observations, , there may be additional information, , on the environment which should be used to design the monitoring procedure. This additional information can be incorporated by applying threshold functions to the standardized measurements to adapt the detector to the environment. For the case of categorical data encoding of discrete‐valued environmental information we study several classes of level threshold functions including a proportional one which favors rare events among imbalanced classes. For the latter rule asymptotic theory is developed for independent and identically distributed and dependent learning samples including data from new discrete autoregressive moving average model (NDARMA) series and Hidden Markov Models. Further, we propose two‐stage designs which allow to distribute in a controlled way the budget over an a priori partition of the sample space of . The approach is illustrated by a real medical data set.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
VC‐PCR: A prediction method based on variable selection and clustering VC-PCR：基于变量选择和聚类的预测方法
IF 1.5 3区 数学
Statistica Neerlandica Pub Date : 2024-08-22 DOI: 10.1111/stan.12358
Rebecca Marion, Johannes Lederer, Bernadette Goevarts, Rainer von Sachs
{"title":"VC‐PCR: A prediction method based on variable selection and clustering","authors":"Rebecca Marion, Johannes Lederer, Bernadette Goevarts, Rainer von Sachs","doi":"10.1111/stan.12358","DOIUrl":"https://doi.org/10.1111/stan.12358","url":null,"abstract":"Sparse linear prediction methods suffer from decreased prediction accuracy when the predictor variables have cluster structure (e.g., highly correlated groups of variables). To improve prediction accuracy, various methods have been proposed to identify variable clusters from the data and integrate cluster information into a sparse modeling process. But none of these methods achieve satisfactory performance for prediction, variable selection and variable clustering performed simultaneously. This paper presents Variable Cluster Principal Component Regression (VC‐PCR), a prediction method that uses variable selection and variable clustering in order to solve this problem. Experiments with real and simulated data demonstrate that, compared to competitor methods, VC‐PCR is the only method that achieves simultaneously good prediction, variable selection, and clustering performance when cluster structure is present.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 density functionals via cross‐validation 通过交叉验证估计密度函数
IF 1.5 3区 数学
Statistica Neerlandica Pub Date : 2024-08-01 DOI: 10.1111/stan.12359
José E. Chacón, Carlos Tenreiro
{"title":"Estimation of density functionals via cross‐validation","authors":"José E. Chacón, Carlos Tenreiro","doi":"10.1111/stan.12359","DOIUrl":"https://doi.org/10.1111/stan.12359","url":null,"abstract":"In density estimation, the mean integrated squared error (MISE) is commonly used as a measure of performance. In that setting, the cross‐validation criterion provides an unbiased estimator of the MISE minus the integral of the squared density. Since the minimum MISE is known to converge to zero, this suggests that the minimum value of the cross‐validation criterion could be regarded as an estimator of minus the integrated squared density. This novel proposal presents the outstanding feature that, unlike all other existing estimators, it does not need the choice of any tuning parameter. Indeed, it is proved here that this approach results in a consistent and efficient estimator, with remarkable performance in practice. Moreover, apart from this base case, it is shown how several other problems on density functional estimation can be similarly handled using this new principle, thus demonstrating full potential for further applications.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141885829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Artificial neural network small‐sample‐bias‐corrections of the AR(1) parameter close to unit root 人工神经网络对接近单位根的 AR(1) 参数进行小样本偏置校正
IF 1.5 3区 数学
Statistica Neerlandica Pub Date : 2024-08-01 DOI: 10.1111/stan.12354
Haozhe Jiang, Ostap Okhrin, Michael Rockinger
{"title":"Artificial neural network small‐sample‐bias‐corrections of the AR(1) parameter close to unit root","authors":"Haozhe Jiang, Ostap Okhrin, Michael Rockinger","doi":"10.1111/stan.12354","DOIUrl":"https://doi.org/10.1111/stan.12354","url":null,"abstract":"This paper introduces an artificial neural network (ANN) approach to estimate the autoregressive process AR(1) when the autocorrelation parameter is near one. Traditional ordinary least squares (OLS) estimators suffer from biases in small samples, necessitating various correction methods proposed in the literature. The ANN, trained on simulated data, outperforms these methods due to its nonlinear structure. Unlike competitors requiring simulations for bias corrections based on specific sample sizes, the ANN directly incorporates sample size as input, eliminating the need for repeated simulations. Stability tests involve exploring different ANN architectures and activation functions and robustness to varying distributions of the process innovations. Empirical applications on financial and industrial data highlight significant differences among methods, with ANN estimates suggesting lower persistence than other approaches.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141885828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Inference for Kumaraswamy‐G family of distributions under unified progressive hybrid censoring with partially observed competing risks data 库马拉斯瓦米-G 系列分布在统一渐进混合删减法下的推断，以及部分观察到的竞争风险数据
IF 1.5 3区 数学
Statistica Neerlandica Pub Date : 2024-07-25 DOI: 10.1111/stan.12357
Subhankar Dutta, Hon Keung Tony Ng, Suchandan Kayal
{"title":"Inference for Kumaraswamy‐G family of distributions under unified progressive hybrid censoring with partially observed competing risks data","authors":"Subhankar Dutta, Hon Keung Tony Ng, Suchandan Kayal","doi":"10.1111/stan.12357","DOIUrl":"https://doi.org/10.1111/stan.12357","url":null,"abstract":"In this study, statistical inference for competing risks model with latent failure times following the Kumaraswamy‐G (Kw‐G) family of distributions under a unified progressive hybrid censoring (UPHC) scheme is developed. Maximum likelihood estimates (MLEs) of the unknown model parameters are obtained, and their existence and uniqueness properties are discussed. Using the asymptotic properties of MLEs, the approximate confidence intervals for the model parameters are constructed. Further, Bayes estimates with associated highest posterior density credible intervals for the model parameters are developed under squared error loss function with informative and noninformative priors. These estimates are obtained under both restricted and nonrestricted parameter spaces. Moreover, frequentist and Bayesian approaches are developed to test the equality of shape parameters of the two competing failure causes. The comparison of censoring schemes based on different criteria is also discussed. Monte Carlo simulation studies are used to evaluate the performance of the proposed statistical inference procedures. An electrical appliances data set is analyzed to illustrate the applicability of the proposed methodologies.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Computing optimal allocation of trials to subregions in crop‐variety testing in case of correlated genotype effects 在基因型效应相关的情况下，计算作物品种测试中试验在次区域的最佳分配方案
IF 1.5 3区 数学
Statistica Neerlandica Pub Date : 2024-07-25 DOI: 10.1111/stan.12353
Maryna Prus
{"title":"Computing optimal allocation of trials to subregions in crop‐variety testing in case of correlated genotype effects","authors":"Maryna Prus","doi":"10.1111/stan.12353","DOIUrl":"https://doi.org/10.1111/stan.12353","url":null,"abstract":"The subject of this work is the allocation of trials to subregions in crop variety testing in the case of correlated genotype effects. A solution for computation of optimal allocations using the OptimalDesign package in R is proposed. The obtained optimal designs minimize linear criteria based on the mean squared error matrix of the best linear unbiased prediction of the genotype effects. The proposed computational approach allows for any kind of additional linear constraint on the designs. The results are illustrated by a real data example.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Degree distributions in networks: Beyond the power law 网络中的学位分布：超越幂律
IF 1.5 3区 数学
Statistica Neerlandica Pub Date : 2024-07-23 DOI: 10.1111/stan.12355
Clement Lee, Emma F. Eastoe, Aiden Farrell
{"title":"Degree distributions in networks: Beyond the power law","authors":"Clement Lee, Emma F. Eastoe, Aiden Farrell","doi":"10.1111/stan.12355","DOIUrl":"https://doi.org/10.1111/stan.12355","url":null,"abstract":"The power law is useful in describing count phenomena such as network degrees and word frequencies. With a single parameter, it captures the main feature that the frequencies are linear on the log‐log scale. Nevertheless, there have been criticisms of the power law, for example, that a threshold needs to be preselected without its uncertainty quantified, that the power law is simply inadequate, and that subsequent hypothesis tests are required to determine whether the data could have come from the power law. We propose a modeling framework that combines two different generalizations of the power law, namely the generalized Pareto distribution and the Zipf‐polylog distribution, to resolve these issues. The proposed mixture distributions are shown to fit the data well and quantify the threshold uncertainty in a natural way. A model selection step embedded in the Bayesian inference algorithm further answers the question whether the power law is adequate.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
A note on convergence of calibration weights to inverse probability weights 关于校准权重向逆概率权重收敛的说明
IF 1.5 3区 数学
Statistica Neerlandica Pub Date : 2024-07-20 DOI: 10.1111/stan.12356
Tadayoshi Fushiki
{"title":"A note on convergence of calibration weights to inverse probability weights","authors":"Tadayoshi Fushiki","doi":"10.1111/stan.12356","DOIUrl":"https://doi.org/10.1111/stan.12356","url":null,"abstract":"Recently, nonresponse rates in sample surveys have been increasing. Nonresponse bias is a serious concern in the analysis of sample surveys. The calibration and propensity score methods are used to adjust nonresponse bias. The propensity score method uses the weights of the inverse probability of response. The inverse probability of response is estimated by the auxiliary variables observed in respondents and nonrespondents. The calibration method can use additional auxiliary variables observed only in respondents if the population distributions of the variables are known. The calibration method is widely used; however, the theoretical property in the nonresponse situation has not been investigated. This study provides a condition that the calibration weights asymptotically go to the inverse probability of response and clarifies the relationship between the calibration and propensity score methods.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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