Scandinavian Journal of Statistics最新文献

{"title":"Efficient\u0000 t\u0000 0\u0000 ‐year risk regression using the logistic model","authors":"T. Martinussen, T. Scheike","doi":"10.1111/sjos.12658","DOIUrl":"https://doi.org/10.1111/sjos.12658","url":null,"abstract":"In some clinical studies patient survival beyond a specific point in time, t0$$ {t}_0 $$ , say, may be of special interest as it may for instance indicate patient cure. To analyze the t0$$ {t}_0 $$ ‐year risk for such patients may be accomplished using logistic regression with appropriate weights (IPWCC) that may further be augmented (AIPWCC) to improve efficiency. In this paper, we derive the most efficient estimator for this problem, which is different from the AIPWCC based on the full data efficient influence function. We first give the result for a survival endpoint and then generalize to the competing risk setting. The proposed estimators superior behavior is illustrated using simulations as well as applying it to some real data concerning the survival of blood and marrow transplanted patients.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45028261","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":"Adaptive estimation of intensity in a doubly stochastic Poisson Process","authors":"Thomas Deschatre","doi":"10.1111/sjos.12651","DOIUrl":"https://doi.org/10.1111/sjos.12651","url":null,"abstract":"In this paper, I consider a doubly stochastic Poisson process with intensity λt=qXt$$ {lambda}_t=qleft({X}_tright) $$ where X$$ X $$ is a continuous Itô semi‐martingale. Both processes are observed continuously over a fixed period 0,1$$ left[0,1right] $$ . I propose a local polynomial estimator for the function q$$ q $$ on a given interval. Next, I propose a method to select the bandwidth in a nonasymptotic framework that leads to an oracle inequality. Considering the asymptotic n$$ n $$ , and q=nq˜$$ q=ntilde{q} $$ , the accuracy of the proposed estimator over the Hölder class of order β$$ beta $$ is n−β2β+1$$ {n}^{frac{-beta }{2beta +1}} $$ if the degree of the chosen polynomial is greater than ⌊β⌋$$ leftlfloor beta rightrfloor $$ and it is optimal in the minimax setting. I apply those results to data on French temperature and electricity spot prices from which I infer the intensity of electricity spot spikes as a function of the temperature.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47185773","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":"Regularized t distribution: definition, properties and applications","authors":"Zongliang Hu, Yiping Yang, Gaorong Li, T. Tong","doi":"10.1111/sjos.12655","DOIUrl":"https://doi.org/10.1111/sjos.12655","url":null,"abstract":"For gene expression data analysis, an important task is to identify genes that are differentially expressed between two or more groups. Nevertheless, as biological experiments are often measured with a relatively small number of samples, how to accurately estimate the variances of gene expression becomes a challenging issue. To tackle this problem, we introduce a regularized t$$ t $$ distribution and derive its statistical properties including the probability density function and the moment generating function. The noncentral regularized t$$ t $$ distribution is also introduced for computing the statistical power of hypothesis testing. For practical applications, we apply the regularized t$$ t $$ distribution to establish the null distribution of the regularized t$$ t $$ statistic, and then formulate it as a regularized t$$ t $$ ‐test for detecting the differentially expressed genes. Simulation studies and real data analysis show that our regularized t$$ t $$ ‐test performs much better than the Bayesian t$$ t $$ ‐test in the “limma” package, in particular when the sample sizes are small.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42701485","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":"Discussion of “Divergence vs. Decision P$$ P $$ ‐values: A Distinction Worth Making in Theory and Keeping in Practice – or, How Divergence P$$ P $$ ‐values Measure Evidence Even When Decision P$$ P $$ ‐values Do Not” by Sander Greenland","authors":"K. Rice","doi":"10.1111/sjos.12656","DOIUrl":"https://doi.org/10.1111/sjos.12656","url":null,"abstract":"I congratulate the author on a stimulating paper, that greatly clarifies important foundational issues. The paper’s utility should also go beyond mere academic interests: misuse of statistical tests and/or p -values is a major problem in practice, so clarity over what these methods provide (and what they do not) should help users connect the analyses they implement with the scientific questions they aim to answer.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"50 1","pages":"931 - 933"},"PeriodicalIF":1.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45017986","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":"Statistical Inference with semiparametric nonignorable nonresponse models","authors":"Masatoshi Uehara, Danhyang Lee, Jae Kwang Kim","doi":"10.1111/sjos.12652","DOIUrl":"https://doi.org/10.1111/sjos.12652","url":null,"abstract":"How to deal with nonignorable response is often a challenging problem encountered in statistical analysis with missing data. Parametric model assumption for the response mechanism is sensitive to model misspecification. We consider a semiparametric response model that relaxes the parametric model assumption in the response mechanism. Two types of efficient estimators, profile maximum likelihood estimator and profile calibration estimator, are proposed, and their asymptotic properties are investigated. Two extensive simulation studies are used to compare with some existing methods. We present an application of our method using data from the Korean Labor and Income Panel Survey.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46853750","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":"Comments on Divergence vs. Decision P‐values","authors":"Paul W. Vos","doi":"10.1111/sjos.12647","DOIUrl":"https://doi.org/10.1111/sjos.12647","url":null,"abstract":"The distinction between the two uses of p-values described by Professor Greenland is related to two distinct interpretations of frequentist probability—that is, probability used to describe a random event. I will illustrate with a simple example. In the North Carolina Pick-4 lottery, 10 ping pong balls labeled with distinct digits from I9 = {0, 1,..., 9} are mixed in a clear container and opening a door allows a single ball to be selected. Prior to opening the door, blown air mixes the balls making equally likely selection of each ball plausible. This is repeated with three identical containers to obtain the remaining three digits. If a winning ticket is defined as one where the sum of the four digits exceeds 28, the state can charge $5 for a ticket with a $100 prize and expect a profit. There are 330 of 104 possible outcomes where the sum exceeds 28 so the expected value is 0.033 × $100 = $3.30. This calculation requires no repeated sampling but it is natural for the state to interpret this value in the long run. For an individual ticket holder, all that is required is that each ball is given an equal chance to be selected for the drawing associated with his ticket. The ticket holder does not need to imagine a long sequence of draws just as a cancer patient does not need to consider a long sequence of 5-year periods to understand a 30% 5-year survival. Using terminology from Vos and Holbert (2022), the scope for the ticket holder is specific while that of the state is generic. The uniform distribution on 4-tuples I4 9 = I9 × I9 × I9 × I9 provides a model for repeated draws of the Pick-4 lottery, that is, of the data generation process. For most inference applications, the distribution of an unknown population can be modeled rather than the process that generated the data. We modify this example to consider inference. We are told the sum of a single lottery draw and we are to infer whether the draw came from the NC lottery or lottery A that also has four containers but each contains 8 balls with labels from I7 = {0, 1,..., 7}. The sum of the digits is 29 but no other information is given. A reduction-to-contradiction argument establishes that the result came from the NC lottery. Premise: lottery A produced our data; every possible sum from lottery A belongs to the set {0, 1,..., 28}; 29 is not in this set; conclusion: the contradiction means it is impossible that the premise is true.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"50 1","pages":"920 - 922"},"PeriodicalIF":1.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44877146","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":"Statistical evidence and surprise unified under possibility theory","authors":"D. Bickel","doi":"10.1111/sjos.12648","DOIUrl":"https://doi.org/10.1111/sjos.12648","url":null,"abstract":"Sander Greenland argues that reported results of hypothesis tests should include the surprisal, the base‐2 logarithm of the reciprocal of a p‐value. The surprisal measures how many bits of evidence in the data warrant rejecting the null hypothesis. A generalization of surprisal also can measure how much the evidence justifies rejecting a composite hypothesis such as the complement of a confidence interval. That extended surprisal, called surprise, quantifies how many bits of astonishment an agent believing a hypothesis would experience upon observing the data. While surprisal is a function of a point in hypothesis space, surprise is a function of a subset of hypothesis space. Satisfying the conditions of conditional min‐plus probability, surprise inherits a wealth of tools from possibility theory. The equivalent compatibility function has been recently applied to the replication crisis, to adjusting p‐values for prior information, and to comparing scientific theories.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"50 1","pages":"923 - 928"},"PeriodicalIF":1.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43372143","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":"Epistemic confidence in the observed confidence interval","authors":"Y. Pawitan, Hangbin Lee, Youngjo Lee","doi":"10.1111/sjos.12654","DOIUrl":"https://doi.org/10.1111/sjos.12654","url":null,"abstract":"We define confidence to be epistemic if it applies to an observed confidence interval. Epistemic confidence is unavailable—or even denied—in orthodox frequentist inference, as the confidence level is understood to apply to the procedure. Yet there are obvious practical and psychological needs to think about the uncertainty in the observed interval. We extend the Dutch Book argument used in the classical Bayesian justification of subjective probability to a stronger market‐based version, which prevents external agents from exploiting unused information in any relevant subset. We previously showed that confidence is an extended likelihood, and the likelihood principle states that the likelihood contains all the information in the data, hence leaving no relevant subset. Intuitively, this implies that confidence associated with the full likelihood is protected from the Dutch Book, and hence is epistemic. Our goal is to validate this intuitive notion through theoretical backing and practical illustrations.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47752304","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":"Discussion on the SJS invited paper by Sander Greenland Divergence vs. Decision P$$ P $$‐values: A Distinction worth making in theory and keeping in Practice","authors":"Dario Gasbarra","doi":"10.1111/sjos.12649","DOIUrl":"https://doi.org/10.1111/sjos.12649","url":null,"abstract":"It is shown in the cited paper written by Michael Lavine and in several others works that the p -value of the test-statistics is not a consistent measure of evidence in the context of testing alternative hypothesis. As Sander Greenland points out, these should not be confused with the p -values of realized goodness of-fit-test statistics. Goodness-of-fit tests are useful sanity checks in order to decide, (and there would be also a decision to be taken there !) whether our model and/or assumptions are compatible with the available data, or we need to take a step back and look for what could be wrong. Greenland argues that although using the p -value in a goodness of fit test violates the likelihood principle, these p -values do not suffer the inconsistencies of p -values arising from testing alternative hypothesis, and can be used as a measure of evidence against the model, (following Popper, scientific theories and models can be only falsified and never verified), on an absolute scale in the [ 0 , 1 ] range, where a zero left-sided p -value corresponds to perfect fit, and every p -value strictly greater than zero should be considered as negative evidence of some level. I find this peculiar, unless we would be dealing only with fully deterministic models. Fitting the data is not","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"50 1","pages":"929 - 930"},"PeriodicalIF":1.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43628758","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":"Comments on Divergence vs. Decision P‐values: A Distinction Worth Making in Theory and Keeping in Practice – or, How Divergence P‐values Measure Evidence Even When Decision P‐values Do Not by Greenland in Scandinavian Journal of Statistics, 2023","authors":"M. Lavine","doi":"10.1111/sjos.12646","DOIUrl":"https://doi.org/10.1111/sjos.12646","url":null,"abstract":"Greenland (2023) distinguishes between P‐values used for data description and P‐values used for declaring significance. That's a useful distinction and Greenland has advanced our field by making it. That distinction comes with the idea that describing data with statistical models is often a useful task for statisticians. Again, we agree. Along the way, Greenland also says (i) there is such a thing as a “measure …of evidence [from data] against a statistical hypothesis or model” without regard to alternatives; (ii) “a discrepancy P‐value is an ordinal description …”; (iii) descriptive “P‐values can be derived to provide coherent measures of refutational evidence”; and a few other things that deserve comment and discussion.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"50 1","pages":"915 - 919"},"PeriodicalIF":1.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42858391","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}