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Martingale posterior distributions for cumulative hazard functions Scand. J. Stat. (IF 1.0) Pub Date : 2024-04-07 Stephen G. Walker
This paper is about the modeling of cumulative hazard functions using martingale posterior distributions. The focus is on uncertainty quantification from a nonparametric perspective. The foundational Bayesian model in this case is the beta process and the classic estimator is the Nelson–Aalen. We use a sequence of estimators which form a martingale in order to obtain a random cumulative hazard function
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On a computable Skorokhod's integral‐based estimator of the drift parameter in fractional SDE Scand. J. Stat. (IF 1.0) Pub Date : 2024-03-23 Nicolas Marie
This paper deals with a Skorokhod's integral‐based least squares‐ (LS) type estimator of the drift parameter computed from multiple (possibly dependent) copies of the solution of a stochastic differential equation (SDE) driven by a fractional Brownian motion of Hurst index . On the one hand, some convergence results are established on our LS estimator when . On the other hand, when , Skorokhod's integral‐based
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Statistical inference for generative adversarial networks and other minimax problems Scand. J. Stat. (IF 1.0) Pub Date : 2024-03-21 Mika Meitz
This paper studies generative adversarial networks (GANs) from the perspective of statistical inference. A GAN is a popular machine learning method in which the parameters of two neural networks, a generator and a discriminator, are estimated to solve a particular minimax problem. This minimax problem typically has a multitude of solutions and the focus of this paper are the statistical properties
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Efficient drift parameter estimation for ergodic solutions of backward SDEs Scand. J. Stat. (IF 1.0) Pub Date : 2024-02-28 Teppei Ogihara, Mitja Stadje
We derive consistency and asymptotic normality results for quasi‐maximum likelihood methods for drift parameters of ergodic stochastic processes observed in discrete time in an underlying continuous‐time setting. The special feature of our analysis is that the stochastic integral part is unobserved and nonparametric. Additionally, the drift may depend on the (unknown and unobserved) stochastic integrand
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Asymptotic inference of the ARMA model with time-functional variance noises Scand. J. Stat. (IF 1.0) Pub Date : 2024-02-05 Bibi Cai, Enwen Zhu, Shiqing Ling
This paper studies the autoregressive and moving average (ARMA) model with time-functional variance (TFV) noises, called the ARMA-TFV model. We first establish the consistency and asymptotic normality of its least squares estimator (LSE). The Wald tests and portmanteau tests are constructed based on the theory for variable selection and model checking. A simulation study is carried out to assess the
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Estimation of treatment effect among treatment responders with a time-to-event endpoint Scand. J. Stat. (IF 1.0) Pub Date : 2024-01-18 Andreas Nordland, Torben Martinussen
In a placebo-controlled clinical study one may calculate the average treatment effect to convey the effect of the active treatment on some outcome. However, if it is speculated that the treatment only has an effect if the patient responds to the treatment defined by a certain biomarker response, then it is arguably more relevant to estimate the treatment effect among such responders. We present such
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Nonparametric plug-in classifier for multiclass classification of S.D.E. paths Scand. J. Stat. (IF 1.0) Pub Date : 2024-01-15 Christophe Denis, Charlotte Dion-Blanc, Eddy Ella-Mintsa, Viet Chi Tran
We study the multiclass classification problem where the features come from a mixture of time-homogeneous diffusion.Specifically, the classes are discriminated by their drift functions while the diffusion coefficient is common to all classes and unknown.In this framework, we build a plug-in classifier which relies on nonparamateric estimators of the drift and diffusion functions.We first establish
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The effect of the working correlation on fitting models to longitudinal data Scand. J. Stat. (IF 1.0) Pub Date : 2024-01-02 Samuel Muller, Suojin Wang, A. H. Welsh
We present a detailed discussion of the theoretical properties of quadratic inference function estimators of the parameters in marginal linear regression models. We consider the effect of the choice of working correlation on fundamental questions including the existence of quadratic inference function estimators, their relationship with generalized estimating equations estimators, and the robustness
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Log-density gradient covariance and automatic metric tensors for Riemann manifold Monte Carlo methods Scand. J. Stat. (IF 1.0) Pub Date : 2023-12-26 Tore Selland Kleppe
A metric tensor for Riemann manifold Monte Carlo particularly suited for nonlinear Bayesian hierarchical models is proposed. The metric tensor is built from symmetric positive semidefinite log-density gradient covariance (LGC) matrices, which are also proposed and further explored here. The LGCs generalize the Fisher information matrix by measuring the joint information content and dependence structure
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Characterization of valid auxiliary functions for representations of extreme value distributions and their max-domains of attraction Scand. J. Stat. (IF 1.0) Pub Date : 2023-12-26 Miriam Isabel Seifert
In this paper we study two important representations for extreme value distributions and their max-domains of attraction (MDA), namely von Mises representation (vMR) and variation representation (VR), which are convenient ways to gain limit results. Both VR and vMR are defined via so-called auxiliary functions ψ $$ \psi $$ . Up to now, however, the set of valid auxiliary functions for vMR has neither
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Consistent covariances estimation for stratum imbalances under minimization method for covariate-adaptive randomization Scand. J. Stat. (IF 1.0) Pub Date : 2023-12-26 Zixuan Zhao, Yanglei Song, Wenyu Jiang, Dongsheng Tu
Pocock and Simon's minimization method is a popular approach for covariate-adaptive randomization in clinical trials. Valid statistical inference with data collected under the minimization method requires the knowledge of the limiting covariance matrix of within-stratum imbalances, whose existence is only recently established. In this work, we propose a bootstrap-based estimator for this limit and
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Confidence bands for survival curves from outcome-dependent stratified samples Scand. J. Stat. (IF 1.0) Pub Date : 2023-12-21 Takumi Saegusa, Peter Nandori
We consider the construction of confidence bands for survival curves under the outcome-dependent stratified sampling. A main challenge of this design is that data are a biased dependent sample due to stratification and sampling without replacement. Most literature on regression approximates this design by Bernoulli sampling but variance is generally overestimated. Even with this approximation, the
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G-optimal grid designs for kriging models Scand. J. Stat. (IF 1.0) Pub Date : 2023-12-11 Subhadra Dasgupta, Siuli Mukhopadhyay, Jonathan Keith
This work is focused on finding G-optimal designs theoretically for kriging models with two-dimensional inputs and separable exponential covariance structures. For design comparison, the notion of evenness of two-dimensional grid designs is developed. The mathematical relationship between the design and the supremum of the mean squared prediction error (SMSPE) function is studied and then optimal designs
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Nonparametric conditional mean testing via an extreme-type statistic in high dimension Scand. J. Stat. (IF 1.0) Pub Date : 2023-12-02 Yiming Liu, Guangming Pan, Guangren Yang, Wang Zhou
We propose a new test to investigate the conditional mean dependence between a response variable and the corresponding covariates in the high-dimensional regimes. The test statistic is an extreme-type one built on the nonparametric method. The limiting null distribution of the proposed extreme type statistic under a mild mixing condition is established. Moreover, to make the test more powerful in general
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Modeling multivariate extreme value distributions via Markov trees Scand. J. Stat. (IF 1.0) Pub Date : 2023-11-30 Shuang Hu, Zuoxiang Peng, Johan Segers
Multivariate extreme value distributions are a common choice for modeling multivariate extremes. In high dimensions, however, the construction of flexible and parsimonious models is challenging. We propose to combine bivariate max-stable distributions into a Markov random field with respect to a tree. Although in general not max-stable itself, this Markov tree is attracted by a multivariate max-stable
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Accurate bias estimation with applications to focused model selection Scand. J. Stat. (IF 1.0) Pub Date : 2023-11-14 Ingrid Dæhlen, Nils Lid Hjort, Ingrid Hobæk Haff
We derive approximations to the bias and squared bias with errors of order o ( 1 / n ) $$ o\left(1/n\right) $$ where n $$ n $$ is the sample size. Our results hold for a large class of estimators, including quantiles, transformations of unbiased estimators, maximum likelihood estimators in (possibly) incorrectly specified models, and functions thereof. Furthermore, we use the approximations to derive
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A new paradigm for high-dimensional data: Distance-based semiparametric feature aggregation framework via between-subject attributes Scand. J. Stat. (IF 1.0) Pub Date : 2023-11-08 Jinyuan Liu, Xinlian Zhang, Tuo Lin, Ruohui Chen, Yuan Zhong, Tian Chen, Tsungchin Wu, Chenyu Liu, Anna Huang, Tanya T. Nguyen, Ellen E. Lee, Dilip V. Jeste, Xin M. Tu
This article proposes a distance-based framework incentivized by the paradigm shift toward feature aggregation for high-dimensional data, which does not rely on the sparse-feature assumption or the permutation-based inference. Focusing on distance-based outcomes that preserve information without truncating any features, a class of semiparametric regression has been developed, which encapsulates multiple
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Maximum likelihood estimator for skew Brownian motion: The convergence rate Scand. J. Stat. (IF 1.0) Pub Date : 2023-10-17 Antoine Lejay, Sara Mazzonetto
We give a thorough description of the asymptotic property of the maximum likelihood estimator (MLE) of the skewness parameter of a Skew Brownian Motion (SBM). Thanks to recent results on the Central Limit Theorem of the rate of convergence of estimators for the SBM, we prove a conjecture left open that the MLE has asymptotically a mixed normal distribution involving the local time with a rate of convergence
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Kernel mean embedding of probability measures and its applications to functional data analysis Scand. J. Stat. (IF 1.0) Pub Date : 2023-10-12 Saeed Hayati, Kenji Fukumizu, Afshin Parvardeh
This study intends to introduce kernel mean embedding of probability measures over infinite-dimensional separable Hilbert spaces induced by functional response statistical models. The embedded function represents the concentration of probability measures in small open neighborhoods, which identifies a pseudo-likelihood and fosters a rich framework for statistical inference. Utilizing Maximum Mean Discrepancy
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Envelopes for multivariate linear regression with linearly constrained coefficients Scand. J. Stat. (IF 1.0) Pub Date : 2023-10-12 R. Dennis Cook, Liliana Forzani, Lan Liu
A constrained multivariate linear model is a multivariate linear model with the columns of its coefficient matrix constrained to lie in a known subspace. This class of models includes those typically used to study growth curves and longitudinal data. Envelope methods have been proposed to improve the estimation efficiency in unconstrained multivariate linear models, but have not yet been developed
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Estimation of the adjusted standard-deviatile for extreme risks Scand. J. Stat. (IF 1.0) Pub Date : 2023-10-12 Haoyu Chen, Tiantian Mao, Fan Yang
In this paper, we modify the Bayes risk for the expectile, the so-called variantile risk measure, to better capture extreme risks. The modified risk measure is called the adjusted standard-deviatile. First, we derive the asymptotic expansions of the adjusted standard-deviatile. Next, based on the first-order asymptotic expansion, we propose two efficient estimation methods for the adjusted standard-deviatile
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Nearly unstable integer-valued ARCH process and unit root testing Scand. J. Stat. (IF 1.0) Pub Date : 2023-10-05 Wagner Barreto-Souza, Ngai Hang Chan
This paper introduces a Nearly Unstable INteger-valued AutoRegressive Conditional Heteroscedastic (NU-INARCH) process for dealing with count time series data. It is proved that a proper normalization of the NU-INARCH process weakly converges to a Cox–Ingersoll–Ross diffusion in the Skorohod topology. The asymptotic distribution of the conditional least squares estimator of the correlation parameter
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Covariance-based soft clustering of functional data based on the Wasserstein–Procrustes metric Scand. J. Stat. (IF 1.0) Pub Date : 2023-10-05 Valentina Masarotto, Guido Masarotto
We consider the problem of clustering functional data according to their covariance structure. We contribute a soft clustering methodology based on the Wasserstein–Procrustes distance, where the in-between cluster variability is penalized by a term proportional to the entropy of the partition matrix. In this way, each covariance operator can be partially classified into more than one group. Such soft
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Greenland, S. (2023). Divergence vs. decision P-values: A distinction worth making in theory and keeping in practice. Scandinavian Journal of Statistics, 50, 1–35, https://onlinelibrary.wiley.com/doi/10.1111/sjos.12625 Scand. J. Stat. (IF 1.0) Pub Date : 2023-10-03
As of September 12, 2023 the following errors in the print version have been found: p. 70, first line Section 4.4, “Continuing the example, the Hodges and Lehmann (1954) UMPU decision P-value pHL for H…” should be “Continuing the example, for μ^$$ \hat{\mu} $$ exterior to the open interval (mL, mU) the Hodges and Lehmann (1954) UMPU decision P-value pHL for H…” because the formula does not give pHL
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Truncated two-parameter Poisson–Dirichlet approximation for Pitman–Yor process hierarchical models Scand. J. Stat. (IF 1.0) Pub Date : 2023-09-19 Junyi Zhang, Angelos Dassios
In this paper, we construct an approximation to the Pitman–Yor process by truncating its two-parameter Poisson–Dirichlet representation. The truncation is based on a decreasing sequence of random weights, thus having a lower approximation error compared to the popular truncated stick-breaking process. We develop an exact simulation algorithm to sample from the approximation process and provide an alternative
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Empirical and instance-dependent estimation of Markov chain and mixing time Scand. J. Stat. (IF 1.0) Pub Date : 2023-09-19 Geoffrey Wolfer
We address the problem of estimating the mixing time of a Markov chain from a single trajectory of observations. Unlike most previous works which employed Hilbert space methods to estimate spectral gaps, we opt for an approach based on contraction with respect to total variation. Specifically, we estimate the contraction coefficient introduced in Wolfer (2020), inspired from Dobrushin's. This quantity
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Sparse additive models in high dimensions with wavelets Scand. J. Stat. (IF 1.0) Pub Date : 2023-08-24 Sylvain Sardy, Xiaoyu Ma
In multiple regression, when covariates are numerous, it is often reasonable to assume that only a small number of them has predictive information. In some medical applications for instance, it is believed that only a few genes out of thousands are responsible for cancer. In that case, the aim is not only to propose a good fit, but also to select the relevant covariates (genes). We propose to perform
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Marginal additive models for population-averaged inference in longitudinal and cluster-correlated data Scand. J. Stat. (IF 1.0) Pub Date : 2023-08-10 Glen McGee, Alex Stringer
We propose a novel marginal additive model (MAM) for modeling cluster-correlated data with nonlinear population-averaged associations. The proposed MAM is a unified framework for estimation and uncertainty quantification of a marginal mean model, combined with inference for between-cluster variability and cluster-specific prediction. We propose a fitting algorithm that enables efficient computation
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Some mechanisms leading to underdispersion: Old and new proposals Scand. J. Stat. (IF 1.0) Pub Date : 2023-08-07 Pedro Puig, Jordi Valero, Amanda Fernández-Fontelo
In statistical modeling, it is important to know the mechanisms that cause underdispersion. Several mechanisms that lead to underdispersed count distributions are revisited from new perspectives, and new ones are introduced. These include procedures based on the number of arrivals in arrival processes, such as renewal and pure birth processes and steady-state distributions of birth-death processes
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Testing the missing at random assumption in generalized linear models in the presence of instrumental variables Scand. J. Stat. (IF 1.0) Pub Date : 2023-08-07 Rui Duan, C. Jason Liang, Pamela A. Shaw, Cheng Yong Tang, Yong Chen
Practical problems with missing data are common, and many methods have been developed concerning the validity and/or efficiency of statistical procedures. On a central focus, there have been longstanding interests on the mechanism governing data missingness, and correctly deciding the appropriate mechanism is crucially relevant for conducting proper practical investigations. In this paper, we present
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Estimating absorption time distributions of general Markov jump processes Scand. J. Stat. (IF 1.0) Pub Date : 2023-08-07 Jamaal Ahmad, Martin Bladt, Mogens Bladt
The estimation of absorption time distributions of Markov jump processes is an important task in various branches of statistics and applied probability. While the time-homogeneous case is classic, the time-inhomogeneous case has recently received increased attention due to its added flexibility and advances in computational power. However, commuting sub-intensity matrices are assumed, which in various
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Design for order-of-addition experiments with two-level components Scand. J. Stat. (IF 1.0) Pub Date : 2023-08-07 Hengzhen Huang
The statistical design for order-of-addition (OofA) experiments has received much recent interest as its potential in determining the optimal sequence of multiple components, for example, the optimal sequence of drug administration for disease treatment. The traditional OofA experiments focus mainly on the sequence effects of components, that is, the experimenters fix the factor level of each component
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Locally adaptive Bayesian isotonic regression using half shrinkage priors Scand. J. Stat. (IF 1.0) Pub Date : 2023-08-07 Ryo Okano, Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa
Isotonic regression or monotone function estimation is a problem of estimating function values under monotonicity constraints, which appears naturally in many scientific fields. This paper proposes a new Bayesian method with global–local shrinkage priors for estimating monotone function values. Specifically, we introduce half shrinkage priors for positive valued random variables and assign them for
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On the perimeter estimation of pixelated excursion sets of two-dimensional anisotropic random fields Scand. J. Stat. (IF 1.0) Pub Date : 2023-08-07 Ryan Cotsakis, Elena Di Bernardino, Thomas Opitz
We are interested in creating statistical methods to provide informative summaries of random fields through the geometry of their excursion sets. To this end, we introduce an estimator for the length of the perimeter of excursion sets of random fields on ℝ2$$ {\mathbb{R}}^2 $$ observed over regular square tilings. The proposed estimator acts on the empirically accessible binary digital images of the
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Communication-efficient low-dimensional parameter estimation and inference for high-dimensional Lp-quantile regression Scand. J. Stat. (IF 1.0) Pub Date : 2023-08-07 Junzhuo Gao, Lei Wang
The Lp$$ {L}^p $$-quantile regression generalizes both quantile regression and expectile regression, and has become popular for its robustness and effectiveness especially when 1
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Density estimation and regression analysis on hyperspheres in the presence of measurement error Scand. J. Stat. (IF 1.0) Pub Date : 2023-08-04 Jeong Min Jeon, Ingrid Van Keilegom
This paper studies density estimation and regression analysis with data observed on a general unit hypersphere and contaminated by measurement errors. We establish novel density and regression estimators, and study their asymptotic properties such as the rates of convergence and asymptotic normality. We also provide two types of asymptotic confidence intervals for both density and regression functions
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Structure recovery for partially observed discrete Markov random fields on graphs under not necessarily positive distributions Scand. J. Stat. (IF 1.0) Pub Date : 2023-08-02 Florencia Leonardi, Rodrigo Carvalho, Iara Frondana
We propose a penalized conditional likelihood criterion to estimate the basic neighborhood of each node in a discrete Markov random field that can be partially observed. We prove the convergence of the estimator in the case of a finite or countable infinite set of nodes. The estimated neighborhoods can be combined to estimate the underlying graph. In the finite case, the graph can be recovered with
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Partial correlation graphical LASSO Scand. J. Stat. (IF 1.0) Pub Date : 2023-08-01 Jack Storror Carter, David Rossell, Jim Q. Smith
Standard likelihood penalties to learn Gaussian graphical models are based on regularizing the off-diagonal entries of the precision matrix. Such methods, and their Bayesian counterparts, are not invariant to scalar multiplication of the variables, unless one standardizes the observed data to unit sample variances. We show that such standardization can have a strong effect on inference and introduce
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Flexible specification testing in quantile regression models Scand. J. Stat. (IF 1.0) Pub Date : 2023-08-01 Tim Kutzker, Nadja Klein, Dominik Wied
We propose three novel consistent specification tests for quantile regression models which generalize former tests in three ways. First, we allow the covariate effects to be quantile-dependent and nonlinear. Second, we allow parameterizing the conditional quantile functions by appropriate basis functions, rather than parametrically. We are thereby able to test for general functional forms, while retaining
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Locally correct confidence intervals for a binomial proportion: A new criteria for an interval estimator Scand. J. Stat. (IF 1.0) Pub Date : 2023-08-01 Paul H. Garthwaite, Maha W. Moustafa, Fadlalla G. Elfadaly
Well-recommended methods of forming “confidence intervals” for a binomial proportion give interval estimates that do not actually meet the definition of a confidence interval, in that their coverages are sometimes lower than the nominal confidence level. The methods are favoured because their intervals have a shorter average length than the Clopper–Pearson (gold-standard) method, whose intervals really
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A nested semiparametric method for case-control study with missingness Scand. J. Stat. (IF 1.0) Pub Date : 2023-08-01 Ge Zhao, Yanyuan Ma, Jill Schnall Hasler, Scott Damrauer, Michael Levin, Jinbo Chen
We propose a nested semiparametric model to analyze a case-control study where genuine case status is missing for some individuals. The concept of a noncase is introduced to allow for the imputation of the missing genuine cases. The odds ratio parameter of the genuine cases compared to controls is of interest. The imputation procedure predicts the probability of being a genuine case compared to a noncase
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Extrapolation estimation for nonparametric regression with measurement error Scand. J. Stat. (IF 1.0) Pub Date : 2023-06-01 Weixing Song, Kanwal Ayub, Jianhong Shi
For the nonparametric regression models with covariates contaminated with the normal measurement errors, this paper proposes an extrapolation algorithm to estimate the regression functions. By applying the conditional expectation directly to the kernel-weighted least squares of the deviations between the local linear approximation and the observed responses, the proposed algorithm successfully bypasses
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Parameter estimation for linear parabolic SPDEs in two space dimensions based on high frequency data Scand. J. Stat. (IF 1.0) Pub Date : 2023-05-18 Yozo Tonaki, Yusuke Kaino, Masayuki Uchida
We consider parameter estimation for a linear parabolic second-order stochastic partial differential equation (SPDE) in two space dimensions driven by two types of Q $$ Q $$ -Wiener processes based on high frequency data in time and space. We first estimate the parameters which appear in the eigenfunctions of the differential operator of the SPDE using the minimum contrast estimator based on the thinned
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Sparse principal component analysis for high-dimensional stationary time series Scand. J. Stat. (IF 1.0) Pub Date : 2023-05-17 Kou Fujimori, Yuichi Goto, Yan Liu, Masanobu Taniguchi
We consider the sparse principal component analysis for high-dimensional stationary processes. The standard principal component analysis performs poorly when the dimension of the process is large. We establish oracle inequalities for penalized principal component estimators for the large class of processes including heavy-tailed time series. The rate of convergence of the estimators is established
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Outlier detection based on extreme value theory and applications Scand. J. Stat. (IF 1.0) Pub Date : 2023-05-17 Shrijita Bhattacharya, Francois Kamper, Jan Beirlant
Whether an extreme observation is an outlier or not depends strongly on the corresponding tail behavior of the underlying distribution. We develop an automatic, data-driven method rooted in the mathematical theory of extremes to identify observations that deviate from the intermediate and central characteristics. The proposed algorithm is an extension of a method previously proposed in the literature
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Deep neural network classifier for multidimensional functional data Scand. J. Stat. (IF 1.0) Pub Date : 2023-05-12 Shuoyang Wang, Guanqun Cao, Zuofeng Shang
We propose a new approach, called as functional deep neural network (FDNN), for classifying multidimensional functional data. Specifically, a deep neural network is trained based on the principal components of the training data which shall be used to predict the class label of a future data function. Unlike the popular functional discriminant analysis approaches which only work for one-dimensional
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Pitfalls of amateur regression: The Dutch New Herring controversies Scand. J. Stat. (IF 1.0) Pub Date : 2023-05-08 Fengnan Gao, Richard D. Gill
Applying simple linear regression models, an economist analyzed a published dataset from an influential annual ranking in 2016 and 2017 of consumer outlets for Dutch New Herring and concluded that the ranking was manipulated. His finding was promoted by his university in national and international media, and this led to public outrage and ensuing discontinuation of the survey. We reconstitute the dataset
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Nonparametric adaptive estimation for interacting particle systems Scand. J. Stat. (IF 1.0) Pub Date : 2023-05-08 Fabienne Comte, Valentine Genon-Catalot
We consider a stochastic system of N $$ N $$ interacting particles with constant diffusion coefficient and drift linear in space, time-depending on two unknown deterministic functions. Our concern here is the nonparametric estimation of these functions from a continuous observation of the process on [ 0 , T ] $$ \left[0,T\right] $$ for fixed T $$ T $$ and large N $$ N $$ . We define two collections
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A robust model averaging approach for partially linear models with responses missing at random Scand. J. Stat. (IF 1.0) Pub Date : 2023-05-08 Zhongqi Liang, Qihua Wang
In this paper, with an assumed parametric model for the selection probability function, a robust model averaging estimation method is proposed for partially linear models with responses missing at random. The method is based on a weighted Mallows-type criterion. The method is robust in the sense that the asymptotic optimality holds true as long as the true model of the selection probability function
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Errata for “A framework for covariate balance using Bregman distances” Scand. J. Stat. (IF 1.0) Pub Date : 2023-05-01
The original version of this article unfortunately contained errors, which have been corrected with this erratum. In the paper, the semiparametric efficiency bound in Theorem 2 should be ∑ semi = E V [ Y ( 1 ) | X ] π ( X ) + V [ Y ( 0 ) | X ] 1 − π ( X ) + μ 1 ( X ) − μ 0 ( X ) − τ ATE 2 . . $$ {\Sigma}_{\mathrm{semi}}=\mathbbm{E}\left\{\frac{\mathbbm{V}\left[Y(1)|\boldsymbol{X}\right]}{\pi \left
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Efficient t0$$ {t}_0 $$-year risk regression using the logistic model Scand. J. Stat. (IF 1.0) Pub Date : 2023-04-26 Torben Martinussen, Thomas Harder Scheike
In some clinical studies patient survival beyond a specific point in time, t 0 $$ {t}_0 $$ , say, may be of special interest as it may for instance indicate patient cure. To analyze the t 0 $$ {t}_0 $$ -year risk for such patients may be accomplished using logistic regression with appropriate weights (IPWCC) that may further be augmented (AIPWCC) to improve efficiency. In this paper, we derive the
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Adaptive estimation of intensity in a doubly stochastic Poisson process Scand. J. Stat. (IF 1.0) Pub Date : 2023-04-18 Thomas Deschatre
In this paper, I consider a doubly stochastic Poisson process with intensity λ t = q X t $$ {\lambda}_t=q\left({X}_t\right) $$ where X $$ X $$ is a continuous Itô semi-martingale. Both processes are observed continuously over a fixed period 0 , 1 $$ \left[0,1\right] $$ . I propose a local polynomial estimator for the function q $$ q $$ on a given interval. Next, I propose a method to select the bandwidth
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Dimension-independent Markov chain Monte Carlo on the sphere Scand. J. Stat. (IF 1.0) Pub Date : 2023-04-14 Han Cheng Lie, Daniel Rudolf, Björn Sprungk, T. J. Sullivan
We consider Bayesian analysis on high-dimensional spheres with angular central Gaussian priors. These priors model antipodally symmetric directional data, are easily defined in Hilbert spaces and occur, for instance, in Bayesian density estimation and binary level set inversion. In this paper we derive efficient Markov chain Monte Carlo methods for approximate sampling of posteriors with respect to
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Regularized t$$ t $$ distribution: definition, properties, and applications Scand. J. Stat. (IF 1.0) Pub Date : 2023-04-14 Zongliang Hu, Yiping Yang, Gaorong Li, Tiejun Tong
For gene expression data analysis, an important task is to identify genes that are differentially expressed between two or more groups. Nevertheless, as biological experiments are often measured with a relatively small number of samples, how to accurately estimate the variances of gene expression becomes a challenging issue. To tackle this problem, we introduce a regularized t $$ t $$ distribution
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Statistical inference with semiparametric nonignorable nonresponse models Scand. J. Stat. (IF 1.0) Pub Date : 2023-04-14 Masatoshi Uehara, Danhyang Lee, Jae-Kwang Kim
How to deal with nonignorable response is often a challenging problem encountered in statistical analysis with missing data. Parametric model assumption for the response mechanism is sensitive to model misspecification. We consider a semiparametric response model that relaxes the parametric model assumption in the response mechanism. Two types of efficient estimators, profile maximum likelihood estimator
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Comments on Divergence vs. Decision P-values Scand. J. Stat. (IF 1.0) Pub Date : 2023-04-12 Paul W. Vos
The distinction between the two uses of p-values described by Professor Greenland is related to two distinct interpretations of frequentist probability—that is, probability used to describe a random event. I will illustrate with a simple example. In the North Carolina Pick-4 lottery, 10 ping pong balls labeled with distinct digits from I 9 = 0 , 1 , … , 9 $$ {I}_9=\left\{0,1,\dots, 9\right\} $$ are
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Statistical evidence and surprise unified under possibility theory Scand. J. Stat. (IF 1.0) Pub Date : 2023-04-12 David R. Bickel
Sander Greenland argues that reported results of hypothesis tests should include the surprisal, the base-2 logarithm of the reciprocal of a p-value. The surprisal measures how many bits of evidence in the data warrant rejecting the null hypothesis. A generalization of surprisal also can measure how much the evidence justifies rejecting a composite hypothesis such as the complement of a confidence interval
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Epistemic confidence in the observed confidence interval Scand. J. Stat. (IF 1.0) Pub Date : 2023-04-12 Yudi Pawitan, Hangbin Lee, Youngjo Lee
We define confidence to be epistemic if it applies to an observed confidence interval. Epistemic confidence is unavailable—or even denied—in orthodox frequentist inference, as the confidence level is understood to apply to the procedure. Yet there are obvious practical and psychological needs to think about the uncertainty in the observed interval. We extend the Dutch Book argument used in the classical
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Time-varying β-model for dynamic directed networks Scand. J. Stat. (IF 1.0) Pub Date : 2023-04-12 Yuqing Du, Lianqiang Qu, Ting Yan, Yuan Zhang
We extend the well-known β $$ \beta $$ -model for directed graphs to dynamic network setting, where we observe snapshots of adjacency matrices at different time points. We propose a kernel-smoothed likelihood approach for estimating 2 n $$ 2n $$ time-varying parameters in a network with n $$ n $$ nodes, from N $$ N $$ snapshots. We establish consistency and asymptotic normality properties of our kernel-smoothed
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Discussion on the SJS invited paper by Sander Greenland Divergence vs. Decision P$$ P $$-values: A Distinction worth making in theory and keeping in Practice Scand. J. Stat. (IF 1.0) Pub Date : 2023-04-11 Dario Gasbarra
It is shown in the cited paper written by Michael Lavine and in several others works that the p $$ p $$ -value of the test-statistics is not a consistent measure of evidence in the context of testing alternative hypothesis. As Sander Greenland points out, these should not be confused with the p $$ p $$ -values of realized goodness of-fit-test statistics. Goodness-of-fit tests are useful sanity checks