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Global jump filters and quasilikelihood analysis for volatility Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20210116
Haruhiko Inatsugu, Nakahiro YoshidaWe propose a new estimation scheme for estimation of the volatility parameters of a semimartingale with jumps based on a jump detection filter. Our filter uses all of the data to analyze the relative size of increments and to discriminate jumps more precisely. We construct quasimaximum likelihood estimators and quasiBayesian estimators and show limit theorems for them including \(L^p\)estimates

A permutation test for the twosample rightcensored model Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20210112
Grzegorz WyłupekThe paper presents a novel approach to solve a classical twosample problem with rightcensored data. As a result, an efficient procedure for verifying equality of the two survival curves is developed. It generalizes, in a natural manner, a wellknown standard, that is, the logrank test. Under the null hypothesis, the new test statistic has an asymptotic Chisquare distribution with one degree of

Efficient likelihoodbased inference for the generalized Pareto distribution Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20210111
Hideki Nagatsuka, N. BalakrishnanIt is well known that inference for the generalized Pareto distribution (GPD) is a difficult problem since the GPD violates the classical regularity conditions in the maximum likelihood method. For parameter estimation, most existing methods perform satisfactorily only in the limited range of parameters. Furthermore, the interval estimation and hypothesis tests have not been studied well in the literature

Identifying shifts between two regression curves Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20210106
Holger Dette, Subhra Sankar Dhar, Weichi WuThis article studies the problem whether two convex (concave) regression functions modelling the relation between a response and covariate in two samples differ by a shift in the horizontal and/or vertical axis. We consider a nonparametric situation assuming only smoothness of the regression functions. A graphical tool based on the derivatives of the regression functions and their inverses is proposed

Improper versus finitely additive distributions as limits of countably additive probabilities Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20210103
Pierre Druilhet, Erwan Saint Loubert BiéThe Bayesian paradigm with proper priors can be extended either to improper distributions or to finitely additive probabilities (FAPs). Improper distributions and diffuse FAPs can be seen as limits of proper distribution sequences for specific convergence modes. In this paper, we compare these two kinds of limits. We show that improper distributions and FAPs represent two distinct features of the limit

Robust test for structural instability in dynamic factor models Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20210102
Byungsoo Kim, Junmo Song, Changryong BaekIn this paper, we consider a robust test for structural breaks in dynamic factor models. The proposed framework considers structural changes when the underlying highdimensional time series is contaminated by outlying observations, which are often observed in many real applications such as fMRI, economics and finance. We propose a test based on the robust estimation of a vector autoregressive model

A universal approach to estimate the conditional variance in semimartingale limit theorems Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20210101
Mathias VetterThe typical central limit theorems in highfrequency asymptotics for semimartingales are results on stable convergence to a mixed normal limit with an unknown conditional variance. Estimating this conditional variance usually is a hard task, in particular when the underlying process contains jumps. For this reason, several authors have recently discussed methods to automatically estimate the conditional

Asymptotic behavior of mean density estimators based on a single observation: the Boolean model case Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20210101
Federico Camerlenghi, Claudio Macci, Elena VillaThe mean density estimation of a random closed set in \(\mathbb {R}^d\), based on a single observation, is a crucial problem in several application areas. In the case of stationary random sets, a common practice to estimate the mean density is to take the ndimensional volume fraction with observation window as large as possible. In the present paper, we provide large and moderate deviation results

Fast estimation of multivariate spatiotemporal Hawkes processes and network reconstruction Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20210101
Baichuan Yuan, Frederic P. Schoenberg, Andrea L. BertozziWe present a fast, accurate estimation method for multivariate Hawkes selfexciting point processes widely used in seismology, criminology, finance and other areas. There are two major ingredients. The first is an analytic derivation of exact maximum likelihood estimates of the nonparametric triggering density. We develop this for the multivariate case and add regularization to improve stability and

Generalized inverseGaussian frailty models with application to TARGET neuroblastoma data Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20201124
Luiza S. C. Piancastelli, Wagner BarretoSouza, Vinícius D. MayrinkA new class of survival frailty models based on the generalized inverseGaussian (GIG) distributions is proposed. We show that the GIG frailty models are flexible and mathematically convenient like the popular gamma frailty model. A piecewiseexponential baseline hazard function is employed, yielding flexibility for the proposed class. Although a closedform observed loglikelihood function is available

Mellin–Meijer kernel density estimation on $${{\mathbb {R}}}^+$$ R + Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20201124
Gery GeenensKernel density estimation is a nonparametric procedure making use of the smoothing power of the convolution operation. Yet, it performs poorly when the density of a positive variable is estimated, due to boundary issues. So, various extensions of the kernel estimator allegedly suitable for \({\mathbb {R}}^+\)supported densities, such as those using asymmetric kernels, abound in the literature. Those

Asymptotic theory of dependent Bayesian multiple testing procedures under possible model misspecification Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20201113
Noirrit Kiran Chandra, Sourabh BhattacharyaWe study asymptotic properties of Bayesian multiple testing procedures and provide sufficient conditions for strong consistency under general dependence structure. We also consider a novel Bayesian multiple testing procedure and associated error measures that coherently accounts for the dependence structure present in the model. We advocate posterior versions of FDR and FNR as appropriate error rates

Regularized bridgetype estimation with multiple penalties Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20201109
Alessandro De Gregorio, Francesco IafrateThe aim of this paper is to introduce an adaptive penalized estimator for identifying the true reduced parametric model under the sparsity assumption. In particular, we deal with the framework where the unpenalized estimator of the structural parameters needs simultaneously multiple rates of convergence (i.e., the socalled mixedrates asymptotic behavior). We introduce a bridgetype estimator by taking

Gaussian graphical models with toric vanishing ideals Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20201102
Pratik Misra, Seth SullivantGaussian graphical models are semialgebraic subsets of the cone of positive definite covariance matrices. They are widely used throughout natural sciences, computational biology and many other fields. Computing the vanishing ideal of the model gives us an implicit description of the model. In this paper, we resolve two conjectures given by Sturmfels and Uhler. In particular, we characterize those

Highdimensional signconstrained feature selection and grouping Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20201012
Shanshan Qin, Hao Ding, Yuehua Wu, Feng LiuIn this paper, we propose a nonnegative feature selection/feature grouping (nnFSG) method for general signconstrained highdimensional regression problems that allows regression coefficients to be disjointly homogeneous, with sparsity as a special case. To solve the resulting nonconvex optimization problem, we provide an algorithm that incorporates the difference of convex programming, augmented

Correction to: Clustering of subsample means based on pairwise L1 regularized empirical likelihood Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20201007
Quynh Van Nong, Chi Tim NgIn the original article, the affiliations were published incorrectly.

Robust highdimensional regression for data with anomalous responses Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20200930
Mingyang Ren, Sanguo Zhang, Qingzhao ZhangThe accuracy of response variables is crucially important to train regression models. In some situations, including the highdimensional case, response observations tend to be inaccurate, which would lead to biased estimators by directly fitting a conventional model. For analyzing data with anomalous responses in the highdimensional case, in this work, we adopt γdivergence to conduct variable selection

Estimation for highfrequency data under parametric market microstructure noise Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20200916
Simon Clinet, Yoann PotironWe develop a general class of noiserobust estimators based on the existing estimators in the nonnoisy highfrequency data literature. The microstructure noise is a parametric function of the limit order book. The noiserobust estimators are constructed as plugin versions of their counterparts, where we replace the efficient price, which is nonobservable, by an estimator based on the raw price and

On localization of source by hidden Gaussian processes with small noise Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20200909
Yury A. KutoyantsWe consider the problem of identification of the position of some source by observations of K detectors receiving signals from this source. The time of arriving of the signal to the kth detector depends of the distance between this detector and the source. The signals are observed in the presence of small Gaussian noise. The properties of the MLE and Bayesian estimators are studied in the asymptotic

Improved empirical likelihood inference and variable selection for generalized linear models with longitudinal nonignorable dropouts Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20200827
Lei Wang, Wei MaIn this paper, we propose improved statistical inference and variable selection methods for generalized linear models based on empirical likelihood approach that accommodates both the withinsubject correlations and nonignorable dropouts. We first apply the generalized method of moments to estimate the parameters in the nonignorable dropout propensity based on an instrument. The inverse probability

Hypothesis tests for highdimensional covariance structures Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20200801
Aki Ishii, Kazuyoshi Yata, Makoto AoshimaWe consider hypothesis testing for highdimensional covariance structures in which the covariance matrix is a (i) scaled identity matrix, (ii) diagonal matrix, or (iii) intraclass covariance matrix. Our purpose is to systematically establish a nonparametric approach for testing the highdimensional covariance structures (i)–(iii). We produce a new common test statistic for each covariance structure

Model averaging for linear models with responses missing at random Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20200701
Yuting Wei, Qihua Wang, Wei LiuIn this paper, a model averaging approach is developed for the linear regression models with response missing at random. It is shown that the proposed method is asymptotically optimal in the sense of achieving the lowest possible squared error. A Monte Carlo study is conducted to investigate the finite sample performance of our proposal by comparing with some related methods, and the simulation results

Instrument search in pseudolikelihood approach for nonignorable nonresponse Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20200613
Ji Chen, Jun Shao, Fang FangWith nonignorable nonresponse, an effective method to construct valid estimators of population parameters is to use a covariate vector called instrument that can be excluded from the nonresponse propensity, but are associated with the response even when other covariates are conditioned. The existing work in this approach assumes such an instrument is given, which is frequently not the case in applications

Model identification and selection for singleindex varyingcoefficient models Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20200610
Peng Lai, Fangjian Wang, Tingyu Zhu, Qingzhao ZhangSingleindex varyingcoefficient models include many types of popular semiparametric models, i.e., singleindex models, partially linear models, varying coefficient models, and so on. In this paper, a twostage efficient variable selection procedure is proposed to select important nonparametric and parametric components and obtain estimators simultaneously. We also find that the proposed procedure

Semiparametric methods for lefttruncated and rightcensored survival data with covariate measurement error Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20200602
LiPang Chen, Grace Y. YiMany methods have been developed for analyzing survival data which are commonly rightcensored. These methods, however, are challenged by complex features pertinent to the data collection as well as the nature of data themselves. Typically, biased samples caused by lefttruncation (or lengthbiased sampling) and measurement error often accompany survival analysis. While such data frequently arise in

Copula and composite quantile regressionbased estimating equations for longitudinal data Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20200525
Kangning Wang, Wen ShanComposite quantile regression (CQR) is a powerful complement to the usual mean regression and becomes increasingly popular due to its robustness and efficiency. In longitudinal studies, it is necessary to consider the intrasubject correlation among repeated measures to improve the estimation efficiency. This paper proposes a new approach that uses copula to account for intrasubject dependence in

Poles of pair correlation functions: When they are real? Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20200509
Ka Yiu Wong, Dietrich StoyanThe most common standard estimator of the pair correlation function (PCF) of a point process has a pole at zero, which is in most cases a statistical artifact. However, sometimes it makes sense to assume that a pole really exists. We propose two independent approaches for the proof of existence of a PCF’s pole and for the determination of its order. In the first, we use a summary characteristic F that

Multiresolution analysis of point processes and statistical thresholding for Haar waveletbased intensity estimation Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20200508
Youssef Taleb, Edward A. K. CohenWe take a waveletbased approach to the analysis of point processes and the estimation of the firstorder intensity under a continuoustime setting. A Haar wavelet multiresolution analysis is formulated which motivates the definition of homogeneity at different scales of resolution, termed Jth level homogeneity. Further to this, the activity in a point process’ firstorder behaviour at different scales

Nonparametric estimation of the kernel function of symmetric stable moving average random functions Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20200407
Jürgen Kampf, Georgiy Shevchenko, Evgeny SpodarevWe estimate the kernel function of a symmetric alpha stable (\(S\alpha S\)) moving average random function which is observed on a regular grid of points. The proposed estimator relies on the empirical normalized (smoothed) periodogram. It is shown to be weakly consistent for positive definite kernel functions, when the grid mesh size tends to zero and at the same time the observation horizon tends

On the power of some sequential multiple testing procedures Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20200402
Shiyun Chen, Ery AriasCastroWe study an online multiple testing problem where the hypotheses arrive sequentially in a stream. The test statistics are independent and assumed to have the same distribution under their respective null hypotheses. We investigate two recently proposed procedures LORD and LOND, which are proved to control the FDR in an online manner. In some (static) model, we show that LORD is optimal in some asymptotic

Quasilikelihood analysis and Bayestype estimators of an ergodic diffusion plus noise Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20200214
Shogo H. Nakakita, Yusuke Kaino, Masayuki UchidaWe consider adaptive maximumlikelihoodtype estimators and adaptive Bayestype ones for discretely observed ergodic diffusion processes with observation noise whose variance is constant. The quasilikelihood functions for the diffusion and drift parameters are introduced and the polynomialtype large deviation inequalities for those quasilikelihoods are shown to see the asymptotic properties of the

Discussion of “Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions” Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20191215
Jouchi NakajimaThe author focuses on the “decoupling and recoupling” idea that can critically increase both computational and forecasting efficiencies in practical problems for economic and financial data. My discussion is twofold. First, I briefly describe the idea with an example of timevarying vector autoregressions, which are widely used in the context. Second, I highlight the issue of how to assess patterns

The k th power expectile regression Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20191212
Yingying Jiang, Fuming Lin, Yong ZhouCheck functions of least absolute deviation make sure quantile regression methods are robust, while squared check functions make expectiles more sensitive to the tails of distributions and more effective for the normal case than quantiles. In order to balance robustness and effectiveness, we adopt a loss function, which falls in between the above two loss functions, to introduce a new kind of expectiles

Clustering of subsample means based on pairwise L1 regularized empirical likelihood Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20191212
Quynh Van Nong, Chi Tim NgTo classify a vast amount of strata or subsamples with unknown families of distributions according to their stratameans, a clustering approach is developed based on pairwise \(L_1\) regularized empirical likelihood. Under such a clustering approach, all possible contradictory conclusions are ruled out automatically. On the contrary, the decision rules based on many existing pairwise comparison procedures

Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20191209
Mike WestI discuss recent research advances in Bayesian statespace modeling of multivariate time series. A main focus is on the “decouple/recouple” concept that enables application of statespace models to increasingly largescale data, applying to continuous or discrete time series outcomes. Applied motivations come from areas such as financial and commercial forecasting and dynamic network studies. Explicit

Fixed point characterizations of continuous univariate probability distributions and their applications Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20191120
Steffen Betsch, Bruno EbnerBy extrapolating the explicit formula of the zerobias distribution occurring in the context of Stein’s method, we construct characterization identities for a large class of absolutely continuous univariate distributions. Instead of trying to derive characterizing distributional transformations that inherit certain structures for the use in further theoretic endeavors, we focus on explicit representations

Integral transform methods in goodnessoffit testing, II: the Wishart distributions Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20191120
Elena Hadjicosta, Donald RichardsWe initiate the study of goodnessoffit testing for data consisting of positive definite matrices. Motivated by the appearance of positive definite matrices in numerous applications, including factor analysis, diffusion tensor imaging, volatility models for financial time series, wireless communication systems, and polarimetric radar imaging, we apply the method of Hankel transforms of matrix argument

On the proportional hazards model with last observation carried forward covariates Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20191109
Hongyuan Cao, Jason P. FineStandard partial likelihood methodology for the proportional hazards model with timedependent covariates requires knowledge of the covariates at the observed failure times, which is not realistic in practice. A simple and commonly used estimator imputes the most recently observed covariate prior to each failure time, which is known to be biased. In this paper, we show that a weighted last observation

The debiased group Lasso estimation for varying coefficient models Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20191109
Toshio HondaThere has been much attention on the debiased or desparsified Lasso. The Lasso is very useful in highdimensional settings. However, it is well known that the Lasso produces biased estimators. Therefore, several authors proposed the debiased Lasso to fix this drawback and carry out statistical inferences based on the debiased Lasso estimators. The debiased Lasso needs desirable estimators of highdimensional

Some explicit solutions of c optimal design problems for polynomial regression with no intercept Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20191107
Holger Dette, Viatcheslav B. Melas, Petr ShpilevIn this paper, we consider the optimal design problem for extrapolation and estimation of the slope at a given point, say z, in a polynomial regression with no intercept. We provide explicit solutions of these problems in many cases and characterize those values of z, where this is not possible.

Weighted Estimating Equations for Additive Hazards Models with Missing Covariates. Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20190919
Lihong Qi,Xu Zhang,Yanqing Sun,Lu Wang,Yichuan ZhaoThis paper presents simple weighted and fully augmented weighted estimators for the additive hazards model with missing covariates when they are missing at random. The additive hazards model estimates the difference in hazards and has an intuitive biological interpretation. The proposed weighted estimators for the additive hazards model use incomplete data nonparametrically and have closeform expressions

Equivalence between adaptive Lasso and generalized ridge estimators in linear regression with orthogonal explanatory variables after optimizing regularization parameters Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20191025
Mineaki Ohishi, Hirokazu Yanagihara, Shuichi KawanoIn this paper, we deal with a penalized leastsquares (PLS) method for a linear regression model with orthogonal explanatory variables. The used penalties are an adaptive Lasso (AL)type \(\ell _1\) penalty (AL penalty) and a generalized ridge (GR)type \(\ell _2\) penalty (GR penalty). Since the estimators obtained by minimizing the PLS methods strongly depend on the regularization parameters, we

The reproducing kernel Hilbert space approach in nonparametric regression problems with correlated observations Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20191001
D. Benelmadani, K. Benhenni, S. LouhichiIn this paper, we investigate the problem of estimating the regression function in models with correlated observations. The data are obtained from several experimental units, each of them forms a time series. Using the properties of the reproducing kernel Hilbert spaces, we construct a new estimator based on the inverse of the autocovariance matrix of the observations. We give the asymptotic expressions

Semiparametric transformation boundary regression models Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20190921
Natalie Neumeyer, Leonie Selk, Charles TillierIn the context of nonparametric regression models with onesided errors, we consider parametric transformations of the response variable in order to obtain independence between the errors and the covariates. In view of estimating the transformation parameter, we use a minimum distance approach and show the uniform consistency of the estimator under mild conditions. The boundary curve, i.e., the regression

Flexible bivariate Poisson integervalued GARCH model Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20190916
Yan Cui, Qi Li, Fukang ZhuIntegervalued time series models have been widely used, especially integervalued autoregressive models and integervalued generalized autoregressive conditional heteroscedastic (INGARCH) models. Recently, there has been a growing interest in multivariate count time series. However, existing models restrict the dependence structures imposed by the way they constructed. In this paper, we consider a

Comparing the marginal densities of two strictly stationary linear processes Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20190822
Paul Doukhan, Ieva Grublytė, Denys Pommeret, Laurence ReboulIn this paper, we adapt a datadriven smooth test to the comparison of the marginal distributions of two independent, short or long memory, strictly stationary linear sequences. Some illustrations are shown to evaluate the performances of our test.

Some information inequalities for statistical inference Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20190801
K. V. Harsha, Alladi SubramanyamIn this paper, we first describe the generalized notion of Cramer–Rao lower bound obtained by Naudts (J Inequal Pure Appl Math 5(4), Article 102, 2004) using two families of probability density functions: the original model and an escort model. We reinterpret the results in Naudts (2004) from a statistical point of view and obtain some interesting examples in which this bound is attained. Further,

Robust estimation for general integervalued time series models Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20190722
Byungsoo Kim, Sangyeol LeeIn this study, we consider a robust estimation method for general integervalued time series models whose conditional distribution belongs to the oneparameter exponential family. As a robust estimator, we employ the minimum density power divergence estimator, and we demonstrate this is strongly consistent and asymptotically normal under certain regularity conditions. A simulation study is carried

Model selection for the robust efficient signal processing observed with small Lévy noise Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20190719
Slim Beltaief, Oleg Chernoyarov, Serguei PergamenchtchikovWe develop a new model selection method for an adaptive robust efficient nonparametric signal estimation observed with impulse noise which is defined by a general nonGaussian Lévy process. On the basis of the developed method, we construct estimation procedures which are analyzed in two settings: in nonasymptotic and in asymptotic ones. For the first time for such models, we show nonasymptotic sharp

Wavelet estimation of the dimensionality of curve time series Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20190715
Rodney V. Fonseca, Aluísio PinheiroFunctional data analysis is ubiquitous in most areas of sciences and engineering. Several paradigms are proposed to deal with the dimensionality problem which is inherent to this type of data. Sparseness, penalization, thresholding, among other principles, have been used to tackle this issue. We discuss here a solution based on a finitedimensional functional subspace. We employ wavelet representation

Biascorrected support vector machine with Gaussian kernel in highdimension, lowsamplesize settings Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20190715
Yugo Nakayama, Kazuyoshi Yata, Makoto AoshimaIn this paper, we study asymptotic properties of nonlinear support vector machines (SVM) in highdimension, lowsamplesize settings. We propose a biascorrected SVM (BCSVM) which is robust against imbalanced data in a general framework. In particular, we investigate asymptotic properties of the BCSVM having the Gaussian kernel and compare them with the ones having the linear kernel. We show that

An optimal test for the additive model with discrete or categorical predictors Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20190713
Abhijit MandalIn multivariate nonparametric regression, the additive models are very useful when a suitable parametric model is difficult to find. The backfitting algorithm is a powerful tool to estimate the additive components. However, due to complexity of the estimators, the asymptotic p value of the associated test is difficult to calculate without a Monte Carlo simulation. Moreover, the conventional tests assume

Poisson source localization on the plane: cusp case Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20190701
O. V. Chernoyarov, S. Dachian, Yu. A. KutoyantsThis work is devoted to the problem of estimation of the localization of Poisson source. The observations are inhomogeneous Poisson processes registered by more than three detectors on the plane. We study the behavior of the Bayes estimators in the asymptotic of large intensities. It is supposed that the intensity functions of the signals arriving in the detectors have cusptype singularity. We show

Bartlett correction of frequency domain empirical likelihood for time series with unknown innovation variance Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20190701
Kun Chen, Ngai Hang Chan, Chun Yip YauThe Bartlett correction is a desirable feature of the likelihood inference, which yields the confidence region for parameters with improved coverage probability. This study examines the Bartlett correction for the frequency domain empirical likelihood (FDEL), based on the Whittle likelihood of linear time series models. Nordman and Lahiri (Ann Stat 34:3019–3050, 2006) showed that the FDEL does not

Detecting deviations from secondorder stationarity in locally stationary functional time series Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20190617
Axel Bücher, Holger Dette, Florian HeinrichsA timedomain test for the assumption of secondorder stationarity of a functional time series is proposed. The test is based on combining individual cumulative sum tests which are designed to be sensitive to changes in the mean, variance and autocovariance operators, respectively. The combination of their dependent p values relies on a jointdependent block multiplier bootstrap of the individual test

On the indirect elicitability of the mode and modal interval Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20190516
Krisztina Dearborn, Rafael FrongilloScoring functions are commonly used to evaluate a point forecast of a particular statistical functional. This scoring function should be consistent, meaning the correct value of the functional is the Bayes act, in which case we say the scoring function elicits the functional. Recent results show that the mode functional is not elicitable. In this work, we ask whether it is at least possible to indirectly

Testing for normality in any dimension based on a partial differential equation involving the moment generating function Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20190516
Norbert Henze, Jaco VisagieWe use a system of firstorder partial differential equations that characterize the moment generating function of the dvariate standard normal distribution to construct a class of affine invariant tests for normality in any dimension. We derive the limit null distribution of the resulting test statistics, and we prove consistency of the tests against general alternatives. In the case \(d>1\), a certain

Regression function estimation as a partly inverse problem Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20190420
F. Comte, V. GenonCatalotThis paper is about nonparametric regression function estimation. Our estimator is a onestep projection estimator obtained by leastsquares contrast minimization. The specificity of our work is to consider a new model selection procedure including a cutoff for the underlying matrix inversion, and to provide theoretical risk bounds that apply to noncompactly supported bases, a case which was specifically

Estimation of extreme conditional quantiles under a general tailfirstorder condition Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20190409
Laurent Gardes, Armelle Guillou, Claire RomanWe consider the estimation of an extreme conditional quantile. In a first part, we propose a new tail condition in order to establish the asymptotic distribution of an extreme conditional quantile estimator. Next, a general class of estimators is introduced, which encompasses, among others, kernel or nearest neighbors types of estimators. A unified theorem of the asymptotic normality for this general

Nonparametric MANOVA in meaningful effects Ann. Inst. Stat. Math. (IF 0.758) Pub Date : 20190406
Dennis Dobler, Sarah Friedrich, Markus PaulyMultivariate analysis of variance (MANOVA) is a powerful and versatile method to infer and quantify main and interaction effects in metric multivariate multifactor data. It is, however, neither robust against change in units nor meaningful for ordinal data. Thus, we propose a novel nonparametric MANOVA. Contrary to existing rankbased procedures, we infer hypotheses formulated in terms of meaningful