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  Subjects -> STATISTICS (Total: 130 journals)
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Statistical Papers
Journal Prestige (SJR): 1.004
Citation Impact (citeScore): 1
Number of Followers: 4  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1613-9798 - ISSN (Online) 0932-5026
Published by Springer-Verlag Homepage  [2467 journals]
  • Kalman recursions Aggregated Online

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      Abstract: Abstract In this article, we aim to improve the prediction from experts’ aggregation by using the underlying properties of the models that provide the experts involved in the aggregation procedure. We restrict ourselves to the case where experts perform their predictions by fitting state-space models to the data using Kalman recursions. Using exponential weights, we construct different Kalman recursions Aggregated Online (KAO) algorithms that compete with the best expert or the best convex combination of experts in a more or less adaptive way. When the experts are Kalman recursions, we improve the existing results on experts’ aggregation literature, taking advantage of the second-order properties of the Kalman recursions. We apply our approach to Kalman recursions and extend it to the general adversarial expert setting by state-space modeling the experts’ errors. We apply these new algorithms to a real-data set of electricity consumption and show how they can improve forecast performances compared to other exponentially weighted average procedures.
      PubDate: 2023-03-16
       
  • Nonnegative group bridge and application in financial index tracking

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      Abstract: Abstract The stock index plays an increasingly important role in investors’ decision-making. With the continuous development of the stock markets and the advancement of financial technology, the methods of compiling stock indices have consistently improved. Index tracking attempts to match the performance of a target market index by setting up a portfolio of assets to obtain similar returns to the target index. Therefore, the methods of selecting which stocks constitute a portfolio are very important. In daily investing, investors select quality assets from the target index to include in their tracking portfolio. In this paper, a nonnegative group bridge method is proposed for variable selection and estimation of grouping variables without overlapping to aid stock selection. The estimation consistency, variable-selection consistency, and asymptotic property of this method are provided. To obtain the solution of this model, we use an idea based on the local group coordinate descent method. Using tracking error as the criterion, the nonnegative group bridge estimation method is found superior to other nonnegative methods in terms of goodness-of-fit.
      PubDate: 2023-03-15
       
  • Computational aspects of experimental designs in multiple-group mixed
           models

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      Abstract: Abstract We extend the equivariance and invariance conditions for construction of optimal designs to multiple-group mixed models and, hence, derive the support of optimal designs for first- and second-order models on a symmetric square. Moreover, we provide a tool for computation of D- and L-efficient exact designs in multiple-group mixed models by adapting the algorithm of Harman et al. (Appl Stoch Models Bus Ind, 32:3–17, 2016). We show that this algorithm can be used both for size-constrained problems and also in settings that require multiple resource constraints on the design, such as cost constraints or marginal constraints.
      PubDate: 2023-03-13
       
  • Correction to: Some properties of the unified skew-normal distribution

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      PubDate: 2023-03-10
       
  • Efficient robust estimation for single-index mixed effects models with
           missing observations

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      Abstract: Abstract In this paper, we study the efficient robust estimation and empirical likelihood for a single-index mixed effects model with a subset of covariates and response missing at random. Three efficient robust estimators and empirical likelihood ratios for index coefficients are constructed using weighted, imputed and weighted-imputed method, their asymptotic properties are proved. Our results show that the three estimators are asymptotically equivalent, and a weighted-imputed empirical log-likelihood ratio is asymptotically chi-squared. An important feature of our methods is their ability to handle missing response and/or partially missing covariates. Some simulation studies and a real data example indicate that our methods have fine performance in finite sample, and are available in practice.
      PubDate: 2023-03-10
       
  • Sparse and smooth functional data clustering

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      Abstract: Abstract A new model-based procedure is developed for sparse clustering of functional data that aims to classify a sample of curves into homogeneous groups while jointly detecting the most informative portions of the domain. The proposed method is referred to as sparse and smooth functional clustering (SaS-Funclust) and relies on a general functional Gaussian mixture model whose parameters are estimated by maximizing a log-likelihood function penalized with a functional adaptive pairwise fusion penalty and a roughness penalty. The former allows identifying the noninformative portion of the domain by shrinking the means of separated clusters to some common values, whereas the latter improves the interpretability by imposing some degree of smoothing to the estimated cluster means. The model is estimated via an expectation-conditional maximization algorithm paired with a cross-validation procedure. Through a Monte Carlo simulation study, the SaS-Funclust method is shown to outperform other methods that already appeared in the literature, both in terms of clustering performance and interpretability. Finally, three real-data examples are presented to demonstrate the favourable performance of the proposed method. The SaS-Funclust method is implemented in the R package sasfunclust, available on CRAN.
      PubDate: 2023-03-09
       
  • Parameters not empirically identifiable or distinguishable, including
           correlation between Gaussian observations

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      Abstract: Abstract It is shown that some theoretically identifiable parameters cannot be empirically identified, meaning that no consistent estimator of them can exist. An important example is a constant correlation between Gaussian observations (in presence of such correlation not even the mean can be empirically identified). Empirical identifiability and three versions of empirical distinguishability are defined. Two different constant correlations between Gaussian observations cannot even be empirically distinguished. A further example are cluster membership parameters in k-means clustering. Several existing results in the literature are connected to the new framework. General conditions are discussed under which independence can be distinguished from dependence.
      PubDate: 2023-03-05
       
  • Instrumental variable estimation of weighted local average treatment
           effects

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      Abstract: Abstract Instrumental variable (IV) analysis addresses bias owing to unmeasured confounding when comparing two nonrandomized treatment groups. To date, studies in the statistical and biomedical literature have focused on the local average treatment effect (LATE), the average treatment effect for compliers. In this article, we study the weighted local average treatment effect (WLATE), which represents the weighted average treatment effect for compliers. In the WLATE, the population of interest is determined by either the instrumental propensity score or compliance score, or both. The LATE is a special case of the proposed WLATE, where the target population is the entire population of compliers. Here, we discuss the interpretation of a few special cases of the WLATE, identification results, inference methods, and optimal weights. We demonstrate the proposed methods with two published examples in which considerations of local causal estimands that deviate from the LATE are beneficial.
      PubDate: 2023-03-03
       
  • Clustering and estimation of finite mixture models under bivariate ranked
           set sampling with application to a breast cancer study

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      Abstract: Abstract In the literature on modeling heterogeneous data via mixture models, it is generally assumed that the samples are drawn from the underlying population using the simple random sampling (SRS) technique. This study exploits the bivariate ranked set sampling (BVRSS) technique to learn finite mixture models. We generalize the expectation-maximization (EM) algorithm under univariate RSS to the bivariate case. Computationally, through a simulation study under a noisy setting, we compare the performance of the proposed rank-based estimators with that of the SRS-based competitors in estimating unknown parameters and cluster assignments. The proposed methodology is applied to a breast cancer data set to diagnose malignant or benign tumors in patients. The results showed that the extra rank information in BVRSS samples leads to a better inference about the unknown features of mixture models.
      PubDate: 2023-03-02
       
  • A method of correction for heaping error in the variables using validation
           data

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      Abstract: Abstract When self-reported data are used in statistical analysis to estimate the mean and variance, as well as the regression parameters, the estimates tend, in many cases, to be biased. This is because interviewees have a tendency to heap their answers to certain values. The aim of the paper is to examine the bias-inducing effect of the heaping error in self-reported data, and study the effect on the heaping error on the mean and variance of a distribution as well as the regression parameters. As a result a new method is introduced to correct the effects of bias due to the heaping error using validation data. Using publicly available data and simulation studies, it can be shown that the newly developed method is practical and can easily be applied to correct the bias in the estimated mean and variance, as well as in the estimated regression parameters computed from self-reported data. Hence, using the method of correction presented in this paper allows researchers to draw accurate conclusions leading to the right decisions, e.g. regarding health care planning and delivery.
      PubDate: 2023-02-21
       
  • Simulations and predictions of future values in the time-homogeneous
           load-sharing model

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      Abstract: Abstract In this paper, some properties of the order dependent time-homogeneous load-sharing model are obtained, including an algorithmic procedure to simulate samples from this model. Then, the problem of how to get predictions of the future failure times is analysed in a sample from censored data (early failures). Punctual predictions based on the median, the mean and the convolutions of exponential distributions are proposed and prediction bands are obtained. Some illustrative examples show how to apply the theoretical results. An application in the study of lifetimes of coherent systems is proposed as well.
      PubDate: 2023-02-17
       
  • Discrete mixture representations of spherical distributions

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      Abstract: Abstract We obtain discrete mixture representations for parametric families of probability distributions on Euclidean spheres, such as the von Mises–Fisher, the Watson and the angular Gaussian families. In addition to several special results we present a general approach to isotropic distribution families that is based on density expansions in terms of special surface harmonics. We discuss the connections to stochastic processes on spheres, in particular random walks, discrete mixture representations derived from spherical diffusions, and the use of Markov representations for the mixing base to obtain representations for families of spherical distributions.
      PubDate: 2023-02-15
       
  • Convergence arguments to bridge cauchy and matérn covariance
           functions

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      Abstract: Abstract The Matérn and the Generalized Cauchy families of covariance functions have a prominent role in spatial statistics as well as in a wealth of statistical applications. The Matérn family is crucial to index mean-square differentiability of the associated Gaussian random field; the Cauchy family is a decoupler of the fractal dimension and Hurst effect for Gaussian random fields that are not self-similar. Our effort is devoted to prove that a scale-dependent family of covariance functions, obtained as a reparameterization of the Generalized Cauchy family, converges to a particular case of the Matérn family, providing a somewhat surprising bridge between covariance models with light tails and covariance models that allow for long memory effect.
      PubDate: 2023-02-15
       
  • Detecting shifts in Conway–Maxwell–Poisson profile with deviance
           residual-based CUSUM and EWMA charts under multicollinearity

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      Abstract: Abstract Monitoring profiles with count responses is a common situation in industrial processes and for a count distributed process, the Conway–Maxwell–Poisson (COM-Poisson) regression model yields better outcomes for under- and overdispersed count variables. In this study, we propose CUSUM and EWMA charts based on the deviance residuals obtained from the COM-Poisson model, which are fitted by the PCR and r–k class estimators. We conducted a simulation study to evaluate the effect of additive and multiplicative types shifts in various shift sizes, the number of predictor, and several dispersion levels and to compare the performance of the proposed control charts with control charts in the literature in terms of average run length and standard deviation of run length. Moreover, a real data set is also analyzed to see the performance of the newly proposed control charts. The results show the superiority of the newly proposed control charts against some competitors, including CUSUM and EWMA control charts based on ML, PCR, and ridge deviance residuals in the presence of multicollinearity.
      PubDate: 2023-02-15
       
  • A multivariate modified skew-normal distribution

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      Abstract: Abstract We introduce a multivariate version of the modified skew-normal distribution, which contains the multivariate normal distribution as a special case. Unlike the Azzalini multivariate skew-normal distribution, this new distribution has a nonsingular Fisher information matrix when the skewness parameters are all zero, and its profile log-likelihood of the skewness parameters is always a non-monotonic function. We study some basic properties of the proposed family of distributions and present an expectation-maximization (EM) algorithm for parameter estimation that we validate through simulation studies. Finally, we apply the proposed model to the univariate frontier data and to a trivariate wind speed data, and compare its performance with the Azzalini skew-normal model.
      PubDate: 2023-02-13
       
  • A review on design inspired subsampling for big data

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      Abstract: Abstract Subsampling focuses on selecting a subsample that can efficiently sketch the information of the original data in terms of statistical inference. It provides a powerful tool in big data analysis and gains the attention of data scientists in recent years. In this review, some state-of-the-art subsampling methods inspired by statistical design are summarized. Three types of designs, namely optimal design, orthogonal design, and space filling design, have shown their great potential in subsampling for different objectives. The relationships between experimental designs and the related subsampling approaches are discussed. Specifically, two major families of design inspired subsampling techniques are presented. The first aims to select a subsample in accordance with some optimal design criteria. The second tries to find a subsample that meets some design requirements, including balancing, orthogonality, and uniformity. Simulated and real data examples are provided to compare these methods empirically.
      PubDate: 2023-02-13
       
  • Testing normality of a large number of populations

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      Abstract: Abstract This paper studies the problem of simultaneously testing that each of k independent samples come from a normal population. The means and variances of those populations may differ. The proposed procedures are based on the BHEP test and they allow k to increase, which can be even larger than the sample sizes.
      PubDate: 2023-02-12
       
  • Centre-free kurtosis orderings for asymmetric distributions

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      Abstract: Abstract The concept of kurtosis is used to describe and compare theoretical and empirical distributions in a multitude of applications. In this connection, it is commonly applied to asymmetric distributions. However, there is no rigorous mathematical foundation establishing what is meant by kurtosis of an asymmetric distribution and what is required to measure it properly. All corresponding proposals in the literature centre the comparison with respect to kurtosis around some measure of central location. Since this either disregards critical amounts of information or is too restrictive, we instead revisit a canonical approach that has barely received any attention in the literature. It reveals the non-transitivity of kurtosis orderings due to an intrinsic entanglement of kurtosis and skewness as the underlying problem. This is circumvented by restricting attention to sets of distributions with equal skewness, on which the proposed kurtosis ordering is shown to be transitive. Moreover, we introduce a functional that preserves this order for arbitrary asymmetric distributions. As application, we examine the families of Weibull and sinh-arcsinh distributions and show that the latter family exhibits a skewness-invariant kurtosis behaviour.
      PubDate: 2023-02-09
       
  • Generalised score distribution: underdispersed continuation of the
           beta-binomial distribution

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      Abstract: Abstract Consider a class of discrete probability distributions with a limited support. A typical example of such support is some variant of a Likert scale, with a response mapped to either the \(\{1, 2, \ldots , 5\}\) or \(\{-3, -2, \ldots , 2, 3\}\) set. Such type of data is common for Multimedia Quality Assessment but can also be found in many other research fields. For modelling such data a latent variable approach is usually used (e.g., Ordered Probit). In many cases it is convenient or even necessary to avoid latent variable approach (e.g., when dealing with too small sample size). To avoid it the proper class of discrete distributions is needed. The main idea of this paper is to propose a family of discrete probability distributions with only two parameters that play the same role as the parameters of the normal distribution. We call the new class the Generalised Score Distribution (GSD). The proposed GSD class covers the entire set of possible means and variances, for any fixed and finite support. Furthermore, the GSD class can be treated as an underdispersed continuation of a reparametrized beta-binomial distribution. The GSD class parameters are intuitive and can be easily estimated by the method of moments. We also offer a Maximum Likelihood Estimation (MLE) algorithm for the GSD class and evidence that the class properly describes response distributions coming from 24 Multimedia Quality Assessment experiments. At last, we show that the GSD class can be represented as a sum of dichotomous zero–one random variables, which points to an interesting interpretation of the class.
      PubDate: 2023-02-09
       
  • Several new classes of space-filling designs

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      Abstract: Abstract Mappable nearly orthogonal arrays were recently proposed as a new class of space-filling designs for computer experiments. Inspired by mappable nearly orthogonal arrays, we propose several new classes of space-filling designs. The corresponding construction methods are provided. The resulting designs are more space-filling than mappable nearly orthogonal arrays while accommodating a large number of factors. In addition to the space-filling properties, the column orthogonality is also desirable for designs of computer experiments. Among the new constructed designs, one class is column-orthogonal, and the other two classes, providing many new column-orthogonal designs, are nearly column-orthogonal in the sense that each column is column-orthogonal to a large proportion of the other columns. The constructed designs are good choices for computer experiments due to their attractive space-filling properties and column orthogonality. The proposed construction methods are flexible in the choices of an orthogonal array and/or a strong orthogonal array and their usefulness is appealing. Many newly constructed space-filling designs are tabulated. The expansive replacement method and the generalized doubling play key roles in the constructions.
      PubDate: 2023-02-06
       
 
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