Asta-Advances in Statistical Analysis最新文献

The authors make an important contribution presenting a comprehensive and thoughtful overview about the many different aspects of data, statistics and data analyses in times of the recent COVID-19 pandemic discussing all relevant topics. The paper certainly provides a very valuable reflection of what has been done, what could have been done and what needs to be done. We contribute here with a few comments and some additional issues. We do not discuss all chapters of Jahn et al. (AStA Adv Stat Anal, 2022. 10.1007/s10182-022-00439-7), but focus on those where our personal views and experiences might add some additional aspects.

Multi-regional input–output (I/O) matrices provide the networks of within- and cross-country economic relations. In the context of I/O analysis, the methodology adopted by national statistical offices in data collection raises the issue of obtaining reliable data in a timely fashion and it makes the reconstruction of (parts of) the I/O matrices of particular interest. In this work, we propose a method combining hierarchical clustering and matrix completion with a LASSO-like nuclear norm penalty, to predict missing entries of a partially unknown I/O matrix. Through analyses based on both real-world and synthetic I/O matrices, we study the effectiveness of the proposed method to predict missing values from both previous years data and current data related to countries similar to the one for which current data are obscured. To show the usefulness of our method, an application based on World Input–Output Database (WIOD) tables—which are an example of industry-by-industry I/O tables—is provided. Strong similarities in structure between WIOD and other I/O tables are also found, which make the proposed approach easily generalizable to them.

In the Corona pandemic, it became clear with burning clarity how much good quality statistics are needed, and at the same time how unsuccessful we are at providing such statistics despite the existing technical and methodological possibilities and diverse data sources. It is therefore more than overdue to get to the bottom of the causes of these issues and to learn from the findings. This defines a high aspiration, namely that firstly a diagnosis is carried out in which the causes of the deficiencies with their interactions are identified as broadly as possible. Secondly, such a broad diagnosis should result in a therapy that includes a coherent strategy that can be generalised, i.e. that goes beyond the Corona pandemic.

The paper explores a testing problem which involves four hypotheses, that is, based on observations of two random variables X and Y, we wish to discriminate between four possibilities: identical survival functions, stochastic dominance of X over Y, stochastic dominance of Y over X, or crossing survival functions. Four-decision testing procedures for repeated measurements data are proposed. The tests are based on a permutation approach and do not rely on distributional assumptions. One-sided versions of the Cramér–von Mises, Anderson–Darling, and Kolmogorov–Smirnov statistics are utilized. The consistency of the tests is proven. A simulation study shows good power properties and control of false-detection errors. The suggested tests are applied to data from a psychophysical experiment.

A myriad of physical, biological and other phenomena are better modeled with semi-infinite distribution families, in which case not knowing the population minimum becomes a hassle when performing parametric inference. Ad hoc methods to deal with this problem exist, but are suboptimal and sometimes unfeasible. Besides, having the statistician handcraft solutions in a case-by-case basis is counterproductive. In this paper, we propose a framework under which the issue can be analyzed, and perform an extensive search in the literature for methods that could be used to solve the aforementioned problem; we also propose a method of our own. Simulation experiments were then performed to compare some methods from the literature and our proposal. We found that the straightforward method, which is to infer the population minimum by maximum likelihood, has severe difficulty in giving a good estimate for the population minimum, but manages to achieve very good inferred models. The other methods, including our proposal, involve estimating the population minimum, and we found that our method is superior to the other methods of this kind, considering the distributions simulated, followed very closely by the endpoint estimator by Alves et al. (Stat Sin 24(4):1811–1835, 2014). Although these two give much more accurate estimates for the population minimum, the straightforward method also displays some advantages, so choosing between these three methods will depend on the problem domain.

In this paper, we propose the use of advanced and flexible statistical models to describe the spatial displacement of earthquake data. The paper aims to account for the external geological information in the description of complex seismic point processes, through the estimation of models with space varying parameters. A local version of the Log-Gaussian Cox processes (LGCP) is introduced and applied for the first time, exploiting the inferential tools in Baddeley (Spat Stat 22:261–295, 2017), estimating the model by the local Palm likelihood. We provide methods and approaches accounting for the interaction among points, typically described by LGCP models through the estimation of the covariance parameters of the Gaussian Random Field, that in this local version are allowed to vary in space, providing a more realistic description of the clustering feature of seismic events. Furthermore, we contribute to the framework of diagnostics, outlining suitable methods for the local context and proposing a new step-wise approach addressing the particular case of multiple covariates. Overall, we show that local models provide good inferential results and could serve as the basis for future spatio-temporal local model developments, peculiar for the description of the complex seismic phenomenon.

This paper deals with the estimation of kurtosis on large datasets. It aims at overcoming two frequent limitations in applications: first, Pearson's standardized fourth moment is computed as a unique measure of kurtosis; second, the fact that data might be just samples is neglected, so that the opportunity of using suitable inferential tools, like standard errors and confidence intervals, is discarded. In the paper, some recent indexes of kurtosis are reviewed as alternatives to Pearson’s standardized fourth moment. The asymptotic distribution of their natural estimators is derived, and it is used as a tool to evaluate efficiency and to build confidence intervals. A simulation study is also conducted to provide practical indications about the choice of a suitable index. As a conclusion, researchers are warned against the use of classical Pearson’s index when the sample size is too low and/or the distribution is skewed and/or heavy-tailed. Specifically, the occurrence of heavy tails can deprive Pearson’s index of any meaning or produce unreliable confidence intervals. However, such limitations can be overcome by reverting to the reviewed alternative indexes, relying just on low-order moments.

External preference mapping is widely used in marketing and R&D divisions to understand the consumer behaviour. The most common preference map is obtained through a two-step procedure that combines principal component analysis and least squares regression. The standard approach exploits classical regression and therefore focuses on the conditional mean. This paper proposes the use of quantile regression to enrich the preference map looking at the whole distribution of the consumer preference. The enriched maps highlight possible different consumer behaviour with respect to the less or most preferred products. This is pursued by exploring the variability of liking along the principal components as well as focusing on the direction of preference. The use of different aesthetics (colours, shapes, size, arrows) equips standard preference map with additional information and does not force the user to change the standard tool she/he is used to. The proposed methodology is shown in action on a case study pertaining yogurt preferences.

In this work, we propose a novel group selection method called Group Square-Root Elastic Net. It is based on square-root regularization with a group elastic net penalty, i.e., a (ell _{2,1}+ell _2) penalty. As a type of square-root-based procedure, one distinct feature is that the estimator is independent of the unknown noise level (sigma ), which is non-trivial to estimate under the high-dimensional setting, especially when (pgg n). In many applications, the estimator is expected to be sparse, not in an irregular way, but rather in a structured manner. It makes the proposed method very attractive to tackle both high-dimensionality and structured sparsity. We study the correct subset recovery under a Group Elastic Net Irrepresentable Condition. Both the slow rate bounds and fast rate bounds are established, the latter under the Restricted Eigenvalue assumption and Gaussian noise assumption. To implement, a fast algorithm based on the scaled multivariate thresholding-based iterative selection idea is introduced with proved convergence. A comparative study examines the superiority of our approach against alternatives.