Last year's top ten most cited papers
Top ten most cited papers in 2022 according to Web of Science (WOS)
On the interpretation of differences between groups for compositional data
Josep-Antoni Martín-Fernández, Josep Daunis-i-Estadella and Glòria Mateu-Figueras
Abstract: Social polices are designed using information collected in surveys; such as the Catalan Time Use survey. Accurate comparisons of time use data among population groups are commonly analysed using statistical methods. The total daily time expended on different activities by a single person is equal to 24 hours. Because this type of data are compositional, its sample space has particular properties that statistical methods should respect. The critical points required to interpret differences between groups are provided and described in terms of log-ratio methods. These techniques facilitate the interpretation of the relative differences detected in multivariate and univariate analysis.
Keywords: Log-ratio transformations, MANOVA, perturbation, simplex, subcomposition.
Vol 39 (2) 2015
Twenty years of P-splines
Paul H.C. Eilers, Brian D. Marx and Maria Durbán
Abstract: P-splines first appeared in the limelight twenty years ago. Since then they have become popular in applications and in theoretical work. The combination of a rich B-spline basis and a simple difference penalty lends itself well to a variety of generalizations, because it is based on regression. In effect, P-splines allow the building of a “backbone” for the “mixing and matching” of a variety of additive smooth structure components, while inviting all sorts of extensions: varying-coefficient effects, signal (functional) regressors, two-dimensional surfaces, non-normal responses, quantile (expectile) modelling, among others. Strong connections with mixed models and Bayesian analysis have been established. We give an overview of many of the central developments during the first two decades of P-splines.
Keywords: B-splines, penalty, additive model, mixed model, multidimensional smoothing.
Vol 39 (2) 2015
Thirty years of progeny from Chao’s inequality: Estimating and comparing richness with incidence data and incomplete sampling
Abstract: In the context of capture-recapture studies, Chao (1987) derived an inequality among capture frequency counts to obtain a lower bound for the size of a population based on individuals’ capture/non-capture records for multiple capture occasions. The inequality has been applied to obtain a non-parametric lower bound of species richness of an assemblage based on species incidence (detection/non-detection) data in multiple sampling units. The inequality implies that the number of undetected species can be inferred from the species incidence frequency counts of the uniques (species detected in only one sampling unit) and duplicates (species detected in exactly two sampling units). In their pioneering paper, Colwell and Coddington (1994) gave the name “Chao2” to the estimator for the resulting species richness. (The “Chao1” estimator refers to a similar type of estimator based on species abundance data). Since then, the Chao2 estimator has been applied to many research fields and led to fruitful generalizations. Here, we first review Chao’s inequality under various models and discuss some related statistical inference questions: (1) Under what conditions is the Chao2 estimator an unbiased point estimator? (2) How many additional sampling units are needed to detect any arbitrary proportion (including 100%) of the Chao2 estimate of asymptotic species richness? (3) Can other incidence frequency counts be used to obtain similar lower bounds? We then show how the Chao2 estimator can be also used to guide a non-asymptotic analysis in which species richness estimators can be compared for equally-large or equally-complete samples via sample-size-based and coverage-based rarefaction and extrapolation. We also review the generalization of Chao’s inequality to estimate species richness under other sampling-without-replacement schemes (e.g. a set of quadrats, each surveyed only once), to obtain a lower bound of undetected species shared between two or multiple assemblages, and to allow inferences about undetected phylogenetic richness (the total length of undetected branches of a phylogenetic tree connecting all species), with associated rarefaction and extrapolation. A small empirical dataset for Australian birds is used for illustration, using online software SpadeR, iNEXT, and PhD.
Keywords: Cauchy-Schwarz inequality, Chao2 estimator, extrapolation, Good-Turing frequency, formula, incidence data, phylogenetic diversity, rarefaction, sampling effort, shared species richness, species richness.
Pages: 3– 54
Vol 41 (1) 2017
A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times
Abstract: Green transportation is becoming relevant in the context of smart cities, where the use of electric vehicles represents a promising strategy to support sustainability policies. However the use of electric vehicles shows some drawbacks as well, such as their limited driving-range capacity. This paper analyses a realistic vehicle routing problem in which both driving-range constraints and stochastic travel times are considered. Thus, the main goal is to minimize the expected time-based cost required to complete the freight distribution plan. In order to design reliable Routing plans, a simheuristic algorithm is proposed. It combines Monte Carlo simulation with a multi-start metaheuristic, which also employs biased-randomization techniques. By including simulation, simheuristics extend the capabilities of metaheuristics to deal with stochastic problems. A series of computational experiments are performed to test our solving approach as well as to analyse the effect of uncertainty on the routing plans.
Keywords: Vehicle routing problem, electric vehicles, green transport and logistics, smart cities, simheuristics, biased-randomized heuristics
Vol 43 (1) 2019
Why simheuristics? Benefits, limitations, and best practices when combining metaheuristics with simulation
Abstract: Many decision-making processes in our society involve NP-hard optimization problems. The largescale, dynamism, and uncertainty of these problems constrain the potential use of stand-alone optimization methods. The same applies for isolated simulation models, which do not have the potential to find optimal solutions in a combinatorial environment. This paper discusses the utilization of modelling and solving approaches based on the integration of simulation with metaheuristics. These ‘simheuristic’ algorithms, which constitute a natural extension of both metaheuristics and simulation techniques, should be used as a ‘first-resort’ method when addressing large-scale and NP-hard optimization problems under uncertainty –which is a frequent case in real-life applications. We outline the benefits and limitations of simheuristic algorithms, provide numerical experiments that validate our arguments, review some recent publications, and outline the best practices to consider during their design and implementation stages.
Keywords: Simulation, metaheuristics, combinatorial optimization, simheuristics
Vol 44 (2) 2020
The exponentiated discrete Weibull distribution
Vahid Nekoukhou and Hamid Bidram
Abstract: In this paper, the exponentiated discrete Weibull distribution is introduced. This new generalization of the discrete Weibull distribution can also be considered as a discrete analogue of the exponentiated Weibull distribution. A special case of this exponentiated discrete Weibull distribution defines a new generalization of the discrete Rayleigh distribution for the first time in the literature. In addition, discrete generalized exponential and geometric distributions are some special sub-models of the new distribution. Here, some basic distributional properties, moments, and order statistics of this new discrete distribution are studied. We will see that the hazard rate function can be in- creasing, decreasing, bathtub, and upside-down bathtub shaped. Estimation of the parameters is illustrated using the maximum likelihood method. The model with a real data set is also examined.
Keywords: Discrete generalized exponential distribution, exponentiated discrete Weibull distribution, exponentiated Weibull distribution, geometric distribution, infinite divisibility, order statistics, resilience parameter family, stress-strength parameter.
Vol 39 (1) 2015
On developing ridge regression parameters: a graphical investigation
Gisela Muniz, B. M. Golam Kibria, Kristofer Mansson and Ghazi Shukur
Abstract: In this paper we review some existing and propose some new estimators for estimating the Ridge parameter. All in all 19 different estimators have been studied. The investigation has been carried out using Monte Carlo simulations. A large number of different models have been investigated where the variance of the random error, the number of variables included in the model, the correlations among the explanatory variables, the sample size and the unknown coefficient vector were varied. For each model we have performed 2000 replications and presented the results both in term of figures and tables. Based on the simulation study, we found that increasing the number of correlated variable, the variance of the random error and increasing the correlation between the independent variables have negative effect on the mean squared error. When the sample size increases the mean squared error decreases even when the correlation between the independent variables and the variance of the random error are large. In all situations, the proposed estimators have smaller mean squared error than the ordinary least squares and other existing estimators.
Keywords: Linear model, LSE, MSE, Monte Carlo simulations, multicollinearity, ridge regression
Vol 36 (2) 2012
Field rules and bias in random surveys with quota samples. An assessment of CIS surveys
Abstract: Surveys applying quota sampling in their final step are widely used in opinion and market research all over the world. This is also the case in Spain, where the surveys carried out by CIS (a public institution for sociological research supported by the government) have become a point of reference. The rules used by CIS to select individuals within quotas, however, could be improved as they lead to biases in age distributions. Analysing more than 545,000 responses collected in the 220 monthly barometers conducted between 1997 and 2016 by CIS, we compare the empirical distributions of the barometers with the expected distributions from the sample design and/or target populations. Among other results, we find, as a consequence of the rules used, significant overrepresentations in the observed proportions of respondents with ages equal to the minimum and maximum of each quota (age and gender group). Furthermore, in line with previous literature, we also note a significant overrepresentation of ages ending in zero. After offering simple solutions to avoid all these biases, we discuss some of their consequences for modelling and inference and about limitations and potentialities of CIS data
Keywords: Centre for Sociological Research, quota sampling, fieldwork rules, age and gender groups, inter-quota distributions, intra-quota distributions
Vol 42 (2) 2018
Markovian arrivals in stochastic modeling: a survey and some new results
Jesús Artalejo, Antonio Gómez-Corral and Qi-Ming He
Abstract: This paper aims to provide a comprehensive review on Markovian arrival processes (MAPs), which constitute a rich class of point processes used extensively in stochastic modelling. Our starting point is the versatile process introduced by Neuts (1979) which, under some simplified notation, was coined as the batch Markovian arrival process (BMAP). On the one hand, a general point process can be approximated by appropriate MAPs and, on the other hand, the MAPs provide a versatile, yet tractable option for modelling a bursty flow by preserving the Markovian formalism. While a number of well-known arrival processes are subsumed under a BMAP as special cases, the literature also shows generalizations to model arrival streams with marks, nonhomogeneous settings or even spatial arrivals. We survey on the main aspects of the BMAP, discuss on some of its variants and generalizations, and give a few new results in the context of a recent state-dependent extension.
Keywords: Markovian arrival process, batch arrivals, marked process, phase-type distribution, BSDE approach
Vol 34 (2) 2010
A general procedure of estimating the population mean in the presence of non-response under double sampling using auxiliary information
Housila P. Singh and Sunil Kumar
Abstract: In the present study, we propose a general class of estimators for population mean of the study variable in the presence of non-response using auxiliary information under double sampling. The expression of mean squared error (MSE) of the proposed class of estimators is derived under double (two-stage) sampling. Some estimators are also derived from the proposed class by allocating the suitable values of constants used. Comparisons of the proposed strategy with the usual unbiased estimator and other estimators are carried out. The results obtained are illustrated numerically using an empirical sample considered in the literature.
Keywords: Double sampling, Mean squared error, Non-response, Study variable, Auxiliary variable
Vol 33 (1) 2009