A coordinate descent algorithm for computing penalized smooth quantile regression

The computation of penalized quantile regression estimates is often computationally intensive in high dimensions. In this paper we propose a coordinate descent algorithm for computing the penalized smooth quantile regression (cdaSQR) with convex and nonconvex penalties. The cdaSQR approach is based on the approximation of the objective check function, which is not differentiable at zero, by a modified check function which is differentiable at zero. Then, using the maximization-minimization trick of the gcdnet algorithm (Yang and Zou in, J Comput Graph Stat 22(2):396–415, 2013), we update each coefficient simply and efficiently. In our implementation, we consider the convex penalties \(\ell _1+\ell _2\) and the nonconvex penalties SCAD (or MCP) \(+ \ell _2\). We establishe the convergence property of the csdSQR with \(\ell _1+\ell _2\) penalty. The numerical results show that our implementation is an order of magnitude faster than its competitors. Using simulations we compare the speed of our algorithm to its competitors. Finally, the performance of our algorithm is illustrated on three real data sets from diabetes, leukemia and Bardet–Bidel syndrome gene expression studies.     

Multiscale local polynomial decompositions using bandwidths as scales

The multiscale local polynomial transform, developped in this paper, combines the benefits from local polynomial smoothing with sparse multiscale decompositions. The contribution of the paper is twofold. First, it focusses on the bandwidths used throughout the transform. These bandwidths operate as user controlled scales in a multiscale analysis, which is explained to be of particular interest in the case of nonequispaced data. The paper presents both a likelihood based optimal bandwidth selection and a fast, heuristic approach. The second contribution of the paper is the combination of local polynomial smoothing with orthogonal prefilters, similar to Daubechies’ wavelet filters, but defined on irregularly spaced covariate values.     

A systematic review of research into the management of manufacturing capabilities

Abstract

Our study aims to explore the past and present knowledge related to the management of manufacturing capabilities and to assess the extent to which our knowledge of this field has developed. It places specific emphasis on the philosophical foundations of the field, while also providing a set of directions for future research. A systematic review is applied, covering 104 articles. The authors unfold a strong dominance of the positivist paradigm and call for a more balanced and informed approach in philosophical and, more specifically, methodological selection by scholars. In terms of the research content, a strong bias exists towards measuring the impact that certain manufacturing capabilities and various configurations of these may have on key performance indicators, such as cost. Our review warns on the dangers of following a dominant paradigmatic stance and on the limitations of researching a limited area of the complex and dynamic manufacturing capabilities arena.     

Deciphering the Relationship Between Internal Migration and Regional Disparities in Tunisia

This study analyses the key factors that shape inter-governorates migration in Tunisia, focusing mainly on the role of demographic, geographical and socio-economic factors in driving migration flows. It uses basic and extended gravity models, as well as Poisson pseudo-maximum-likelihood model for modeling migration data to assess the relative importance of distance, job market characteristics and economic variables. The main findings reveal that inter-governorate migrations in Tunisia are affected by high population size at the origin and destination locations, high unemployment rate at the origin and low unemployment rate at the destination. The results suggest also that migration flows are negatively affected by high job vacancies and the annual average per capita household expenditure at the origin.     

Rate of uniform consistency for a class of mode regression on functional stationary ergodic data

The aim of this paper is to study the asymptotic properties of a class of kernel conditional mode estimates whenever functional stationary ergodic data are considered. To be more precise on the matter, in the ergodic data setting, we consider a random elements (X, Z) taking values in some semi-metric abstract space \(E\times F\). For a real function \(\varphi \) defined on the space F and \(x\in E\), we consider the conditional mode of the real random variable \(\varphi (Z)\) given the event “\(X=x\)”. While estimating the conditional mode function, say \(\theta _\varphi (x)\), using the well-known kernel estimator, we establish the strong consistency with rate of this estimate uniformly over Vapnik–Chervonenkis classes of functions \(\varphi \). Notice that the ergodic setting offers a more general framework than the usual mixing structure. Two applications to energy data are provided to illustrate some examples of the proposed approach in time series forecasting framework. The first one consists in forecasting the daily peak of electricity demand in France (measured in Giga-Watt). Whereas the second one deals with the short-term forecasting of the electrical energy (measured in Giga-Watt per Hour) that may be consumed over some time intervals that cover the peak demand.     

An Arab perspective on social media: How banks in Kuwait use instagram for public relations

This study examines the public relations functions of eight Kuwaiti banks listed in the Kuwait Stock Exchange Market (KSEM) that post on their Instagram accounts. It also uses market-size, organization, and individual level predictors to model Instagram postings. A content analysis of 1502 posts revealed the banks use Instagram for promotion, information dissemination, community building, and interactive engagement, in that order of prominence. The findings suggest banks operate at the confluence of visual communication through Instagram and integration of religious symbolism to serve a public relations function predicated on image and influence. In doing so, the banks tap into the wellspring of social media use by target publics by navigating a balance between obedience to Allah and meeting bank business objectives. The resulting tension between sacred and secular highlights Arab cultural values and suggests Instagram fosters individualism, which challenges Arab emphasis on collectivism and fractures the notion online platforms can effectively build meaningful relationships that characterize Arab culture. The study posits Instagram is more effective in an Arab context at image building than relationship building, threading the importance of visual communication through social media across Arab culture and global public relations practices.     

Testing for constancy in varying coefficient models

We consider varying coefficient models, which are an extension of the classical linear regression models in the sense that the regression coefficients are replaced by functions in certain variables (for example, time), the covariates are also allowed to depend on other variables. Varying coefficient models are popular in longitudinal data and panel data studies, and have been applied in fields such as finance and health sciences. We consider longitudinal data and estimate the coefficient functions by the flexible B-spline technique. An important question in a varying coefficient model is whether an estimated coefficient function is statistically different from a constant (or zero). We develop testing procedures based on the estimated B-spline coefficients by making use of nice properties of a B-spline basis. Our method allows longitudinal data where repeated measurements for an individual can be correlated. We obtain the asymptotic null distribution of the test statistic. The power of the proposed testing procedures are illustrated on simulated data where we highlight the importance of including the correlation structure of the response variable and on real data.     

An ensemble method for concept drift in nonstationary environment

Most statistical and data-mining algorithms assume that data come from a stationary distribution. However, in many real-world classification tasks, data arrive over time and the target concept to be learned from the data stream may change accordingly. Many algorithms have been proposed for learning drifting concepts. To deal with the problem of learning when the distribution generating the data changes over time, dynamic weighted majority was proposed as an ensemble method for concept drift. Unfortunately, this technique considers neither the age of the classifiers in the ensemble nor their past correct classification. In this paper, we propose a method that takes into account expert's age as well as its contribution to the global algorithm's accuracy. We evaluate the effectiveness of our proposed method by using m classifiers and training a collection of n-fold partitioning of the data. Experimental results on a benchmark data set show that our method outperforms existing ones.     

A robust principal component analysis

This work is concerned with robustness in Principal Component Analysis (PCA). The approach, which we adopt here, is to replace the criterion of least squares by another criterion based on a convex and sufficiently differentiable loss function ρ. Using this criterion we propose a robust estimate of the location vector and introduce an orthogonality with respect to (w.r.t.) ρ in order to define the different steps of a PCA. The influence functions of a vector mean and principal vectors are developed in order to provide method for obtaining a robust PCA. The practical procedure is based on an alternative-steps algorithm.