A power log-Dagum distribution: estimation and applications

Development and application of probability models in data analysis are of major importance for all sciences. Therefore, we introduce a new model called a power log-Dagum distribution defined on the entire real line. The model contains many new sub-models: power logistic, linear log-Dagum, linear logistic and log-Dagum distributions among them. Some properties of the model including three different estimation procedures are justified. The model exhibits various shapes for the density and hazard rate functions. Moreover, the estimation procedures are compared using simulation studies. Finally, the model with others are fitted to three data sets, and it shows a better fit than the compared distributions defined on the real line.     

Wavelet Estimation of a Density in a GARCH-type Model

We consider the GARCH-type model: S = σ² Z, where σ² and Z are independent random variables. The density of σ² is unknown whereas the one of Z is known. We want to estimate the density of σ² from n observations of S under some dependence assumption (the exponentially strongly mixing dependence). Adopting the wavelet methodology, we construct a nonadaptive estimator based on projections and an adaptive estimator based on the hard thresholding rule. Taking the mean integrated squared error over Besov balls, we prove that the adaptive one attains a sharp rate of convergence.     

The Valuation of Non-Monetary Consumption in Household Surveys

Many social indicators are based on household consumption information. The valuation of non-monetary operations is crucial for the analysis of consumption surveys in developing countries because of the importance of own-consumption and transfers in kind. What are the price statistics used in the valuation of consumption indicators? How is the available price information exploited to produce consumption indicators? How can the different steps of the valuation process be analysed? We explore these questions by presenting the valuation method for the consumption used in rural Rwanda for the 1983 consumption survey, and by proposing a general model of valuation algorithm. This is useful not only for improving such algorithms, but also for assessing the impact of the valuation process on economic analyses.     

On the estimation of female births missing due to prenatal sex selection

This research note is prompted by a paper by Kashyap (Is prenatal sex selection associated with lower female child mortality? Population Studies 73(1): 57–78). Kashyap’s paper, which provides 40 original estimates of missing female births, relies on an alternative definition of missing female births, leading to estimates of about half the magnitude of other estimates. There appears, therefore, a real need to take stock of the concept of missing female births widely used by statisticians around the world for assessing the demographic consequences of prenatal sex selection. This research note starts with a brief review of the history of the concept and the difference between Amartya Sen’s original method and the alternative method found elsewhere to compute missing female births. We then put forward three different arguments (deterministic and probabilistic approaches, and consistency analysis) in support of the original computation procedure based on the number of observed male births and the expected sex ratio at birth.     

Peakedness‐Based Staffing for Call Center Outsourcing

This study considers the staffing problem of a vendor call center in a co‐sourcing setting. The aim is to take short‐term variability and correlations in time for call arrivals at such a vendor call center into account. To do so, peakedness is proposed as a useful measure of the burstiness in the arrival stream. The study empirically demonstrates the presence of bursty arrivals at a call center and proposes an approach to the measurement of the peakedness of the arrival stream making use of standard call center data. The problematic nature of bursty arrivals in the context of call center co‐sourcing is demonstrated along with an asymptotic result establishing that the problem persists in large call centers. The study then analyzes two peakedness‐based staffing methods: one which is a well known extension of the square root staffing rule and another which makes use of the Hayward approximation principles. Both approaches are simple and enable the vendor to improve its staffing procedure with good accuracy.     

Minimum d-blockers and d-transversals in graphs

We consider a set V of elements and an optimization problem on V: the search for a maximum (or minimum) cardinality subset of V verifying a given property ℘. A d-transversal is a subset of V which intersects any optimum solution in at least d elements while a d-blocker is a subset of V whose removal deteriorates the value of an optimum solution by at least d. We present some general characteristics of these problems, we review some situations which have been studied (matchings, s–t paths and s–t cuts in graphs) and we study d-transversals and d-blockers of stable sets or vertex covers in bipartite and in split graphs.     

Initializing EM using the properties of its trajectories in Gaussian mixtures

A strategy is proposed to initialize the EM algorithm in the multivariate Gaussian mixture context. It consists in randomly drawing, with a low computational cost in many situations, initial mixture parameters in an appropriate space including all possible EM trajectories. This space is simply defined by two relations between the two first empirical moments and the mixture parameters satisfied by any EM iteration. An experimental study on simulated and real data sets clearly shows that this strategy outperforms classical methods, since it has the nice property to widely explore local maxima of the likelihood function.     

Testing Infants' Discrimination With the Orientation Latency Procedure

A new discrimination procedure based on the measurement of visual orientation latency to speech stimuli is introduced. Each participant listens to a series of short familiarization test trials. In each trial, 5 to 7 centrally‐presented familiarization stimuli are followed by laterally‐presented test stimuli. Infants were found to orient faster to different‐category than to same‐category test stimuli. This result was found despite a high degree of prosodic variability in the familiarization and test stimuli introduced by changes in talker and speaking rate. The combination of a multitrial design with use of acoustic and prosodic variability seems suitable for studying the representation of phonological categories.     

Simple estimation of the mode of a multivariate density

The authors consider an estimate of the mode of a multivariate probability density using a kernel estimate drawn from a random sample. The estimate is defined by maximizing the kernel estimate over the set of sample values. The authors show that this estimate is strongly consistent and give an almost sure rate of convergence. This rate depends on the sharpness of the density near the true mode, which is measured by a peak index.     

The influence function of penalized regression estimators

To perform regression analysis in high dimensions, lasso or ridge estimation are a common choice. However, it has been shown that these methods are not robust to outliers. Therefore, alternatives as penalized M-estimation or the sparse least trimmed squares (LTS) estimator have been proposed. The robustness of these regression methods can be measured with the influence function. It quantifies the effect of infinitesimal perturbations in the data. Furthermore, it can be used to compute the asymptotic variance and the mean-squared error (MSE). In this paper we compute the influence function, the asymptotic variance and the MSE for penalized M-estimators and the sparse LTS estimator. The asymptotic biasedness of the estimators make the calculations non-standard. We show that only M-estimators with a loss function with a bounded derivative are robust against regression outliers. In particular, the lasso has an unbounded influence function.