On a control chart for the Gini index with simulations

In this work we suggest the use of the Gini index on control charts. The asymptotic properties of Gini index are presented and the control charts based on appropriate confidence intervals are constructed. The suitability of the proposed charts are investigated by means of extensive simulations.     

Interval censored and truncated data: Rate of convergence of NPMLE of the density

We consider survival data that are both interval censored and truncated. Under appropriate assumptions on the involved distributions, the censoring, truncation and survival, we prove the consistency of the NPMLE of the density of the survival, and give the rate of convergence. Finally, we give an example where the joint law of the censoring and truncation can be explicitly computed.     

EFFICIENT ESTIMATION IN REGRESSION FRAILTY OR TRANSFORMATION MODELS BASED ON AN ALGORITHM

This paper considers a regression frailty or transformation model in which the structural parameter is the vector of regression coefficients and the nuisance parameter is a vector of arbitrarily high dimension. It proposes jointly (implicitly) defined parameter estimators which have been proved to be consistent and asymptotically efficient, and develops an algorithmic procedure that provides these estimators. The behaviour of the algorithm is illustrated by analysing simulated and real data.     

Frailty Models for Arbitrarily Censored and Truncated Data

In this paper, we propose a frailty model for statistical inference in the case where we are faced with arbitrarily censored and truncated data. Our results extend those of Alioum and Commenges (1996), who developed a method of fitting a proportional hazards model to data of this kind. We discuss the identifiability of the regression coefficients involved in the model which are the parameters of interest, as well as the identifiability of the baseline cumulative hazard function of the model which plays the role of the infinite dimensional nuisance parameter. We illustrate our method with the use of simulated data as well as with a set of real data on transfusion-related AIDS.     

Higher Moments and Prediction‐Based Estimation for the COGARCH(1,1) Model

COGARCH models are continuous time versions of the well‐known GARCH models of financial returns. The first aim of this paper is to show how the method of prediction‐based estimating functions can be applied to draw statistical inference from observations of a COGARCH(1,1) model if the higher‐order structure of the process is clarified. A second aim of the paper is to provide recursive expressions for the joint moments of any fixed order of the process. Asymptotic results are given, and a simulation study shows that the method of prediction‐based estimating function outperforms the other available estimation methods.     

A real survival analysis application via variable selection methods for Cox's proportional hazards model

Variable selection is fundamental to high-dimensional statistical modeling in diverse fields of sciences. In our health study, different statistical methods are applied to analyze trauma annual data, collected by 30 General Hospitals in Greece. The dataset consists of 6334 observations and 111 factors that include demographic, transport, and clinical data. The statistical methods employed in this work are the nonconcave penalized likelihood methods, Smoothly Clipped Absolute Deviation, Least Absolute Shrinkage and Selection Operator, and Hard, the maximum partial likelihood estimation method, and the best subset variable selection, adjusted to Cox's proportional hazards model and used to detect possible risk factors, which affect the length of stay in a hospital. A variety of different statistical models are considered, with respect to the combinations of factors while censored observations are present. A comparative survey reveals several differences between results and execution times of each method. Finally, we provide useful biological justification of our results.     

How Is Power Shared in Africa?

Is African politics characterized by concentrated power in the hands of a narrow group (ethnically determined) that then fluctuates from one extreme to another via frequent coups? Employing data on the ethnicity of cabinet ministers since independence, we show that African ruling coalitions are surprisingly large and that political power is allocated proportionally to population shares across ethnic groups. This holds true even restricting the analysis to the subsample of the most powerful ministerial posts. We argue that the likelihood of revolutions from outsiders and coup threats from insiders are major forces explaining allocations within these regimes. Alternative allocation mechanisms are explored. Counterfactual experiments that shed light on the role of Western policies in affecting African national coalitions and leadership group premia are performed.     

Strategic Choice of Variability in Multiround Contests and Contests with Handicaps

Variability can be an important strategic variable in a contest. We study optimal strategies involving choice of variability in contests with fixed and probabilistic targets, one-round and multiround contests, contests with and without handicaps, and situations where one contestant can modify variability as well as those in which all contestants have this opportunity. A contestant should maximize variability in a weak position (low mean, high handicap, or low previous performance) and minimize variability in a strong position. In some cases, only these extremes should be used. In other cases, intermediate levels of variability are optimal when the contestant's position is neither too weak nor too strong.     

Variable selection strategies in survival models with multiple imputations

In this paper, the variable selection strategies (criteria) are thoroughly discussed and their use in various survival models is investigated. The asymptotic efficiency property, in the sense of Shibata Ann Stat 8: 147-164, 1980, of a class of variable selection strategies which includes the AIC and all criteria equivalent to it, is established for a general class of survival models, such as parametric frailty or transformation models and accelerated failure time models, under minimum conditions. Furthermore, a multiple imputations method is proposed which is found to successfully handle censored observations and constitutes a competitor to existing methods in the literature. A number of real and simulated data are used for illustrative purposes.