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.     

Off-farm labour decision of Canadian farm operators: Urbanization effects and rural labour market linkages

Understanding the factors affecting off-farm labour decisions of census-farm operators has significant implications for rural development and farm income support policy. We examine the off-farm labour decisions of Canadian farm operators using micro-level data from the 2001 Census of Agriculture combined with community level data from the 2001 Census of Population. While confirming some of the findings of previous research with respect to the effects of human capital and farm characteristics on off-farm work participation, this study shows the differential impact of those variables for operators of smaller and larger holdings. Family, community and regional characteristics appear more relevant in determining the joint decision to work off-farm and operate a smaller holding, compared to the decision to work off-farm and operate a larger farm. Results suggest that, once other factors are accounted for, proximity to urban centres does not have a positive effect on the joint decisions to participate in off-farm work and to operate a holding. This joint decision, in fact, is more related to the dynamics of the local labour market. A major implication of these findings is that while urban centers might represent an engine of growth for overall rural income through employment opportunities for the non-farm workforce, the non-farm income of farm operators is more likely to be affected by policy initiatives that address directly the dynamics of labour markets in the community where the operator lives.     

Conflicting Subcultures in Mergers and Acquisitions: a Longitudinal Study of Integrating a Radical Internet Firm into a Bureaucratic Telecoms Firm

Media and telecommunications companies face the problem of how to integrate diametrically opposite radical internet firms after acquisition. Extant mergers and acquisitions (M&A) studies report that differences in the organizational culture are important in the cultural integration process. Frequently, M&A research assumes organizational cultures to be homogeneous and unified, but a large body of organizational literature suggests that organizations should be understood as heterogeneous living worlds in which employees construct their own subcultures. The paper focuses on the question of how such subcultures affect the long‐term cultural integration of merged firms. A 12‐year longitudinal field study in the Netherlands examined the integration of iPioneer into Telcom. The findings of the study show how three subcultures in iPioneer influenced the cultural integration process. The paper makes a contribution to the academic debate on cultural integration in domestic M&A by acknowledging that the numerous coexisting subcultures influence cultural integration in the complex process of post‐acquisition integration.     

Semiparametric Estimation of Stochastic Production Frontier Models

This article extends the linear stochastic frontier model proposed by Aigner, Lovell, and Schmidt to a semiparametric frontier model in which the functional form of the production frontier is unspecified and the distributions of the composite error terms are of known form. Pseudolikelihood estimators of the parameters characterizing the two error terms of the model are constructed based on kernel estimation of the conditional mean function. The Monte Carlo results show that the proposed estimators perform well in finite samples. An empirical application is presented. Extensions to a partially linear frontier function and to more flexible one-sided error distributions than the half-normal are discussed     

Informing the selection of futility stopping thresholds: case study from a late-phase clinical trial

In an environment where (i) potential risks to subjects participating in clinical studies need to be managed carefully, (ii) trial costs are increasing, and (iii) there are limited research resources available, it is necessary to prioritize research projects and sometimes re-prioritize if early indications suggest that a trial has low probability of success. Futility designs allow this re-prioritization to take place. This paper reviews a number of possible futility methods available and presents a case study from a late-phase study of an HIV therapeutic, which utilized conditional power-based stopping thresholds. The two most challenging aspects of incorporating a futility interim analysis into a trial design are the selection of optimal stopping thresholds and the timing of the analysis, both of which require the balancing of various risks. The paper outlines a number of graphical aids that proved useful in explaining the statistical risks involved to the study team. Further, the paper outlines a decision analysis undertaken which combined expectations of drug performance with conditional power calculations in order to produce probabilities of different interim and final outcomes, and which ultimately led to the selection of the final stopping thresholds.     

Simulation of close-to-reality population data for household surveys with application to EU-SILC

Statistical simulation in survey statistics is usually based on repeatedly drawing samples from population data. Furthermore, population data may be used in courses on survey statistics to explain issues regarding, e.g., sampling designs. Since the availability of real population data is in general very limited, it is necessary to generate synthetic data for such applications. The simulated data need to be as realistic as possible, while at the same time ensuring data confidentiality. This paper proposes a method for generating close-to-reality population data for complex household surveys. The procedure consists of four steps for setting up the household structure, simulating categorical variables, simulating continuous variables and splitting continuous variables into different components. It is not required to perform all four steps so that the framework is applicable to a broad class of surveys. In addition, the proposed method is evaluated in an application to the European Union Statistics on Income and Living Conditions (EU-SILC).