Two-Term Edgeworth Expansions for the Classes of U- and V-statistics

Much effort has been devoted to deriving Edgeworth expansions for various classes of statistics that are asymptotically normally distributed, with derivations tailored to the individual structure of each class. Expansions with smaller error rates are needed for more accurate statistical inference. Two such Edgeworth expansions are derived analytically in this paper. One is a two-term expansion for the standardized U-statistic of order m, m ? 3, with an error rate o(n^{? 1}). The other is an expansion with the same error rate for the distribution of the standardized V-statistic of the same order. In deriving the Edgeworth expansion, we made use of the close connection between the V- and U-statistics, which permits to first derive the needed expansion for the related U-statistic, then extend it to the V-statistic, taking into consideration the estimation of all difference terms between the two statistics.     

Asymptotic behaviour of M‐estimators in AR(p) models under nonstandard conditions

The authors derive the limiting distribution of M‐estimators in AR(p) models under nonstandard conditions, allowing for discontinuities in score and density functions. Unlike usual regularity assumptions, these conditions are satisfied in the context of L₁‐estimation and autoregression quantiles. The asymptotic distributions of the resulting estimators, however, are not generally Gaussian. Moreover, their bootstrap approximations are consistent along very specific sequences of bootstrap sample sizes only.     

Likelihood ratio tests for covariance hypotheses generating commutative quadratic subspaces

We show that the likelihood ratio (LR) tests, for covariance hypotheses in multivariate normal models, take the form of a product of powers of independent beta variates whenever the covariance matrices generate a commutative quadratic subspace (CQS), See Seely (1971), under both the model and the hypothesis.     

Risk‐Based Input‐Output Analysis of Influenza Epidemic Consequences on Interdependent Workforce Sectors

Outbreaks of contagious diseases underscore the ever‐looming threat of new epidemics. Compared to other disasters that inflict physical damage to infrastructure systems, epidemics can have more devastating and prolonged impacts on the population. This article investigates the interdependent economic and productivity risks resulting from epidemic‐induced workforce absenteeism. In particular, we develop a dynamic input‐output model capable of generating sector‐disaggregated economic losses based on different magnitudes of workforce disruptions. An ex post analysis of the 2009 H1N1 pandemic in the national capital region (NCR) reveals the distribution of consequences across different economic sectors. Consequences are categorized into two metrics: (i) economic loss, which measures the magnitude of monetary losses incurred in each sector, and (ii) inoperability, which measures the normalized monetary losses incurred in each sector relative to the total economic output of that sector. For a simulated mild pandemic scenario in NCR, two distinct rankings are generated using the economic loss and inoperability metrics. Results indicate that the majority of the critical sectors ranked according to the economic loss metric comprise of sectors that contribute the most to the NCR's gross domestic product (e.g., federal government enterprises). In contrast, the majority of the critical sectors generated by the inoperability metric include sectors that are involved with epidemic management (e.g., hospitals). Hence, prioritizing sectors for recovery necessitates consideration of the balance between economic loss, inoperability, and other objectives. Although applied specifically to the NCR, the proposed methodology can be customized for other regions.     

The Adaptive Gril Estimator with a Diverging Number of Parameters

We consider the problem of variables selection and estimation in linear regression model in situations where the number of parameters diverges with the sample size. We propose the adaptive Generalized Ridge-Lasso (\mboxAdaGril) which is an extension of the the adaptive Elastic Net. AdaGril incorporates information redundancy among correlated variables for model selection and estimation. It combines the strengths of the quadratic regularization and the adaptively weighted Lasso shrinkage. In this article, we highlight the grouped selection property for AdaCnet method (one type of AdaGril) in the equal correlation case. Under weak conditions, we establish the oracle property of AdaGril which ensures the optimal large performance when the dimension is high. Consequently, it achieves both goals of handling the problem of collinearity in high dimension and enjoys the oracle property. Moreover, we show that AdaGril estimator achieves a Sparsity Inequality, i.e., a bound in terms of the number of non-zero components of the “true” regression coefficient. This bound is obtained under a similar weak Restricted Eigenvalue (RE) condition used for Lasso. Simulations studies show that some particular cases of AdaGril outperform its competitors.     

Guided Censored Regression

Parametrically guided non‐parametric regression is an appealing method that can reduce the bias of a non‐parametric regression function estimator without increasing the variance. In this paper, we adapt this method to the censored data case using an unbiased transformation of the data and a local linear fit. The asymptotic properties of the proposed estimator are established, and its performance is evaluated via finite sample simulations.     

Local Weighted Average Estimation of the Regression Operator for Functional Data

We are concerned with the problem of local weighted average estimation of the regression operator when the responses are real-valued random variables, the explanatory data are of functional fixed-design type, and the errors consist of an independent and identically distributed variables. In this article, our main contributions on the local linear functional estimation concern from one part, the situation when the data are of functional fixed-design kind, and from the other part, in deriving uniform asymptotic results on the behavior of this estimator with respect to the topological properties of the space data (normed or semi-metric).     

Benefits of adopting lean production on green performance of SMEs: a case study

Abstract

Small- and medium-sized enterprises (SMEs) are widely recommended to strive, not only for their performance in operational side, but they need to integrate the environmental dimension. To do that, the literature widely indicates a positive impact of lean practices on green performance. However, very few studies have been carried out in SMEs context. In this view, this paper aims at providing through a practical case study of lean implementation in a real-life small manufacturing company, the information to confirm this synergy between lean and green performance. To achieve this aim, a proposed framework of lean and green integration has been implemented in a SME producing different types of pumps. The results achieved shows that there is a strong correlation between operational metrics improvement and green metrics improvement. Moreover, the benefits of lean practices such as 5S, Kanban, SMED, AM, cellular manufacturing and quality control on green performance are widely demonstrated. Future work could study other processes and explore other case-specific supporting tools and techniques.     

Rural Telephone Company Adoption of Service Innovations: A Community Field Theory Approach*

Abstract Recent telecommunications innovations have the potential to improve the economic vitality of rural communities, but many rural telephone companies have not adopted them to provide needed advanced services. To explain the differences in adoption by rural telephone companies we distinguish between service innovations, adopted primarily to improve direct services to clients, and operations innovations, adopted primarily to improve the operation of the business. Using community interaction field theory we develop and test a conceptual framework for the adoption of innovative service telecommunications technologies by Iowa rural telephone companies. As field theory predicts, involvement in local economic development activities has a strong, direct effect on innovativeness. Results suggest that the service and operations innovations distinction has theoretical utility in explaining organizational innovativeness.