A Measure of Production Performance

Kokic, Philip; Chambers, Ray; Breckling, Jens; Beare, Steve

doi:10.2307/1392490

Cited by 13 publications

(8 citation statements)

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“…For example, they were used to describe the relationship between healthy life expectancy and health care status of a country in Evans et al (2001); Hollingsworth and Wildman (2003). In addition to DEA and SFA Kokic et al (1997) propose M-quantiles to model production frontiers. It can be shown that there is a relationship between expectiles and M-quantiles (Jones 1994).…”

Section: Econometric Methods For Efficiency Estimationmentioning

confidence: 99%

“…Note that the reference distribution is being used here only as a guide for selecting values of p. It is possible and useful to compare the empirical expectiles with theoretical expectiles of the reference distribution, but we do not pursue this approach here. Kokic et al (1997) propose M-quantiles to model production frontiers and to measure productive efficiency. According to Jones (1994) there is a relationship between expectiles and M-quantiles.…”

Section: What Do We Call a Frontier?mentioning

confidence: 99%

“…From previous applications in this area (see Hollingsworth and Wildman 2003) as well as from the theoretical connections we suggest efficiency analysis in the context of LAWS. As a variant of the definition suggested in Kokic et al (1997) we assign as value for the so called performance the value p of the closest p-expectile (in terms of absolute distance):…”

Section: What Do We Call a Frontier?mentioning

confidence: 99%

See 2 more Smart Citations

An analysis of life expectancy and economic production using expectile frontier zones

Schnabel¹,

Eilers²

2009

DemRes

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The wealth of a country is assumed to have a strong non-linear influence on the life expectancy of its inhabitants. We follow up on research by Preston and study the relationship with gross domestic product. Smooth curves for the average but also for upper frontiers are constructed by a combination of least asymmetrically weighted squares and P -splines. Guidelines are given for optimizing the amount of smoothing and the definition of frontiers. The model is applied to a large set of countries in different years. It is also used to estimate life expectancy performance for individual countries and to show how it changed over time.

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Section: Econometric Methods For Efficiency Estimationmentioning

confidence: 99%

Section: What Do We Call a Frontier?mentioning

confidence: 99%

Section: What Do We Call a Frontier?mentioning

confidence: 99%

See 1 more Smart Citation

An analysis of life expectancy and economic production using expectile frontier zones

Schnabel¹,

Eilers²

2009

DemRes

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“…One of the earlier applications of M ‐quantile modelling was Kokic et al (). In this article, M ‐quantile regression was used to calculate a performance measure, which had very practical uses.…”

Section: M‐quantile Models For Group Heterogeneitymentioning

confidence: 99%

Modelling Group Heterogeneity for Small Area Estimation Using M‐Quantiles

Dawber

Chambers

2018

Int Statistical Rev

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Summary Small area estimation typically requires model‐based methods that depend on isolating the contribution to overall population heterogeneity associated with group (i.e. small area) membership. One way of doing this is via random effects models with latent group effects. Alternatively, one can use an M‐quantile ensemble model that assigns indices to sampled individuals characterising their contribution to overall sample heterogeneity. These indices are then aggregated to form group effects. The aim of this article is to contrast these two approaches to characterising group effects and to illustrate them in the context of small area estimation. In doing so, we consider a range of different data types, including continuous data, count data and binary response data.

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“…Equation (8) takes into account both the downweight of the influence points for the response and auxiliary variables. Moreover, the IWLS algorithm guarantees the convergence to a unique solution (Kokic et al 1997;Chambers and Tzavidis 2006).…”

Section: M-quantile Estimators With Outliers In the Auxiliary Variablesmentioning

confidence: 99%

Small area estimation based on M-quantile models in presence of outliers in auxiliary variables

et al. 2017

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When using small area estimation models, the presence of outlying observations in the response and/or in the auxiliary variables can severely affect the estimates of the model parameters, which can in turn affect the small area estimates produced using these models. In this paper we propose an M-quantile estimator of the small area mean that is robust to the presence of outliers in the response variable and in the continuous auxiliary variables. To estimate the variability of this estimator we propose a non-parametric bootstrap estimator. The performance of the proposed estimator is evaluated by means of model- and design-based simulations and by an application to real data. In these comparisons we also include the extension of the Robust EBLUP able to down-weight the outliers in the auxiliary variables. The results show that in the presence of outliers in the auxiliary variables the proposed estimator outperforms its traditional version that takes into account the presence of outliers only in the response variable

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A Measure of Production Performance

Cited by 13 publications

References 0 publications

An analysis of life expectancy and economic production using expectile frontier zones

An analysis of life expectancy and economic production using expectile frontier zones

Modelling Group Heterogeneity for Small Area Estimation Using M‐Quantiles

Small area estimation based on M-quantile models in presence of outliers in auxiliary variables

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