Frontier-based Methods Research Articles

Risk-aversion versus risk-loving preferences in nonparametric frontier-based fund ratings: A buy-and-hold backtesting strategy

The eventual risk-loving nature of preferences of investors has largely been ignored in the existing frontier-based fund rating literature. This contribution develops a series of nonparametric frontier-based methods to rate mutual funds accounting for both mixed risk-loving and mixed risk-aversion preferences. These new methods are proposed by defining the corresponding shortage functions that can allow for increases in all moments, or increases in odd moments and reductions in even moments. The empirical part designs a buy-and-hold backtesting to test the out-of-sample performance of the proposed rating methods corresponding to different risk preferences on the actual MF selection. The evidence indicates that the backtesting strategies based on the output frontier-based rating models with risk-loving preferences exhibit an overwhelming dominance compared to most existing frontier-based and traditional financial ratings.

A heuristic autonomous exploration method based on environmental information gain during quadrotor flight

Autonomous exploration is a widely studied fundamental application in the field of quadrotor, which requires them to automatically explore unknown space to obtain complete information about the environment. The frontier-based method, one of the representative works on autonomous exploration, drives autonomous determination by the definition of frontier information so that complete information about the environment is available to the quadrotor. However, existing frontier-based methods are able to accomplish the task but still suffer from inefficient exploration, and how to improve the efficiency of autonomous exploration is the focus of research nowadays. Slow frontier generation affecting real-time viewpoint determination and insufficient determination methods affecting the quality of viewpoints are typical of these problems. Therefore, to overcome the aforementioned problems, this article proposes a two-level viewpoint determination method for frontier-based autonomous exploration. First, a sampling-based frontier detection method is presented for faster frontier generation, improving the immediacy of environmental representation compared to traditional traversal-based methods. Second, the access to environmental information during flight is considered for the first time, and an innovative heuristic evaluation function is designed to decide on high-quality viewpoint as the next local navigation target in each exploration iteration. Extensive benchmark and real-world tests are conducted to validate our method. The results confirm that our method optimizes the frontier search time by 85%, the exploration time by around 20%–30%, and the exploration path by 25%–35%.

Efficiency analyses for the railway sector: An overview of key issues

Abstract 1 Over the past 3-4 decades numerous efficiency analyses have been undertaken in different economic sectors including transport. These studies provide an insight into variations of efficiency levels for different entities (e.g. country or company comparisons). As such they can be used to identify strategies for improvement at company level as well as broader policy measures. This paper will focus on the so-called frontier-based methods covering both parametric (e.g. Stochastic Frontier Analysis) and non-parametric (e.g. Data Envelopment Analysis) methods. It will set out main challenges in applying these methods in the railway sector as well as highlighting key findings according to the studies undertaken to date. Moreover, the paper will discuss issues from methodological and empirical perspectives which would need to be addressed in future research.

A sampling-based multi-tree fusion algorithm for frontier detection

Autonomous exploration is a key step toward real robotic autonomy. Among various approaches for autonomous exploration, frontier-based methods are most commonly used. One efficient method of frontier detection exploits the idea of the rapidly-exploring random tree and uses tree edges to search for frontiers. However, this method usually needs to consume a lot of memory resources and searches for frontiers slowly in the environments where random trees are not easy to grow (unfavorable environments). In this article, a sampling-based multi-tree fusion algorithm for frontier detection is proposed. Firstly, the random tree’s growing and storage rules are changed so that the disadvantage of its slow growing under unfavorable environments is overcome. Secondly, a block structure is proposed to judge whether tree nodes in a block play a decisive role in frontier detection, so that a large number of redundant tree nodes can be deleted. Finally, two random trees with different growing rules are fused to speed up frontier detection. Experimental results in both simulated and real environments demonstrate that our algorithm for frontier detection consumes fewer memory resources and shows better performances in unfavorable environments.

Measuring management practices

Good management practices are remarkably difficult to robustly measure, especially when unique data on firms and their managers are not available. We propose a new model estimated with Bayesian techniques that requires only the usual accounting data on inputs and outputs and thus can be applied to any firm. We show that our management practices estimates are more than 90% correlated with existing state-of-the-art measures from a very specialized data set by Bloom and Van Reenen (2007). We also obtain very high correlations when conducting an extensive Monte Carlo analysis. Finally, we show that frontier-based methods previously used to estimate management practices do not provide good approximations.

A step forward on order-α robust nonparametric method: inclusion of weight restrictions, convexity and non-variable returns to scale

Partial frontiers have been recently developed in order to overcome several drawbacks of the traditional nonparametric techniques. These robust frontier (order-\(\alpha\) and order-m) methods avoid the curse of dimensionality, are less sensitive to outliers and extreme data and may include direct environmental information in the model. Nonetheless, the disadvantages of these partial frontier-based methods according to the formulation proposed in the literature are that they do not allow weight restrictions or non-variable returns to scale technology. The procedure here proposed is an extension of the traditional order-\(\alpha\) method, allowing the estimation of an empirical convex \(\alpha\)-level, assuming also some additional constraints, such as the virtual weight restrictions and non-variable returns to scale. In the particular case of nonconvex attainable sets, unrestricted formulations and variable returns to scale assumption, the proposed procedure returns the same results as the standard order-\(\alpha\).

Measuring Managerial Ability: Getting Close to the Holy Grail

Managerial ability is remarkably difficult to robustly measure, especially when unique data on firms and their managers are not available. We propose a new latent-variable model estimated with Bayesian techniques that requires only the usual accounting data on inputs and outputs and thus can be applied to any firm. We show that our managerial ability estimates are more than 90% correlated with existing robust measures derived from a very specialized data set by Bloom and Van Reenen (2007). We also validate our model using Monte Carlo simulations and show that frontier-based methods previously used to estimate managerial ability do not provide good approximations. We conclude that our method produces robust estimates of managerial ability, while having the advantage that can be applied to all firms for which very simple accounting data are available.

Comparing efficiency of holding business model and individual management model of airports

This research compares the efficiency of holding business model to individual management model of airports, employing some robust non-parametric partial frontier-based methods to compare the statistical distributions of efficiency, under different scenarios, to find out which group of airports yields better global performance. The comparison between groups will follow a Malmquist index decomposition, which seems to be the most appropriate tool for within- and inter-group performance comparison. For this purpose, a sample of 145 airports from three continents is utilized. The results provide evidence that European airports are the most productive ones, and within this cluster, the individual management model presented a significant frontier shift with respect the holding cluster frontier, meaning that the former is much more productive than the latter.

Productivity change using growth accounting and frontier-based approaches – Evidence from a Monte Carlo analysis

This study presents some quantitative evidence from a number of simulation experiments on the accuracy of the productivity growth estimates derived from growth accounting (GA) and frontier-based methods (namely data envelopment analysis-, corrected ordinary least squares-, and stochastic frontier analysis-based malmquist indices) under various conditions. These include the presence of technical inefficiency, measurement error, misspecification of the production function (for the GA and parametric approaches) and increased input and price volatility from one period to the next. The study finds that the frontier-based methods usually outperform GA, but the overall performance varies by experiment. Parametric approaches generally perform best when there is no functional form misspecification, but their accuracy greatly diminishes otherwise. The results also show that the deterministic approaches perform adequately even under conditions of (modest) measurement error and when measurement error becomes larger, the accuracy of all approaches (including stochastic approaches) deteriorates rapidly, to the point that their estimates could be considered unreliable for policy purposes.

A conceptual framework for selecting the most appropriate variables for measuring hospital efficiency with a focus on Iranian public hospitals

Over the past few decades, there has been an increasing interest in the measurement of hospital efficiency in developing countries and in Iran. While the choice of measurement methods in hospital efficiency assessment has been widely argued in the literature, few authors have offered a framework to specify variables that reflect different hospital functions, the quality of the process of care and the effectiveness of hospital services. However, without the knowledge of hospital objectives and all relevant functions, efficiency studies run the risk of making biased comparisons, particularly against hospitals that provide higher quality services requiring the use of more resources. Undertaking an in-depth investigation regarding the multi-product nature of hospitals, various hospital functions and the values of various stakeholders (patient, staff and community) with a focus on the Iranian public hospitals, this study has proposed a conceptual framework to select the most appropriate variables for measuring hospital efficiency using frontier-based techniques. This paper contributes to hospital efficiency studies by proposing a conceptual framework and incorporating a broader set of variables in Iran. This can enhance the validity of hospital efficiency studies using frontier-based methods in developing countries.

Reference models and incentive regulation of electricity distribution networks: An evaluation of Sweden's Network Performance Assessment Model (NPAM)

Electricity sector reforms across the world have led to a search for innovative approaches to regulation that promote efficiency in the natural monopoly distribution networks and reduce their service charges. To this aim, a number of countries have adopted incentive regulation models based on efficiency benchmarking. While most regulators have used parametric and non-parametric frontier-based methods of benchmarking some have adopted engineering-designed “reference firm” or “norm” models. This paper examines the incentive properties and related aspects of the reference firm model—NPAM—as used in Sweden and compares this with frontier-based benchmarking methods. We identify a number of important differences between the two approaches that are not readily apparent and discuss their ramifications for the regulatory objectives and process. We conclude that, on balance, the reference models are less appropriate as benchmarks than real firms. Also, the implementation framework based on annual ex-post reviews exacerbates the regulatory problems mainly by increasing uncertainty and reducing the incentive for innovation.