Constrained nonparametric estimation of input distance function

This paper proposes a constrained nonparametric method of estimating an input distance function. A regression function is estimated via kernel methods without functional form assumptions. To guarantee that the estimated input distance function satisfies its properties, monotonicity constraints are imposed on the regression surface via the constraint weighted bootstrapping method borrowed from statistics literature. The first, second, and cross partial analytical derivatives of the estimated input distance function are derived, and thus the elasticities measuring input substitutability can be computed from them. The method is then applied to a cross-section of 3,249 Norwegian timber producers.

The enhanced Russell-based directional distance measure with undesirable outputs: Numerical example considering CO2 emissions

Following the spirit of the enhanced Russell graph measure, this paper proposes an enhanced Russell-based directional distance measure (ERBDDM) model for dealing with desirable and undesirable outputs in data envelopment analysis (DEA) and allowing some inputs and outputs to be zero. The proposed method is analogous to the output oriented slacks-based measure (OSBM) and directional output distance function approach because it allows the expansion of desirable outputs and the contraction of undesirable outputs. The ERBDDM is superior to the OSBM model and traditional approach since it is not only able to identify all the inefficiency slacks just as the latter, but also avoids the misperception and misspecification of the former, which fails to identify null-jointness production of goods and bads. The paper also imposes a strong complementary slackness condition on the ERBDDM model to deal with the occurrence of multiple projections. Furthermore, we use the Penn Table data to help us explore our new approach in the context of environmental policy evaluations and guidance for performance improvements in 111 countries.

A grid-based index method for time warping distance

Recently DTW (dynamic time warping) has been recognized as the most robust distance function to measure the similarity between two time series, and this fact has spawned a flurry of research on this topic. Most indexing methods proposed for DTW are based on the R-tree structure. Because of high dimensionality and loose lower bounds for time warping distance, the pruning power of these tree structures are quite weak, resulting in inefficient search. In this paper, we propose a dimensionality reduction method motivated by observations about the inherent character of each time series. A very compact index file is constructed. By scanning the index file, we can get a very small candidate set, so that the number of page access is dramatically reduced. We demonstrate the effectiveness of our approach on real and synthetic datasets.

Learning sparse latent representation and distance metric for image retrieval

The performance of image retrieval depends critically on the semantic representation and the distance function used to estimate the similarity of two images. A good representation should integrate multiple visual and textual (e.g., tag) features and offer a step closer to the true semantics of interest (e.g., concepts). As the distance function operates on the representation, they are interdependent, and thus should be addressed at the same time. We propose a probabilistic solution to learn both the representation from multiple feature types and modalities and the distance metric from data. The learning is regularised so that the learned representation and information-theoretic metric will (i) preserve the regularities of the visual/textual spaces, (ii) enhance structured sparsity, (iii) encourage small intra-concept distances, and (iv) keep inter-concept images separated. We demonstrate the capacity of our method on the NUS-WIDE data. For the well-studied 13 animal subset, our method outperforms state-of-the-art rivals. On the subset of single-concept images, we gain 79:5% improvement over the standard nearest neighbours approach on the MAP score, and 45.7% on the NDCG.

A Bayesian approach to imposing curvature on distance functions

The estimated parameters of output distance functions frequently violate the monotonicity, quasi-convexity and convexity constraints implied by economic theory, leading to estimated elasticities and shadow prices that are incorrectly signed, and ultimately to perverse conclusions concerning the effects of input and output changes on productivity growth and relative efficiency levels. We show how a Bayesian approach can be used to impose these constraints on the parameters of a translog output distance function. Implementing the approach involves the use of a Gibbs sampler with data augmentation. A Metropolis-Hastings algorithm is also used within the Gibbs to simulate observations from truncated pdfs. Our methods are developed for the case where panel data is available and technical inefficiency effects are assumed to be time-invariant. Two models-a fixed effects model and a random effects model-are developed and applied to panel data on 17 European railways. We observe significant changes in estimated elasticities and shadow price ratios when regularity restrictions are imposed. (c) 2004 Elsevier B.V. All rights reserved.

An improved conflicting evidence combination approach based on a new supporting probability distance

To avoid counter-intuitive result of classical Dempster's combination rule when dealing with highly conflict information, many improved combination methods have been developed through modifying the basic probability assignments (BPAs) of bodies of evidence (BOEs) by using a certain measure of the degree of conflict or uncertain information, such as Jousselme's distance, the pignistic probability distance and the ambiguity measure. However, if BOEs contain some non-singleton elements and the differences among their BPAs are larger than 0.5, the current conflict measure methods have limitations in describing the interrelationship among the conflict BOEs and may even lead to wrong combination results. In order to solve this problem, a new distance function, which is called supporting probability distance, is proposed to characterize the differences among BOEs. With the new distance, the information of how much a focal element is supported by the other focal elements in BOEs can be given. Also, a new combination rule based on the supporting probability distance is proposed for the combination of the conflicting evidences. The credibility and the discounting factor of each BOE are generated by the supporting probability distance and the weighted BOEs are combined directly using Dempster's rules. Analytical results of numerical examples show that the new distance has a better capability of describing the interrelationships among BOEs, especially for the highly conflicting BOEs containing non-singleton elements and the proposed new combination method has better applicability and effectiveness compared with the existing methods.

Dynamic Distance-Based Shape Features for Gait Recognition

We propose a novel skeleton-based approach to gait recognition using our Skeleton Variance Image. The core of our approach consists of employing the screened Poisson equation to construct a family of smooth distance functions associated with a given shape. The screened Poisson distance function approximation nicely absorbs and is relatively stable to shape boundary perturbations which allows us to define a rough shape skeleton. We demonstrate how our Skeleton Variance Image is a powerful gait cycle descriptor leading to a significant improvement over the existing state of the art gait recognition rate.

Impacts of vessel capacity reduction programs on the efficiency in fisheries. The case of Australia's multispecies in northern prawn fishery

Capacity reduction programs in the form of buybacks or decommissioning programs have had relatively widespread application in fisheries in the US, Europe and Australia. A common criticism of such programs is that they remove the least efficient vessels first, resulting in an increase in average efficiency of the remaining fleet. The effective fishing power of the fleet, therefore, does not decrease in proportion to the number of vessels removed. Further, reduced crowding may increase efficiency of the remaining vessels. In this paper, the effects of a buyback program on average technical efficiency in Australia’s Northern Prawn Fishery are examined using a multi-output distance function approach with an explicit inefficiency model. The results indicate that average efficiency of the remaining vessels was greater than that of the removed vessels, and that average efficiency of remaining vessels also increased as a result of reduced crowding.

Pre-crash and non-crash traffic flow trends analysis on motorways

Crashes that occur on motorways contribute to a significant proportion (40-50%) of non-recurrent motorway congestions. Hence, reducing the frequency of crashes assists in addressing congestion issues (Meyer, 2008). Crash likelihood estimation studies commonly focus on traffic conditions in a short time window around the time of a crash while longer-term pre-crash traffic flow trends are neglected. In this paper we will show, through data mining techniques that a relationship between pre-crash traffic flow patterns and crash occurrence on motorways exists. We will compare them with normal traffic trends and show this knowledge has the potential to improve the accuracy of existing models and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with crashes corresponding to traffic flow data using an incident detection algorithm. Traffic trends (traffic speed time series) revealed that crashes can be clustered with regards to the dominant traffic patterns prior to the crash. Using the K-Means clustering method with Euclidean distance function allowed the crashes to be clustered. Then, normal situation data was extracted based on the time distribution of crashes and were clustered to compare with the “high risk” clusters. Five major trends have been found in the clustering results for both high risk and normal conditions. The study discovered traffic regimes had differences in the speed trends. Based on these findings, crash likelihood estimation models can be fine-tuned based on the monitored traffic conditions with a sliding window of 30 minutes to increase accuracy of the results and minimize false alarms.

Impacts of vessel capacity reduction programmes on efficiency in fisheries : the case of Australia’s multispecies northern prawn fishery

Capacity reduction programmes, in the form of buybacks or decommissioning, have had relatively widespread application in fisheries in the US, Europe and Australia. A common criticism of such programmes is that they remove the least efficient vessels first, resulting in an increase in average efficiency of the remaining fleet, which tends to increase the effective fishing power of the remaining fleet. In this paper, the effects of a buyback programme on average technical efficiency in Australia’s Northern Prawn Fishery are examined using a multi-output production function approach with an explicit inefficiency model. As expected, the results indicate that average efficiency of the remaining vessels was generally greater than that of the removed vessels. Further, there was some evidence of an increase in average scale efficiency in the fleet as the remaining vessels were closer, on average, to the optimal scale. Key factors affecting technical efficiency included company structure and the number of vessels fishing. In regard to fleet size, our model suggests positive externalities associated with more boats fishing at any point in time (due to information sharing and reduced search costs), but also negative externalities due to crowding, with the latter effect dominating the former. Hence, the buyback resulted in a net increase in the individual efficiency of the remaining vessels due to reduced crowding, as well as raising average efficiency through removal of less efficient vessels.

The technical efficiency of the Japanese banks : non-radial directional performance measurement with undesirable output

The paper analyses technical efficiency of the Japanese banks from 2000 to 2007. The estimation technique is based on the Russell directional distance function that takes into consideration not only desirable outputs but also an undesirable output that is represented by non-performing loans (NPLs). The results indicate that NPLs remain a significant burden as for banks' performance. We show that banks' inputs have to be utilised more efficiently, particularly labour and premises. We also argue that a further restructuring process is needed in the segment of Regional Banks. We conclude that the Japanese banking system is still far away from being fully consolidated and restructured.

Modal choice between air and rail : a social efficiency benchmarking analysis that considers CO2 emissions

The problem of modal choice between rail and air arises as public awareness of carbon dioxide (CO2) emissions by the transportation sector rises. In this paper, we answer this question quantitatively by performing an efficiency benchmarking analysis that takes into account life-cycle CO2 emission due to transport service provision. The paper employs nonparametric efficiency estimation methods, namely a slacks-based inefficiency measure, as well as a more conventional directional distance function approach. We apply them to a panel data set for three major railway companies and the aviation sector in Japan for the period from 1999 to 2007. Results shows that, contrary to the common argument, air transport can still be more socially efficient than rail transport, even when the environmental load due to CO2 emission is incorporated. This is due to the aviation sector's extremely low user cost, measured in terms of in-vehicle time. In other words, aviation is a necessary transportation mode for those with a very high willingness to pay for their time.

Changes in environmentally sensitive productivity and technological modernization in China's iron and steel industry in the 1990s

Technological modernization is widely believed to contribute positively both to economic development and to environmental and resource conservation, through improvements in productivity and strengthening of business competitiveness. However, this may not always be true, particularly in the short term, as it requires substantial investments and may impose financial burdens on firms undertaking such investments. This study empirically examines the effects of technological modernization in China's iron and steel industry in the 1990s on conventional economic productivity (CEP) and environmentally sensitive productivities (ESPs). We employ a directional distance function that can handle multiple inputs and outputs to compute relative production efficiencies. We apply these models to the data covering 27 iron and steel firms in China between 1990 and 1999-a period when the Chinese iron and steel industry modernized rapidly. We find that ESPs have continuously improved, even in the period when the CEP declined.

Sulfur dioxide allowances : trading and technological progress

The US Clean Air Act Amendments introduce an emissions trading system to regulate SO2 emissions. This study finds that changes in SO2 emissions prices are related to innovations induced by these amendments. We find that electricity-generating plants are able to increase electricity output and reduce emissions of SO2 and NOx from 1995 to 2007 due to the introduction of the allowance trading system. However, compared to the approximate 8% per year of exogenous technological progress, the induced effect is relatively small, and the contribution of the induced effect to overall technological progress is about 1-2%.

Energy price-induced and exogenous technological change : Assessing the economic and environmental outcomes

In this paper, we distinguish between factor/output substitution and shifts in the production technology frontier. Our model includes the by-products of carbon dioxide and sulfur dioxide emissions where the function requires the simultaneous expansion of good outputs and reductions in emissions. We estimate a directional output distance function for 80 countries over the period 1971-2000 to measure the exogenous and oil price-induced technological change. On average, we find substantial oil price-induced technological progress at the world level when long-term oil prices are rising, although the growth rate is more volatile in developed countries than in developing countries. The results also show that developed countries experience higher exogenous technological progress in comparison with developing countries, and the gap between the two has increased during the period of our study.