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The purpose of this paper is to demonstrate improvement of the accuracy of electrical tomography reconstruction by incorporation of a priori knowledge into the inverse problem solution.

The fusion of two different inversion algorithms capable of real‐time operation is discussed, namely a non‐iterative monotonicity‐based approach, determining the a priori knowledge and an iterative Gauss‐Newton (GN)‐based reconstruction algorithm. Furthermore, the method is compared with the unmodified algorithms themselves by means of reconstructions from simulated inclusions at different noise levels.

The accuracy of the inverse problem reconstructions, especially at the boundary regions of the unknown inclusions, benefit from the investigations of incorporating a priori knowledge about material values and can be considerable improved. The monotonicity method itself, which has low complexity, provides remarkable reconstruction results in electrical tomography.

The paper is applied to simulated discrete two‐phase scenarios, e.g. gas/oil mixtures. In a further step the method would be tested with measured data. Moreover, investigations have to be carried out in order to make the monotonicity‐based reconstruction principle more robust against disturbing artifacts.

The fusion of the non‐iterative monotonicity‐based method with the GN‐based algorithm demonstrates a novel approach of improving the reconstruction accuracy in electrical tomography.

The aim of this study is to investigate for monotonicity, linearity and symmetry for the price volatility–trading volume relationship in the futures markets of agricultural commodities.

Empirical findings are produced with the use of a highly flexible, nonparametric approach. Data are daily prices and volumes from the commodities of corn, hard red wheat, oats, rice and soybeans.

Results reveal violations of monotonicity locally but not globally. Volume and price volatility have, in all markets, a nonlinear relationship to each other, indicating that the strength of the relationship does not remain constant over the entire joint distribution. Global symmetry is rejected for the markets of oats and hard red wheat but cannot be rejected for the remaining three markets. The latter suggests that large values of good volatility are likely to occur together with high trading volumes, as do large values of bad volatility in these markets.

To the best of the authors’ knowledge, this is the first empirical work to test simultaneously for monotonicity, linearity and symmetry between price volatility and trading volume in the futures markets of agricultural commodities.

This paper proposes the use of a gauge function as a measure of technical efficiency. The measure of technical inefficiency from a gauge function is desirable as the estimation of a gauge function is not subject to the endogeneity problem under the behavioral assumption of profit maximization in the competitive market.

The authors address three important properties of a gauge function, i.e. linear homogeneity, monotonicity and convexity in inputs and outputs, and show how such properties are utilized in its estimation. Then, the authors apply the estimation of a gauge function to US Blacksmiths in 1850 and 1880 to show that a failure to satisfy such properties may lead to an incorrect inference on the technical efficiency.

The authors find that the Blacksmiths in the 1850s were technically more efficient than the ones in the 1880s, indicating technical regress in Blacksmithing when the properties are satisfied.

This paper introduces a measure of technical inefficiency from a gauge function and shows how to estimate the gauge function parametrically for the measure. The authors show McFadden's gauge function and its properties, which differ from the properties of other distance functions. The authors emphasize linear homogeneity as well as monotonicity and convexity in inputs and outputs, which must be satisfied in the estimation of a gauge function.

This paper aims to investigate the contemporaneous link between price volatility and trading volume in the futures markets of energy.

Non-parametric (local linear) regression models and formal statistical tests are used to assess monotonicity, linearity and symmetry. The data are daily price and volumes from five futures markets (West Texas Intermediate, Brent, gasoline, heating oil and natural gas) in the USA.

Trading volume and price volatility have, in all markets, a strong nonlinear relation to each other. There are violations of monotonicity locally but not globally. The qualitative nature of the price shocks may have implications for the trading activity locally.

To the authors’ best knowledge, this is the first manuscript that investigates simultaneously and formally all the three important issues (i.e. monotonicity, linearity and asymmetry) for the price volatility–volume relationship using a highly flexible nonparametric approach.

This paper aims to investigate the usability of the non-iterative monotonicity approach for electrical capacitance tomography (ECT)-based object detection. This is of particular importance with respect to object detection in robotic applications.

With respect to the detection problem, the authors propose a precomputed threshold value for the exclusion test to speed up the algorithm. Furthermore, they show that the use of an inhomogeneous split-up strategy of the region of interest (ROI) improves the performance of the object detection.

The proposed split-up strategy enables to use the monotonicity approach for robotic applications, where the spatial placement of the electrodes is constrained to a planar geometry. Additionally, owing to the improvements in the exclusion tests, the selection of subregions in the ROI allows for avoiding self-detection. Furthermore, the computational costs of the algorithm are reduced owing to the use of a predefined threshold, while the detection capabilities are not significantly influenced.

The presented simulation results show that the adapted split-up strategies for the ROI improve significantly the detection performance in comparison to the traditional ROI split-up strategy. Thus, the monotonicity approach becomes applicable for ECT-based object detection for applications, where only a reduced number of electrodes with constrained spatial placement can be used, such as in robotics.

Economic conditions such as convexity, homogeneity, homotheticity, and monotonicity are all important assumptions or consequences of assumptions of economic functionals to be estimated. Recent research has seen a renewed interest in imposing constraints in nonparametric regression. We survey the available methods in the literature, discuss the challenges that present themselves when empirically implementing these methods, and extend an existing method to handle general nonlinear constraints. A heuristic discussion on the empirical implementation for methods that use sequential quadratic programming is provided for the reader, and simulated and empirical evidence on the distinction between constrained and unconstrained nonparametric regression surfaces is covered.

In the unidimensional poverty field, a number of axioms capture the distribution sensitivity among the poor. One of them is the monotonicity sensitivity axiom that demands that a poverty measure should be more sensitive to a reduction in the income of a poor person, the poorer that person is. On the other hand, the minimal transfer axiom requires poverty to decrease when a transfer of income is made from a poor person to a poorer one. These axioms turn out to be identical, but they provide different and interesting interpretations. Both of them rely deeply on the income-ranking of the poor.

Some generalizations of the minimal transfer axiom and its variations have been proposed in the multidimensional framework. In none of them the partial ordering of the poor is taken into account. No counterpart of the monotonicity sensitivity axiom exists.

This note introduces multidimensional generalizations of the two mentioned axioms, keeping the crucial assumption that only when the poor involved are unambiguously ranked are the axioms uncontroversial. We show that the two generalizations proposed are also identical in the multidimensional setting although offering different interpretations. Relationships between the new properties and those existing in the literature are analyzed.

The purpose of this paper is to expand the peer effect analysis to investments in the stock market, where neither direct competition nor interaction with other investors exists.

A total of 772 subjects dwelling in six countries completed a questionnaire about their satisfaction with the performance of their hypothetical investment in the stock market. They were informed about the performance of the local stock market and the performance of their peer group, referred to in the questionnaire as their “friends.”

Only 5 per cent of subjects are indifferent to their friends’ investment performance, as advocates by expected utility paradigm. Most subjects are happier when their friends earn lower rather than higher returns. On average, subjects are better off losing rather than gaining money as long as their friends lose more money, which violates the univariate monotonicity axiom. A negligible number of subjects exhibit a consistent favorable response, which is a necessary condition for pure economic altruism. Hostility is greater in less-wealthy countries. No link is found with regard to economic inequality.

This paper shows that when a conflict between absolute wealth and relative wealth arises, the latter dominates, even when the comparison is not with an opponent or a colleague but with the subject’s friends. The astonishing result is that subjects prefer having less wealth as long as their friends lose more, despite no direct competition between subjects as in ultimatum games and despite the performance being equal to market performance.