Non-Parametric Tests for Firm Efficiency in Case of Errors-in-Variables

This paper develops a novel statistic for firm efficiency called efficiency depth thatallows for statistical inference in case of errors-in-variables. We derive statistical teststhat require minimal statistical assumptions; neither the sample distribution nor thenoise level is required. An empirical illustration for European banks illustrates that -despite the minimal assumptions- the tests can have substantial discriminating powerin practical applications.errors-in-variables;firm efficiency;nonparametric analysis

Non-parametric production analysis under alternative price conditions.

The literature on non-parametric production analysis has formulated tests for profit maximizing behavior that do not require a parametric specification of technology. Negative test results have conventionally been interpreted as inefficiency, or have been attributed to data perturbations. In this paper, we exploit the possibility that negative test results reveal violations of the underlying neoclassical assumption that prices are exogenously fixed and perfectly certain. We propose non-parametric tests that do allow for endogenous price formation and price uncertainty. In addition, we investigate how to recover the technology and how to forecast behavior in new economic situations.Non-parametric production analysis; Endogenous price formation; Price uncertainty;

New tools for dealing with errors-in-variables in DEA.

Errors in variables; Tool;

The effect of visual detail on cybersickness:predicting symptom severity using spatial velocity

Abstract. In this work, we examine the effect of visual realism on the severity of cybersickness symptoms experienced by users of virtual environments. We also seek to validate a metric called spatial velocity as a predictor of cybersickness. The proposed metric combines the visual complexity of a virtual scene with the amount of movement within the scene. To achieve this, we prepared two virtual scenes depicting the same environment with a variable level of detail. We recruited volunteers who were exposed to both scenes in two separate sessions. We obtained the sickness ratings after both sessions and saved the data required for spatial velocity calculations. After comparing the sickness ratings between the two scenes, we found no evidence of the visual realism playing any significant role in the generation of cybersickness symptoms. The spatial velocity also proved inadequate in characterizing the difference in visual complexity and correlated poorly with all the observed sickness scores.Visuaalisen yksityiskohtaisuuden vaikutus VR-pahoinvointiin : oireiden vakavuuden ennustaminen käyttäen SV-metriikkaa. Tiivistelmä. Tässä työssä tutkimme sitä, millainen vaikutus virtuaalisten ympäristöjen graafisella yksityiskohtaisuudella on VR-pahoinvointiin. Pyrimme myös validoimaan "spatial velocity" -nimisen mittasuureen kyvyn ennustaa VR-pahoinvoinnin oireiden vakavuutta. Kyseisen mittasuureen etuna on, että se yhdistää visuaalisen kompleksisuuden ja ympäristössä koetun liikkeen yhdeksi suureeksi. Tutkimusta varten valmistimme kaksi virtuaaliympäristöä, joissa mallinnettiin Oulun yliopiston kampusaluetta. Toinen ympäristö pyrki mahdollisimman realistiseen esitystapaan, kun taas toisessa yksityiskohtien määrä minimoitiin. Koetta varten värväsimme 18 vapaaehtoista. Vapaaehtoiset altistettiin kummallekin ympäristölle kahdessa noin kymmenen minuutin mittaisessa kokeessa. Vapaaehtoisten kokeman VR-pahoinvoinnin vakavuutta arvioitiin kunkin kokeen jälkeen täytetyillä kyselylomakkeilla. Kokeiden aikana tallensimme myös SV laskentaan tarvittavat tiedot. Verrattuamme koeolosuhteiden tuloksia, emme löytäneet todisteita siitä, että ympäristön graafisten yksityiskohtien määrällä olisi merkittävää vaikutusta koettuun pahoinvointiin. Käytetty SV metriikka ei myöskään kyennyt erottelemaan ympäristöjä oletetulla tavalla, eivätkä lasketut arvot korreloineet merkittävästi minkään mitatun pahoinvointisuureen kanssa

Non-Parametric Tests for Firm Efficiency in Case of Errors-in-Variables

This paper develops a novel statistic for firm efficiency called efficiency depth that allows for statistical inference in case of errors-in-variables. We derive statistical tests that require minimal statistical assumptions; neither the sample distribution nor the noise level is required. An empirical illustration for European banks illustrates that - despite the minimal assumptions- the tests can have substantial discriminating power in practical applications

Testing for Productive Efficiency with Errors-in-Variables: with an application to the Dutch electricity sesctor

We develop a nonparametric test of productive efficiency that accounts for the possibility of errors-in-variables. The test allows for statistical inference based on the extreme value distribution of the L?? norm. In contrast to the test proposed by Varian, H (1985): 'Nonparametric Analysis of Optimising Behaviour with Measurement Error, Journal of Econometrics 30, 445-458, our test can be computed using simple enumeration algorithms or linear programming. An empirical application for the Dutch electricity sector illustrates the proposed test procedure

Representation theorem for convex nonparametric least squares

We examine a nonparametric least-squares regression model that endogenously selects the functional form of the regression function from the family of continuous, monotonic increasing and globally concave functions that can be nondifferentiable. We show that this family of functions can be characterized without a loss of generality by a subset of continuous, piece-wise linear functions whose intercept and slope coefficients are constrained to satisfy the required monotonicity and concavity conditions. This representation theorem is useful at least in three respects. First, it enables us to derive an explicit representation for the regression function, which can be used for assessing marginal properties and for the purposes of forecasting and ex post economic modelling. Second, it enables us to transform the infinite dimensional regression problem into a tractable quadratic programming (QP) form, which can be solved by standard QP algorithms and solver software. Importantly, the QP formulation applies to the general multiple regression setting. Third, an operational computational procedure enables us to apply bootstrap techniques to draw statistical inference

Computing the output distribution and selection probabilities of a stack filter from the DNF of its positive Boolean function

Many nonlinear filters used in practise are stack filters. An algorithm is presented which calculates the output distribution of an arbitrary stack filter S from the disjunctive normal form (DNF) of its underlying positive Boolean function. The so called selection probabilities can be computed along the way.Comment: This is the version published in Journal of Mathematical Imaging and Vision, online first, 1 august 201