The matching problem between functional shapes via a BV-penalty term: a Γ\Gamma-convergence result

In this paper we study a variant of the matching model between functional shapes introduced in \cite{ABN}. Such a model allows to compare surfaces equipped with a signal and the matching energy is defined by the $L^2$-norm of the signal on the surface and a varifold-type attachment term. In this work we study the problem with fixed geometry which means that we optimize the initial signal (supported on the initial surface) with respect to a target signal supported on a different surface. In particular, we consider a $BV$ or $H^1$-penalty for the signal instead of its $L^2$-norm. Several numerical examples are shown in order to prove that the $BV$-penalty improves the quality of the matching. Moreover, we prove a $\Gamma$-convergence result for the discrete matching energy towards the continuous-one

The Apparent Fractal Conjecture

This short communication advances the hypothesis that the observed fractal structure of large-scale distribution of galaxies is due to a geometrical effect, which arises when observational quantities relevant for the characterization of a cosmological fractal structure are calculated along the past light cone. If this hypothesis proves, even partially, correct, most, if not all, objections raised against fractals in cosmology may be solved. For instance, under this view the standard cosmology has zero average density, as predicted by an infinite fractal structure, with, at the same time, the cosmological principle remaining valid. The theoretical results which suggest this conjecture are reviewed, as well as possible ways of checking its validity.Comment: 6 pages, LaTeX. Text unchanged. Two references corrected. Contributed paper presented at the "South Africa Relativistic Cosmology Conference in Honour of George F. R. Ellis 60th Birthday"; University of Cape Town, February 1-5, 199

An Analysis of Housing Expenditure Using Semiparametric Cross-Section Models

In this paper we model expenditure on housing for owners and renters by means of endogenous switching regression models using cross-section data. We explain the share of housing in total expenditure from family characteristics and total expenditure, where the latter is allowed to be endogenous. We apply various existing parametric and semiparametric techniques for cross-section data. Exogeneity of total expenditure is rejected for the parametric models but not for most semiparametric models. The results are compared on the basis of graphs of the estimated relationship between the budget share spent on housing and the logarithm of total expenditure and on the baisi of budget elasticities.sample selection;Engel curves;semiparametric cross-section models

Estimation of a censored regression panel data model using conditional moment restrictions efficiently

A new semiparametric estimator for the censored regression panel data model with fixed effects is introduced. It is based upon an estimator proposed by Honore for the case of two time periods combined with ideas of Newey to improve the efficiency. The estimator is more efficient than Honore's and is generalized to the case of a balanced or unbalanced panel of more than two waves. Estimation is performed in two steps. Using Honore's estimator in a first step, efficient GMM using conditional moment restrictions is applied. The performance of this estimator is compared to that of Honore's and other existing estimators in an empirical example concerning labour income of married females, using panel data from the Dutch Socio-Economic Panel, 1984-1988. Attention is paid to specification testing and the sensitivity of the results for the choice of smoothness parameters.Estimation;Panel Data;statistics

An Analysis of Housing Expenditure Using Semiparametric Models and Panel Data

In this paper we model expenditure on housing for owners and renters by means of endogenous switching regression models for panel data. We explain the share of housing in total expenditure from a household specific effect, family characteristics and total expenditure, where the latter is allowed to be endogenous. We consider both random and fixed effects panel data models. We compare estimates for the random effects model with estimates for the linear panel data model in which selection only enters through the fixed effects and with estimates allowing for fixed effects and a more general type of selectivity. Differences appear to be substantial. The results imply that the random effects model as well as the linear panel data model are too restrictive.sample selection;Engel curves;semiparametric models;panel data

Novel Insights in the faecal egg count reduction test for monitoring drug efficacy against gastrointestinal nematodes of veterinary importance

The faecal egg count reduction test (FECRT) is the method of choice to monitor anthelmintic efficacy against gastro-intestinal nematodes in livestock. Guidelines on how to conduct a FECRT are made available by the World Association for the Advancement of Veterinary Parasitology (WAAVP). Since the publication of these guidelines in the early 1990s, some limitations have been noted, including (i) the ignorance of host-parasite interactions that depend on animal and parasite species, (ii) their feasibility under field conditions, (iii) appropriateness of study design, and (iv) the low analytic sensitivity of the recommended faecal egg count (FEC) method. Therefore, the objective of the present study was to empirically assess the impact of the level of excretion and aggregation of FEC, sample size and detection limit of the FEC method on the sensitivity and specificity of the FECRT to detect reduced efficacy (<90% or <95%) and to develop recommendations for surveys on anthelmintic resistance. A simulation study was performed in which the FECRT (based on the arithmetic mean of grouped FEC of the same animals before and after drug administration) was conducted under varying conditions of mean FEC, aggregation of FEC (inversely correlated with k), sample size, detection limit and ‘true’ drug efficacies. Classification trees were built to explore the impact of the above factors on the sensitivity and specificity of detecting a truly reduced efficacy. For a reduced-efficacy threshold of 90%, most combinations resulted in a reliable detection of reduced and normal efficacy. For the reduced-efficacy threshold of 95% however, unreliable FECRT results were found when sample sizes <15 were combined with highly aggregated FEC (k = 0.25) and detection limits ≥5 EPG or when combined with detection limits ≥15 EPG. Overall, an increase in sample size and mean preDA FEC, and a decrease in detection limit improved the diagnostic accuracy. FECRT remained inconclusive under any evaluated condition for drug efficacies ranging from 87.5% to 92.5% for a reduced-efficacy-threshold of 90% and from 92.5% to 97.5% for a threshold of 95%. The results highlight that (i) the interpretation of this FECRT is affected by a complex interplay of factors, including the level of excretion and aggregation of FEC and (ii) the diagnostic value of FECRT to detect small reductions in efficacy is limited. This study, therefore, provides a framework allowing researchers to adapt their study design according to a wide range of field conditions, while ensuring a good diagnostic performance of the FECRT

An Analysis of Housing Expenditure Using Semiparametric Models and Panel Data

In this paper we model expenditure on housing for owners and renters by means of endogenous switching regression models for panel data. We explain the share of housing in total expenditure from a household specific effect, family characteristics and total expenditure, where the latter is allowed to be endogenous. We consider both random and fixed effects panel data models. We compare estimates for the random effects model with estimates for the linear panel data model in which selection only enters through the fixed effects and with estimates allowing for fixed effects and a more general type of selectivity. Differences appear to be substantial. The results imply that the random effects model as well as the linear panel data model are too restrictive

An Analysis of Housing Expenditure Using Semiparametric Cross-Section Models

In this paper we model expenditure on housing for owners and renters by means of endogenous switching regression models using cross-section data. We explain the share of housing in total expenditure from family characteristics and total expenditure, where the latter is allowed to be endogenous. We apply various existing parametric and semiparametric techniques for cross-section data. Exogeneity of total expenditure is rejected for the parametric models but not for most semiparametric models. The results are compared on the basis of graphs of the estimated relationship between the budget share spent on housing and the logarithm of total expenditure and on the baisi of budget elasticities.