A Monte Carlo EM Algorithm for the Estimation of a Logistic Auto-logistic Model with Missing Data

This paper proposes an algorithm for the estimation of the parameters of a Logistic Auto-logistic Model when some values of the target variable are missing at random but the auxiliary information is known for the same areas. First, we derive a Monte Carlo EM algorithm in the setup of maximum pseudo-likelihood estimation; given the analytical intractability of the conditional expectation of the complete pseudo-likelihood function, we implement the E-step by means of Monte Carlo simulation. Second, we give an example using a simulated dataset. Finally, a comparison with the standard non-missing data case shows that the algorithm gives consistent results.Spatial Missing Data, Monte Carlo EM Algorithm, Logistic Auto-logistic Model, Pseudo-Likelihood.

Spatial models for flood risk assessment

The problem of computing risk measures associated to flood events is extremely important not only from the point of view of civil protection systems but also because of the necessity for the municipalities of insuring against the damages. In this work we propose, in the framework of an integrated strategy, an operating solution which merges in a conditional approach the information usually available in this setup. First we use a Logistic Auto-Logistic (LAM) model for the estimation of the univariate conditional probabilities of flood events. This approach has two fundamental advantages: it allows to incorporate auxiliary information and does not require the target variables to be independent. Then we simulate the joint distribution of floodings by means of the Gibbs Sampler. Finally we propose an algorithm to increase ex post the spatial autocorrelation of the simulated events. The methodology is shown to be effective by means of an application to the estimation of the flood probability of Italian hydrographic regions

Spatial models for flood risk assessment

The problem of computing risk measures associated to flood events is extremely important not only from the point of view of civil protection systems but also because of the necessity for the municipalities of insuring against the damages. In this work we propose, in the framework of an integrated strategy, an operating solution which merges in a conditional approach the information usually available in this setup. First we use a Logistic Auto-Logistic (LAM) model for the estimation of the univariate conditional probabilities of flood events. This approach has two fundamental advantages: it allows to incorporate auxiliary information and does not require the target variables to be indepen- dent. Then we simulate the joint distribution of floodings by means of the Gibbs Sampler. Finally we propose an algorithm to increase ex post the spatial autocorrelation of the simulated events. The methodology is shown to be effective by means of an application to the estimation of the flood probability of Italian hydrographic regions.Flood Risk, Conditional Approach, LAM Model, Pseudo-Maximum Likelihood Estimation, Spatial Autocorrelation, Gibbs Sampler.

A note on maximum likelihood estimation of a Pareto mixture

In this paper we study Maximum Likelihood Estimation of the parameters of a Pareto mixture. Application of standard techniques to a mixture of Pareto is problematic. For this reason we develop two alternative algorithms. The first one is the Simulated Annealing and the second one is based on Cross-Entropy minimization. The Pareto distribution is a commonly used model for heavy-tailed data. It is a two-parameter distribution whose shape parameter determines the degree of heaviness of the tail, so that it can be adapted to data with different features. This work is motivated by an application in the operational risk measurement field: we fit a Pareto mixture to operational losses recorded by a bank in two different business lines. Losses below an unknown threshold are discarded, so that the observed data are truncated. The thresholds used in the two business lines are unknown. Thus, under the assumption that each population follows a Pareto distribution, the appropriate model is a mixture of Pareto where all the parameters have to be estimated.

A framework for cut-off sampling in business survey design

In sampling theory the large concentration of the population with respect to most surveyed variables constitutes a problem which is difficult to tackle by means of classical tools. One possible solution is given by cut-off sampling, which explicitly prescribes to discard part of the population; in particular, if the population is composed by firms or establishments, the method results in the exclusion of the “smallest” firms. Whereas this sampling scheme is common among practitioners, its theoretical foundations tend to be considered weak, because the inclusion probability of some units is equal to zero. In this paper we propose a framework to justify cut-off sampling and to determine the census and cut-off thresholds. We use an estimation model which assumes as known the weight of the discarded units with respect to each variable; we compute the variance of the estimator and its bias, which is caused by violations of the aforementioned hypothesis. We develop an algorithm which minimizes the MSE as a function of multivariate auxiliary information at the population level. Considering the combinatorial optimization nature of the model, we resort to the theory of stochastic relaxation: in particular, we use the simulated annealing algorithm.Cut-off sampling, skewed populations, model-based estimation, optimal stratification, simulated annealing

Testing for Asymmetries and Anisotropies in Regional Economic Models

This paper develops a new methodology for estimating and testing the form of anisotropy of homogeneous spatial processes. We derive a generalised version of the isotropy test proposed by Arbia, Bee and Espa (2013) and analyse its properties in various settings. In light of this, we propose a new approach that allows one to estimate and test under mild conditions any form of anisotropy in homogeneous spatial processes. The power of the test is studied by means of Monte Carlo simulations performed both on regularly and irregularly spaced data. Finally, the method is used to analyse the soybeans yields in the US

A Cross-Entropy Approach to the Estimation of Generalised Linear Multilevel Models

In this paper we use the cross-entropy method for noisy optimisation for fitting generalised linear multilevel models through maximum likelihood. We propose specifications of the instrumental distributions for positive and bounded parameters that improve the computational performance. We also introduce a new stopping criterion, which has the advantage of being problem-independent. In a second step we find, by means of extensive Monte Carlo experiments, the most suitable values of the input parameters of the algorithm. Finally, we compare the method to benchmark estimation technique based on numerical integration. The cross-entropy approach turns out to be preferable from both the statistical and the computational point of view. In the last part of the paper, the method is used to model death probability of firms in the healthcare industry in Italy

Investigations on Transgenerational Epigenetic Response Down the Male Line in F2 Pigs

We investigated the nutritional effects on carcass traits, gene expression and DNA methylation in a three generation Large White pig feeding experiment. A group of experimental (E) F0 boars were fed a standard diet supplemented with high amounts of methylating micronutrients whereas a control group (C) of F0 boars received a standard diet. These differentially fed F0 boars sired F1 boars which then sired 60 F2 pigs. Carcass traits were compared between 36 F2 descendants of E F0 boars and 24 F2 descendants of C F0 boars. The two F2 offspring groups differed with respect to backfat percentage (P = 0.03) and tended to differ with respect to adipose tissue (P = 0.09), fat thickness at the 10th rib (P = 0.08) and at the croup (P = 0.09) as well as percentages of shoulder (P = 0.07). Offspring from the experimental F0 boars had a higher percentage of shoulder and were leaner compared to the control group. Gene expression profiles showed significant twofold differences in mRNA level between 8 C F2 offspring and 8 E F2 offspring for 79, 64 and 53 genes for muscle, liver and kidney RNA, respectively. We found that in liver and muscle respective pathways of lipid metabolism and metabolic pathway were over-represented for the differentially expressed genes between these groups. A DNA methylation analysis in promoters of differentially expressed genes indicated a significant difference in DNA methylation at the IYD gene. If these responses on carcass traits, gene expression and DNA methylation withstand verification and can indeed be attributed to transgenerational epigenetic inheritance, it would open up pioneering application in pork production and would have implications for human health

Impact of increasing levels of condensed tannins from sainfoin in the grower-finisher diets of entire male pigs on growth performance, carcass characteristics, and meat quality.

Sainfoin is a protein-rich legume with an ideal amino acid profile and therefore could partly replace soybeans in the diets of growing pigs. However, sainfoin also contains a non-negligible amount of condensed tannins (CTs), which can act as antinutritional factors. Bioactive plant compounds, like hydrolysable tannins, have been suggested to be suitable in entire male (EM) production, as they impair the development of accessory sex glands and, by that, reduce boar taint compound levels without negatively impacting growth. It is unknown whether, similar to hydrolysable tannins, CTs from sainfoin reduce the incidence of boar taint without impacting growth performance, carcass traits, and meat quality. For the experiment, 48 Swiss Large White EM were assigned within litter to one of four grower (25-60 kg BW) and finisher (60-105 kg BW) diets supplemented with 0 (T0), 5 (T5), 10 (T10), and 15% (T15) sainfoin meal, respectively. The four diets were designed to be isocaloric and isoproteic. Increasing the dietary sainfoin level had no negative effect on growth performance or the carcass characteristics. Despite leading to a similar feed intake between the treatment groups, increasing the dietary sainfoin levels tended (P ≤ 0.08) to reduce the number of feeder visits but increased the time spent at the feeder as well as the feed intake per visit during the finisher period. By increasing sainfoin intake, the levels of C18:3n-3 and long-chain homologs linearly increased (P < 0.01) in the backfat and intramuscular fat (IMF), whereas in the backfat, but not the IMF, the 18:2n-6 levels decreased (P < 0.01). The latter triggered a greater (P < 0.01) desaturation rate (C18:1n-9/C18:0) of the saturated fatty acids, resulting in a greater (P < 0.01) proportion of monounsaturated fatty acid. Apart from a linear decrease (P = 0.02) in the androstenone levels in the longissimus thoracis (LT), increasing the sainfoin intake had no effect on the level of boar taint in the LT and backfat. As determined by the elevated correlation coefficient, skatole and indole levels, but not androstenone levels, in the adipose tissue seem to be reliable proxies for their respective levels in LT and, therefore, in pork. In conclusion, sainfoin is a suitable homegrown protein source for grower finisher pigs and can be included at up to 15% in the diet to replace 7% of soybean in a diet without producing any noteworthy effects on growth, whereas the impact of CTs on boar taint was limited