A Gaussian-mixture ensemble transform filter

We generalize the popular ensemble Kalman filter to an ensemble transform filter, in which the prior distribution can take the form of a Gaussian mixture or a Gaussian kernel density estimator. The design of the filter is based on a continuous formulation of the Bayesian filter analysis step. We call the new filter algorithm the ensemble Gaussian-mixture filter (EGMF). The EGMF is implemented for three simple test problems (Brownian dynamics in one dimension, Langevin dynamics in two dimensions and the three-dimensional Lorenz-63 model). It is demonstrated that the EGMF is capable of tracking systems with non-Gaussian uni- and multimodal ensemble distributions. Copyright © 2011 Royal Meteorological Society

A fast algorithm for sparse probability density function construction

A new sparse kernel density estimator is introduced. Our main contribution is to develop a recursive algorithm for the selection of significant kernels one at time using the minimum integrated square error (MISE) criterion for both kernel selection. The proposed approach is simple to implement and the associated computational cost is very low. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with competitive accuracy to existing kernel density estimators.

Linear and non-linear transmission of equity return volatility: evidence from the US, Japan and Australia

This paper models the transmission of shocks between the US, Japanese and Australian equity markets. Tests for the existence of linear and non-linear transmission of volatility across the markets are performed using parametric and non-parametric techniques. In particular the size and sign of return innovations are important factors in determining the degree of spillovers in volatility. It is found that a multivariate asymmetric GARCH formulation can explain almost all of the non-linear causality between markets. These results have important implications for the construction of models and forecasts of international equity returns.

Interaction between allelic variations in vitamin D receptor and retinoid X receptor genes on metabolic traits

BACKGROUND: Low vitamin D status has been shown to be a risk factor for several metabolic traits such as obesity, diabetes and cardiovascular disease. The biological actions of 1, 25-dihydroxyvitamin D, are mediated through the vitamin D receptor (VDR), which heterodimerizes with retinoid X receptor, gamma (RXRG). Hence, we examined the potential interactions between the tagging polymorphisms in the VDR (22 tag SNPs) and RXRG (23 tag SNPs) genes on metabolic outcomes such as body mass index, waist circumference, waist-hip ratio (WHR), high- and low-density lipoprotein (LDL) cholesterols, serum triglycerides, systolic and diastolic blood pressures and glycated haemoglobin in the 1958 British Birth Cohort (1958BC, up to n = 5,231). We used Multifactor- dimensionality reduction (MDR) program as a non-parametric test to examine for potential interactions between the VDR and RXRG gene polymorphisms in the 1958BC. We used the data from Northern Finland Birth Cohort 1966 (NFBC66, up to n = 5,316) and Twins UK (up to n = 3,943) to replicate our initial findings from 1958BC. RESULTS: After Bonferroni correction, the joint-likelihood ratio test suggested interactions on serum triglycerides (4 SNP - SNP pairs), LDL cholesterol (2 SNP - SNP pairs) and WHR (1 SNP - SNP pair) in the 1958BC. MDR permutation model testing analysis showed one two-way and one three-way interaction to be statistically significant on serum triglycerides in the 1958BC. In meta-analysis of results from two replication cohorts (NFBC66 and Twins UK, total n = 8,183), none of the interactions remained after correction for multiple testing (Pinteraction >0.17). CONCLUSIONS: Our results did not provide strong evidence for interactions between allelic variations in VDR and RXRG genes on metabolic outcomes; however, further replication studies on large samples are needed to confirm our findings.

Sparse density estimation on the multinomial manifold

A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion for the finite mixture model. Since the constraint on the mixing coefficients of the finite mixture model is on the multinomial manifold, we use the well-known Riemannian trust-region (RTR) algorithm for solving this problem. The first- and second-order Riemannian geometry of the multinomial manifold are derived and utilized in the RTR algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with an accuracy competitive with those of existing kernel density estimators.

Sparse density estimation on multinomial manifold combining local component analysis

A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion combining local component analysis for the finite mixture model. We start with a Parzen window estimator which has the Gaussian kernels with a common covariance matrix, the local component analysis is initially applied to find the covariance matrix using expectation maximization algorithm. Since the constraint on the mixing coefficients of a finite mixture model is on the multinomial manifold, we then use the well-known Riemannian trust-region algorithm to find the set of sparse mixing coefficients. The first and second order Riemannian geometry of the multinomial manifold are utilized in the Riemannian trust-region algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with competitive accuracy to existing kernel density estimators.

Application of a double bootstrap to investigation of determinants of technical efficiency of farms in Central Europe

The paper provides one of the first applications of the double bootstrap procedure (Simar and Wilson 2007) in a two-stage estimation of the effect of environmental variables on non-parametric estimates of technical efficiency. This procedure enables consistent inference within models explaining efficiency scores, while simultaneously producing standard errors and confidence intervals for these efficiency scores. The application is to 88 livestock and 256 crop farms in the Czech Republic, split into individual and corporate.

Application of a double bootstrap to investigation of determinants of technical efficiency of farms in Central Europe

The paper provides one of the first applications of the double bootstrap procedure (Simar and Wilson 2007) in a two-stage estimation of the effect of environmental variables on non-parametric estimates of technical efficiency. This procedure enables consistent inference within models explaining efficiency scores, while simultaneously producing standard errors and confidence intervals for these efficiency scores. The application is to 88 livestock and 256 crop farms in the Czech Republic, split into individual and corporate.

Some factors affecting reproductive success in crossbred dairy cows on smallholder farms in coastal north-east Tanzania

A 2-year longitudinal survey was carried out to investigate factors affecting reproduction in crossbred cows on smallholder farms in and around an urban centre. Sixty farms were visited at approximately 2-week intervals and details of reproductive traits and body condition score (BCS) were collected. Fifteen farms were within the town (U), 23 farms were approximately 5 km from town (SU), and 22 farms approximately 10 km from town (PU). Sources of variation in reproductive traits were investigated using a general linear model (GLM) by a stepwise forward selection and backward elimination approach to judge important independent variables. Factors considered for the first step of formulation of the model included location (PU, SU and U), type of insemination, calving season, BCS at calving, at 3 months postpartum and at 6 months postpartum, calving year, herd size category, source of labour (hired and family labour), calf rearing method (bucket and partial suckling) and parity number of the cow. The effects of the independent variables identified were then investigated using a non-parametric survival technique. The number of days to first oestrus was increased on the U site (p = 0.045) and when family labour was used (p = 0.02). The non-parametric test confirmed the effect of site (p = 0.059), but effect of labour was not significant. The number of days from calving to conception was reduced by hiring labour (p = 0.003) and using natural service (p = 0.028). The non-parametric test confirmed the effects of type of insemination (p = 0.0001) while also identifying extended calving intervals on U and SU sites (p = 0.014). Labour source was again non-significant. Calving interval was prolonged on U and SU sites (p = 0.021), by the use of AI (p = 0.031) and by the use of family labour (p = 0.001). The non-parametric test confirmed the effect of site (p = 0.008) and insemination type (p > 0.0001) but not of labour source. It was concluded that under favourable conditions (PU site, hired labour and natural service) calving intervals of around 440 days could be achieved.

A comparison of the active surveillance of scrapie in the European Union

The abattoir and the fallen stock surveys constitute the active surveillance component aimed at improving the detection of scrapie across the European Union. Previous studies have suggested the occurrence of significant differences in the operation of the surveys across the EU. In the present study we assessed the standardisation of the surveys throughout time across the EU and identified clusters of countries with similar underlying characteristics allowing comparisons between them. In the absence of sufficient covariate information to explain the observed variability across countries, we modelled the unobserved heterogeneity by means of non-parametric distributions on the risk ratios of the fallen stock over the abattoir survey. More specifically, we used the profile likelihood method on 2003, 2004 and 2005 active surveillance data for 18 European countries on classical scrapie, and on 2004 and 2005 data for atypical scrapie separately. We extended our analyses to include the limited covariate information available, more specifically, the proportion of the adult sheep population sampled by the fallen stock survey every year. Our results show that the between-country heterogeneity dropped in 2004 and 2005 relative to that of 2003 for classical scrapie. As a consequence, the number of clusters in the last two years was also reduced indicating the gradual standardisation of the surveillance efforts across the EU. The crude analyses of the atypical data grouped all the countries in one cluster and showed non-significant gain in the detection of this type of scrapie by any of the two sources. The proportion of the population sampled by the fallen stock appeared significantly associated with our risk ratio for both types of scrapie, although in opposite directions: negative for classical and positive for atypical. The initial justification for the fallen stock, targeting a high-risk population to increase the likelihood of case finding, appears compromised for both types of scrapie in some countries.

Case study: object and ground point separation on a river bank using airborne based LIDAR data

In the past decade, airborne based LIght Detection And Ranging (LIDAR) has been recognised by both the commercial and public sectors as a reliable and accurate source for land surveying in environmental, engineering and civil applications. Commonly, the first task to investigate LIDAR point clouds is to separate ground and object points. Skewness Balancing has been proven to be an efficient non-parametric unsupervised classification algorithm to address this challenge. Initially developed for moderate terrain, this algorithm needs to be adapted to handle sloped terrain. This paper addresses the difficulty of object and ground point separation in LIDAR data in hilly terrain. A case study on a diverse LIDAR data set in terms of data provider, resolution and LIDAR echo has been carried out. Several sites in urban and rural areas with man-made structure and vegetation in moderate and hilly terrain have been investigated and three categories have been identified. A deeper investigation on an urban scene with a river bank has been selected to extend the existing algorithm. The results show that an iterative use of Skewness Balancing is suitable for sloped terrain.

Physiological and bifidogenic effects of prebiotic supplements in infant formulae

This randomized controlled trial involving 110 healthy neonates studied physiological and bifidogenic effects of galactooligosaccharides (GOS), oligofructose and long-chain inulin (FOS) in formula. Subjects were randomized to Orafti Synergy1 (50 oligofructose: 50 FOS) 0.4g/dl or 0.8g/dl, GOS:FOS (90:10) 0.8g/dl or a standard formula according to Good Clinical Practise (GCP) guidelines. A breast-fed group was included for comparison. Outcome parameters were weight, length, intake, stool characteristics, crying, regurgitation, vomiting, adverse events and fecal bacterial population counts. Statistical analyses used non-parametric tests. During the first month of life weight, length, intake and crying increased significantly in all groups. Regurgitation and vomiting scores were low and similar. Stool frequency decreased significantly and similarly in all formula groups but was lower than in the breast-fed. All prebiotic groups maintained soft stools, only slightly harder than those of breast-fed infants. The standard group had significantly harder stools at wks 2 and 4 compared to 1 (P<0.001 & P=0.0279). The total number of fecal bacteria increased in all prebiotic groups (9.82, 9.73 and 9.91 to 10.34, 10.38 and 10.37, respectively, log10 cells/g feces, P=0.2298) and resembled more the breast-fed pattern. Numbers of lactic acid bacteria, bacteroides and clostridia were comparable. In the SYN1 0.8 g/dl and GOS:FOS groups Bifidobacterium counts were significantly higher at D14 & 28 compared to D3 and comparable to the breast-fed group. Tolerance and growth were normal. In conclusion, stool consistency and bacterial composition of infants taking SYN1 0.8 g/dl or GOS:FOS supplemented formula was closer to the breast-fed pattern. There was no risk for dehydration.

Economic and agronomic impact of commercialised GM crops: a meta-analysis

The present paper presents a meta-analysis of the economic and agronomic performance of genetically modified (GM) crops worldwide. Bayesian, classical and non-parametric approaches were used to evaluate the performance of GM crops v. their conventional counterparts. The two main GM crop traits (herbicide tolerant (HT) and insect resistant (Bt)) and three of the main GM crops produced worldwide (Bt cotton, HT soybean and Bt maize) were analysed in terms of yield, production cost and gross margin. The scope of the analysis covers developing and developed countries, six world regions, and all countries combined. Results from the statistical analyses indicate that GM crops perform better than their conventional counterparts in agronomic and economic (gross margin) terms. Regarding countries’ level of development, GM crops tend to perform better in developing countries than in developed countries, with Bt cotton being the most profitable crop grown.

A process for analysis of microarray comparative genomics hybridisation studies for bacterial genomes

Background: Microarray based comparative genomic hybridisation (CGH) experiments have been used to study numerous biological problems including understanding genome plasticity in pathogenic bacteria. Typically such experiments produce large data sets that are difficult for biologists to handle. Although there are some programmes available for interpretation of bacterial transcriptomics data and CGH microarray data for looking at genetic stability in oncogenes, there are none specifically to understand the mosaic nature of bacterial genomes. Consequently a bottle neck still persists in accurate processing and mathematical analysis of these data. To address this shortfall we have produced a simple and robust CGH microarray data analysis process that may be automated in the future to understand bacterial genomic diversity. Results: The process involves five steps: cleaning, normalisation, estimating gene presence and absence or divergence, validation, and analysis of data from test against three reference strains simultaneously. Each stage of the process is described and we have compared a number of methods available for characterising bacterial genomic diversity, for calculating the cut-off between gene presence and absence or divergence, and shown that a simple dynamic approach using a kernel density estimator performed better than both established, as well as a more sophisticated mixture modelling technique. We have also shown that current methods commonly used for CGH microarray analysis in tumour and cancer cell lines are not appropriate for analysing our data. Conclusion: After carrying out the analysis and validation for three sequenced Escherichia coli strains, CGH microarray data from 19 E. coli O157 pathogenic test strains were used to demonstrate the benefits of applying this simple and robust process to CGH microarray studies using bacterial genomes.

Survival analysis of cereal crop variety innovations in the UK

This paper explores the changing survival patterns of cereal crop variety innovations in the UK since the introduction of plant breeders’ rights in the mid-1960s. Using non-parametric, semi-parametric and parametric approaches, we examine the determinants of the survival of wheat variety innovations, focusing on the impacts of changes to Plant Variety Protection (PVP) regime over the last four decades. We find that the period since the introduction of the PVP regime has been characterised by the accelerated development of new varieties and increased private sector participation in the breeding of cereal crop varieties. However, the increased flow of varieties has been accompanied by a sharp decline in the longevity of innovations. These trends may have contributed to a reduction in the returns appropriated by plant breeders from protected variety innovations and may explain the decline of conventional plant breeding in the UK. It may also explain the persistent demand from the seed industry for stronger protection. The strengthening of the PVP regime in conformity with the UPOV Convention of 1991, the introduction of EU-wide protection through the Community Plant Variety Office and the introduction of royalties on farm-saved seed have had a positive effect on the longevity of protected variety innovations, but have not been adequate to offset the long term decline in survival durations.