Bayesian analysis of multiple thresholds autoregressive model

Bayesian analysis of threshold autoregressive (TAR) model with various possible thresholds is considered. A method of Bayesian stochastic search selection is introduced to identify a threshold-dependent sequence with highest probability. All model parameters are computed by a hybrid Markov chain Monte Carlo (MCMC) method, which combines Metropolis-Hastings (M-H) algorithm and Gibbs sampler. The main innovation of the method introduced here is to estimate the TAR model without assuming the fixed number of threshold values, thus is more flexible and useful. Simulation experiments and a real data example lend further support to the proposed approach

Testing a linear ARMA Model against threshold-ARMA models : a Bayesian approach

We introduce a Bayesian approach to test linear autoregressive moving-average (ARMA) models against threshold autoregressive moving-average (TARMA) models. Firstly, the marginal posterior densities of all parameters, including the threshold and delay, of a TARMA model are obtained by using Gibbs sampler with Metropolis-Hastings algorithm. Secondly, reversible-jump Markov chain Monte Carlo (RJMCMC) method is adopted to calculate the posterior probabilities for ARMA and TARMA models: Posterior evidence in favor of TARMA models indicates threshold nonlinearity. Finally, based on RJMCMC scheme and Akaike information criterion (AIC) or Bayesian information criterion (BIC), the procedure for modeling TARMA models is exploited. Simulation experiments and a real data example show that our method works well for distinguishing a ARMA from a TARMA model and for building TARMA models

Subset selection of double-threshold moving average models through the application of the Bayesian method

The Bayesian method is firstly applied for the selection of the best subset for the double-threshold moving average (DTMA) model. The Markov chain Monte Carlo (MCMC) techniques and the stochastic search variable selection (SSVS) method are used to identify the best subset model from a very large number of possible models. Simulation experiments show that the proposed method is feasible and efficient, despite the complexity being increased by the large number of subsets, and the uncertainty of the threshold and delay variables. Our method is illustrated by real data analysis on the Yen-Dollar exchange rate

Investigation of genetic diversity and population structure of common wheat cultivars in northern China using DArT markers

<p>Abstract</p> <p>Background</p> <p>In order to help establish heterotic groups of Chinese northern wheat cultivars (lines), Diversity arrays technology (DArT) markers were used to investigate the genetic diversity and population structure of Chinese common wheat (<it>Triticum aestivum </it>L.).</p> <p>Results</p> <p>In total, 1637 of 7000 DArT markers were polymorphic and scored with high confidence among a collection of 111 lines composed mostly of cultivars and breeding lines from northern China. The polymorphism information content (PIC) of DArT markers ranged from 0.03 to 0.50, with an average of 0.40, with P > 80 (reliable markers). With principal-coordinates analysis (PCoA) of DArT data either from the whole genome or from the B-genome alone, all lines fell into one of two major groups reflecting 1RS/1BL type (1RS/1BL and non-1RS/1BL). Evidence of geographic clustering of genotypes was also observed using DArT markers from the A genome. Cluster analysis based on the unweighted pair-group method with algorithmic mean suggested the existence of two subgroups within the non-1RS/1BL group and four subgroups within the 1RS/1BL group. Furthermore, analysis of molecular variance (AMOVA) revealed highly significant (<it>P </it>< 0.001) genetic variance within and among subgroups and among groups.</p> <p>Conclusion</p> <p>These results provide valuable information for selecting crossing parents and establishing heterotic groups in the Chinese wheat-breeding program.</p

Location privacy without mutual trust: The spatial Bloom filter

Location-aware applications are one of the biggest innovations brought by the smartphone era, and are effectively changing our everyday lives. But we are only starting to grasp the privacy risks associated with constant tracking of our whereabouts. In order to continue using location-based services in the future without compromising our privacy and security, we need new, privacy-friendly applications and protocols. In this paper, we propose a new compact data structure based on Bloom filters, designed to store location information. The spatial Bloom filter (SBF), as we call it, is designed with privacy in mind, and we prove it by presenting two private positioning protocols based on the new primitive. The protocols keep the user's exact position private, but allow the provider of the service to learn when the user is close to specific points of interest, or inside predefined areas. At the same time, the points and areas of interest remain oblivious to the user. The two proposed protocols are aimed at different scenarios: a two-party setting, in which communication happens directly between the user and the service provider, and a three-party setting, in which the service provider outsources to a third party the communication with the user. A detailed evaluation of the efficiency and security of our solution shows that privacy can be achieved with minimal computational and communication overhead. The potential of spatial Bloom filters in terms of generality, security and compactness makes them ready for deployment, and may open the way for privacy preserving location-aware applications

Development of a new marker system for identifying the complex members of the low-molecular-weight glutenin subunit gene family in bread wheat (Triticum aestivum L.)

Low-molecular-weight glutenin subunits (LMW-GSs) play an important role in determining the bread-making quality of bread wheat. However, LMW-GSs display high polymorphic protein complexes encoded by multiple genes, and elucidating the complex LMW-GS gene family in bread wheat remains challenging. In the present study, using conventional polymerase chain reaction (PCR) with conserved primers and high-resolution capillary electrophoresis, we developed a new molecular marker system for identifying LMW-GS gene family members. Based on sequence alignment of 13 LMW-GS genes previously identified in the Chinese bread wheat variety Xiaoyan 54 and other genes available in GenBank, PCR primers were developed and assigned to conserved sequences spanning the length polymorphism regions of LMW-GS genes. After PCR amplification, 17 DNA fragments in Xiaoyan 54 were detected using capillary electrophoresis. In total, 13 fragments were identical to previously identified LMW-GS genes, and the other 4 were derived from unique LMW-GS genes by sequencing. This marker system was also used to identify LMW-GS genes in Chinese Spring and its group 1 nulli–tetrasomic lines. Among the 17 detected DNA fragments, 4 were located on chromosome 1A, 5 on 1B, and 8 on 1D. The results suggest that this marker system is useful for large-scale identification of LMW-GS genes in bread wheat varieties, and for the selection of desirable LMW-GS genes to improve the bread-making quality in wheat molecular breeding programmes

New Insights into the Organization, Recombination, Expression and Functional Mechanism of Low Molecular Weight Glutenin Subunit Genes in Bread Wheat

The bread-making quality of wheat is strongly influenced by multiple low molecular weight glutenin subunit (LMW-GS) proteins expressed in the seeds. However, the organization, recombination and expression of LMW-GS genes and their functional mechanism in bread-making are not well understood. Here we report a systematic molecular analysis of LMW-GS genes located at the orthologous Glu-3 loci (Glu-A3, B3 and D3) of bread wheat using complementary approaches (genome wide characterization of gene members, expression profiling, proteomic analysis). Fourteen unique LMW-GS genes were identified for Xiaoyan 54 (with superior bread-making quality). Molecular mapping and recombination analyses revealed that the three Glu-3 loci of Xiaoyan 54 harbored dissimilar numbers of LMW-GS genes and covered different genetic distances. The number of expressed LMW-GS in the seeds was higher in Xiaoyan 54 than in Jing 411 (with relatively poor bread-making quality). This correlated with the finding of higher numbers of active LMW-GS genes at the A3 and D3 loci in Xiaoyan 54. Association analysis using recombinant inbred lines suggested that positive interactions, conferred by genetic combinations of the Glu-3 locus alleles with more numerous active LMW-GS genes, were generally important for the recombinant progenies to attain high Zeleny sedimentation value (ZSV), an important indicator of bread-making quality. A higher number of active LMW-GS genes tended to lead to a more elevated ZSV, although this tendency was influenced by genetic background. This work provides substantial new insights into the genomic organization and expression of LMW-GS genes, and molecular genetic evidence suggesting that these genes contribute quantitatively to bread-making quality in hexaploid wheat. Our analysis also indicates that selection for high numbers of active LMW-GS genes can be used for improvement of bread-making quality in wheat breeding

Mechanism Study and Tendency Judgement of Rockburst in Deep-Buried Underground Engineering

Rockburst is a type of dynamic instability failure phenomenon and frequently brings huge losses to underground engineering projects such as mines and tunnels. In order to explore rockburst mechanisms and predict rockbursts better, relying on the background of Wulaofeng deep-buried highway tunnel, in situ stress measurement was performed using new wireless devices, and mechanics tests of surrounding rock samples taken from different burial depths were carried out. The rockburst mechanism was explored from the microscopic perspective based on the analysis of scanning electron microscopy (SEM). Rockburst tendency was judged comprehensively by a tendency analysis, grade prediction and numerical simulation. The result showed that the mechanical parameters of granite rocks in the deep-buried section were larger than those in the entrance section, and the fractured morphology mainly comprised sheet and monolithic block, corresponding to transgranular fracture and intergranular fracture. Rocks with few types of mineral cementation, good crystallization and small particle size differences had better energy storage and release characteristics. There was little difference in the rockburst tendency of rocks with different buried depths, but there were obvious differences in the rockburst grade. In the deep-buried section of the tunnel, the rockburst grade was of a moderate–heavy level and the rockburst risk at the vault and right spandrel of the cross section was more severe, which was basically consistent with the situation at the tunnel site. This study can provide a theoretical basis for the prevention and control of rockbursts in Wulaofeng tunnel and other similar engineering projects

Markov-switching poisson generalized autoregressive conditional heteroscedastic models

We consider a kind of regime-switching autoregressive models for nonnegative integer-valued time series when the conditional distribution given historical information is Poisson distribution. In this type of models the link between the conditional variance (i.e. the conditional mean for Poisson distribution) and its past values as well as the observed values of the Poisson process may be different when an unobservable (hidden) variable, which is a Markovian Chain, takes different states. We study the stationarity and ergodicity of Markov-switching Poisson generalized autoregressive heteroscedastic (MS-PGARCH) models, and give a condition on parameters under which a MS-PGARCH process can be approximated by a geometrically ergodic process. Under this condition we discuss maximum likelihood estimation for MS-PGARCH models. Simulation studies and application to modelling financial count time series are presented to support our methodology