188 resultados para Counterfactual conditional
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A discrete agent-based model on a periodic lattice of arbitrary dimension is considered. Agents move to nearest-neighbor sites by a motility mechanism accounting for general interactions, which may include volume exclusion. The partial differential equation describing the average occupancy of the agent population is derived systematically. A diffusion equation arises for all types of interactions and is nonlinear except for the simplest interactions. In addition, multiple species of interacting subpopulations give rise to an advection-diffusion equation for each subpopulation. This work extends and generalizes previous specific results, providing a construction method for determining the transport coefficients in terms of a single conditional transition probability, which depends on the occupancy of sites in an influence region. These coefficients characterize the diffusion of agents in a crowded environment in biological and physical processes.
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This paper employs a VAR-GARCH model to investigate the return links and volatility transmission between the S&P 500 and commodity price indices for energy, food, gold and beverages over the turbulent period from 2000 to 2011. Understanding the price behavior of commodity prices and the volatility transmission mechanism between these markets and the stock exchanges are crucial for each participant, including governments, traders, portfolio managers, consumers, and producers. For return and volatility spillover, the results show significant transmission among the S&P 500 and commodity markets. The past shocks and volatility of the S&P 500 strongly influenced the oil and gold markets. This study finds that the highest conditional correlations are between the S&P 500 and gold index and the S&P 500 and WTI index. We also analyze the optimal weights and hedge ratios for commodities/S&P 500 portfolio holdings using the estimates for each index. Overall, our findings illustrate several important implications for portfolio hedgers for making optimal portfolio allocations, engaging in risk management and forecasting future volatility in equity and commodity markets. © 2013 Elsevier B.V.
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This thesis has contributed to the advancement of knowledge in disease modelling by addressing interesting and crucial issues relevant to modelling health data over space and time. The research has led to the increased understanding of spatial scales, temporal scales, and spatial smoothing for modelling diseases, in terms of their methodology and applications. This research is of particular significance to researchers seeking to employ statistical modelling techniques over space and time in various disciplines. A broad class of statistical models are employed to assess what impact of spatial and temporal scales have on simulated and real data.
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Ecological studies are based on characteristics of groups of individuals, which are common in various disciplines including epidemiology. It is of great interest for epidemiologists to study the geographical variation of a disease by accounting for the positive spatial dependence between neighbouring areas. However, the choice of scale of the spatial correlation requires much attention. In view of a lack of studies in this area, this study aims to investigate the impact of differing definitions of geographical scales using a multilevel model. We propose a new approach -- the grid-based partitions and compare it with the popular census region approach. Unexplained geographical variation is accounted for via area-specific unstructured random effects and spatially structured random effects specified as an intrinsic conditional autoregressive process. Using grid-based modelling of random effects in contrast to the census region approach, we illustrate conditions where improvements are observed in the estimation of the linear predictor, random effects, parameters, and the identification of the distribution of residual risk and the aggregate risk in a study region. The study has found that grid-based modelling is a valuable approach for spatially sparse data while the SLA-based and grid-based approaches perform equally well for spatially dense data.
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Section 95AT of the Competition and Consumer Act 2010 (Cth) (CCA) provides that the Tribunal may grant an authorisation to acquire shares or assets that would otherwise contravene s 50. Section 95AT was inserted by the Trade Practices Legislation Amendment Act 2006 (Cth) and commenced on 1 January 2007. In Application for Authorisation of Macquarie Generation by AGL Energy Limited, (AGL Energy) the Tribunal has for the first time granted AGL Energy Limited (AGL) a conditional authorisation to acquire the assets of Macquarie Generation from the NSW Government.
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The HOXB13 gene has been implicated in prostate cancer (PrCa) susceptibility. We performed a high resolution fine-mapping analysis to comprehensively evaluate the association between common genetic variation across the HOXB genetic locus at 17q21 and PrCa risk. This involved genotyping 700 SNPs using a custom Illumina iSelect array (iCOGS) followed by imputation of 3195 SNPs in 20,440 PrCa cases and 21,469 controls in The PRACTICAL consortium. We identified a cluster of highly correlated common variants situated within or closely upstream of HOXB13 that were significantly associated with PrCa risk, described by rs117576373 (OR 1.30, P = 2.62×10(-14)). Additional genotyping, conditional regression and haplotype analyses indicated that the newly identified common variants tag a rare, partially correlated coding variant in the HOXB13 gene (G84E, rs138213197), which has been identified recently as a moderate penetrance PrCa susceptibility allele. The potential for GWAS associations detected through common SNPs to be driven by rare causal variants with higher relative risks has long been proposed; however, to our knowledge this is the first experimental evidence for this phenomenon of synthetic association contributing to cancer susceptibility.
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The adhesion molecule L1, which is extensively characterized in the nervous system, is also expressed in dendritic cells (DCs), but its function there has remained elusive. To address this issue, we ablated L1 expression in DCs of conditional knockout mice. L1-deficient DCs were impaired in adhesion to and transmigration through monolayers of either lymphatic or blood vessel endothelial cells, implicating L1 in transendothelial migration of DCs. In agreement with these findings, L1 was expressed in cutaneous DCs that migrated to draining lymph nodes, and its ablation reduced DC trafficking in vivo. Within the skin, L1 was found in Langerhans cells but not in dermal DCs, and L1 deficiency impaired Langerhans cell migration. Under inflammatory conditions, L1 also became expressed in vascular endothelium and enhanced transmigration of DCs, likely through L1 homophilic interactions. Our results implicate L1 in the regulation of DC trafficking and shed light on novel mechanisms underlying transendothelial migration of DCs. These observations might offer novel therapeutic perspectives for the treatment of certain immunological disorders.
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The existence of a healthy immigrant effect—where immigrants are on average healthier than the native born—is a widely cited phenomenon across a multitude of literatures including epidemiology and the social sciences. There are many competing explanations. The goals of this paper are twofold: first, to provide further evidence on the presence of the healthy immigrant effect across source and destination country using a set of consistently defined measures of health; and second, to evaluate the role of selectivity as a potential explanation for the existence of the phenomenon. Utilizing data from four major immigrant recipient countries, USA, Canada, UK, and Australia allows us to compare the health of migrants from each with the respective native born who choose not to migrate. This represents a much more appropriate counterfactual than the native born of the immigrant recipient country and yields new insights into the importance of observable selection effects. The analysis finds strong support for the healthy immigrant effect across all four destination countries and that selectivity plays an important role in the observed better health of migrants vis a vis those who stay behind in their country of origin.
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In this paper conditional hidden Markov model (HMM) filters and conditional Kalman filters (KF) are coupled together to improve demodulation of differential encoded signals in noisy fading channels. We present an indicator matrix representation for differential encoded signals and the optimal HMM filter for demodulation. The filter requires O(N3) calculations per time iteration, where N is the number of message symbols. Decision feedback equalisation is investigated via coupling the optimal HMM filter for estimating the message, conditioned on estimates of the channel parameters, and a KF for estimating the channel states, conditioned on soft information message estimates. The particular differential encoding scheme examined in this paper is differential phase shift keying. However, the techniques developed can be extended to other forms of differential modulation. The channel model we use allows for multiplicative channel distortions and additive white Gaussian noise. Simulation studies are also presented.
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Interpolation techniques for spatial data have been applied frequently in various fields of geosciences. Although most conventional interpolation methods assume that it is sufficient to use first- and second-order statistics to characterize random fields, researchers have now realized that these methods cannot always provide reliable interpolation results, since geological and environmental phenomena tend to be very complex, presenting non-Gaussian distribution and/or non-linear inter-variable relationship. This paper proposes a new approach to the interpolation of spatial data, which can be applied with great flexibility. Suitable cross-variable higher-order spatial statistics are developed to measure the spatial relationship between the random variable at an unsampled location and those in its neighbourhood. Given the computed cross-variable higher-order spatial statistics, the conditional probability density function (CPDF) is approximated via polynomial expansions, which is then utilized to determine the interpolated value at the unsampled location as an expectation. In addition, the uncertainty associated with the interpolation is quantified by constructing prediction intervals of interpolated values. The proposed method is applied to a mineral deposit dataset, and the results demonstrate that it outperforms kriging methods in uncertainty quantification. The introduction of the cross-variable higher-order spatial statistics noticeably improves the quality of the interpolation since it enriches the information that can be extracted from the observed data, and this benefit is substantial when working with data that are sparse or have non-trivial dependence structures.
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This paper discusses Compulsory Income Management (CIM) in Australia and the implications of technology backed forms of surveillance and increasingly conditional benefit payments. The CIM project raises important questions about requiring people to take greater responsibility for their personal behaviour when they no longer have control over key financial aspects of their lives. Some Indigenous communities have resisted the BasicsCard, because CIM was imposed with little prior consultation or subsequent independent evaluation. The compulsory income management of individuals by a paternalist welfare state contradicts and undermines the purported policy aims that they become less welfare dependent and more positively engaged with the world of paid employment and does little to address the growing condition of poverty in Australia.
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Bayesian networks (BNs) are graphical probabilistic models used for reasoning under uncertainty. These models are becoming increasing popular in a range of fields including ecology, computational biology, medical diagnosis, and forensics. In most of these cases, the BNs are quantified using information from experts, or from user opinions. An interest therefore lies in the way in which multiple opinions can be represented and used in a BN. This paper proposes the use of a measurement error model to combine opinions for use in the quantification of a BN. The multiple opinions are treated as a realisation of measurement error and the model uses the posterior probabilities ascribed to each node in the BN which are computed from the prior information given by each expert. The proposed model addresses the issues associated with current methods of combining opinions such as the absence of a coherent probability model, the lack of the conditional independence structure of the BN being maintained, and the provision of only a point estimate for the consensus. The proposed model is applied an existing Bayesian Network and performed well when compared to existing methods of combining opinions.
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Incentives are commonly offered by commercial landlords to tenants in the form of short term rent deductions or contributions to the tenant’s fitout. Usually these incentives are conditional upon the lessee remaining in the premises for the term of the lease with an obligation on the tenant to repay a proportion of the fitout contribution and rent deductions upon early termination or assignment. While the enforceability of clawback provisions has always been unclear, there was commercial benefit to landlords in maintaining high rentals on the face of the lease and attracting good quality tenants through fitout contributions. The use of clawback provisions as part of these incentives was recently analysed by the Queensland Supreme Court through the lens of the penalties doctrine in GWC Property Group Pty Ltd v Higginson & Ors [2014] QSC 264, with a negative outcome for the landlord. Unless the decision is overturned on appeal, the salient message for landlords is that repayment of incentives for any reason, not just a breach of the lease, is unlikely to be enforceable.
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Background Burden of disease estimates for South Africa have highlighted the particularly high rates of injuries related to interpersonal violence compared with other regions of the world, but these figures tell only part of the story. In addition to direct physical injury, violence survivors are at an increased risk of a wide range of psychological and behavioral problems. This study aimed to comprehensively quantify the excess disease burden attributable to exposure to interpersonal violence as a risk factor for disease and injury in South Africa. Methods The World Health Organization framework of interpersonal violence was adapted. Physical injury mortality and disability were categorically attributed to interpersonal violence. In addition, exposure to child sexual abuse and intimate partner violence, subcategories of interpersonal violence, were treated as risk factors for disease and injury using counterfactual estimation and comparative risk assessment methods. Adjustments were made to account for the combined exposure state of having experienced both child sexual abuse and intimate partner violence. Results Of the 17 risk factors included in the South African Comparative Risk Assessment study, interpersonal violence was the second leading cause of healthy years of life lost, after unsafe sex, accounting for 1.7 million disability-adjusted life years (DALYs) or 10.5% of all DALYs (95% uncertainty interval: 8.5%-12.5%) in 2000. In women, intimate partner violence accounted for 50% and child sexual abuse for 32% of the total attributable DALYs. Conclusions The implications of our findings are that estimates that include only the direct injury burden seriously underrepresent the full health impact of interpersonal violence. Violence is an important direct and indirect cause of health loss and should be recognized as a priority health problem as well as a human rights and social issue. This study highlights the difficulties in measuring the disease burden from interpersonal violence as a risk factor and the need to improve the epidemiological data on the prevalence and risks for the different forms of interpersonal violence to complete the picture. Given the extent of the burden, it is essential that innovative research be supported to identify social policy and other interventions that address both the individual and societal aspects of violence.