Hidden Gibbs random fields model selection using Block Likelihood Information Criterion

Performing model selection between Gibbs random fields is a very challenging task. Indeed, due to the Markovian dependence structure, the normalizing constant of the fields cannot be computed using standard analytical or numerical methods. Furthermore, such unobserved fields cannot be integrated out and the likelihood evaluztion is a doubly intractable problem. This forms a central issue to pick the model that best fits an observed data. We introduce a new approximate version of the Bayesian Information Criterion. We partition the lattice into continuous rectangular blocks and we approximate the probability measure of the hidden Gibbs field by the product of some Gibbs distributions over the blocks. On that basis, we estimate the likelihood and derive the Block Likelihood Information Criterion (BLIC) that answers model choice questions such as the selection of the dependency structure or the number of latent states. We study the performances of BLIC for those questions. In addition, we present a comparison with ABC algorithms to point out that the novel criterion offers a better trade-off between time efficiency and reliable results

Modeling and Testing for Joint Association Using a Genetic Random Field Model

Substantial progress has been made in identifying single genetic variants predisposing to common complex diseases. Nonetheless, the genetic etiology of human diseases remains largely unknown. Human complex diseases are likely influenced by the joint effect of a large number of genetic variants instead of a single variant. The joint analysis of multiple genetic variants considering linkage disequilibrium (LD) and potential interactions can further enhance the discovery process, leading to the identification of new disease-susceptibility genetic variants. Motivated by the recent development in spatial statistics, we propose a new statistical model based on the random field theory, referred to as a genetic random field model (GenRF), for joint association analysis with the consideration of possible gene-gene interactions and LD. Using a pseudo-likelihood approach, a GenRF test for the joint association of multiple genetic variants is developed, which has the following advantages: 1. considering complex interactions for improved performance; 2. natural dimension reduction; 3. boosting power in the presence of LD; 4. computationally efficient. Simulation studies are conducted under various scenarios. Compared with a commonly adopted kernel machine approach, SKAT, GenRF shows overall comparable performance and better performance in the presence of complex interactions. The method is further illustrated by an application to the Dallas Heart Study.Comment: 17 pages, 4 tables, the paper has been published on Biometric

Sequential Implementation of Monte Carlo Tests with Uniformly Bounded Resampling Risk

This paper introduces an open-ended sequential algorithm for computing the p-value of a test using Monte Carlo simulation. It guarantees that the resampling risk, the probability of a different decision than the one based on the theoretical p-value, is uniformly bounded by an arbitrarily small constant. Previously suggested sequential or non-sequential algorithms, using a bounded sample size, do not have this property. Although the algorithm is open-ended, the expected number of steps is finite, except when the p-value is on the threshold between rejecting and not rejecting. The algorithm is suitable as standard for implementing tests that require (re-)sampling. It can also be used in other situations: to check whether a test is conservative, iteratively to implement double bootstrap tests, and to determine the sample size required for a certain power.Comment: Major Revision 15 pages, 4 figure

Power properties if invariant tests for spatial autocorrelation in linear regression

This paper derives some exact power properties of tests for spatial autocorrelation in the context of a linear regression model. In particular, we characterize the circumstances in which the power vanishes as the autocorrelation increases, thus extending the work of Krämer (2005). More generally, the analysis in the paper sheds new light on how the power of tests for spatial autocorrelation is affected by the matrix of regressors and by the spatial structure. We mainly focus on the problem of residual spatial autocorrelation, in which case it is appropriate to restrict attention to the class of invariant tests, but we also consider the case when the autocorrelation is due to the presence of a spatially lagged dependent variable among the regressors. A numerical study aimed at assessing the practical relevance of the theoretical results is include

Valproate-Associated Parkinsonism: A Critical Review of the Literature

Valproate was first approved as an antiepileptic drug in 1962 and has since also become established as a mood stabiliser and as prophylaxis for migraine. In 1979, Lautin published the first description of a valproate-associated extrapyramidal syndrome. Many cases of valproate-associated parkinsonism have subsequently been published, but uncertainties remain concerning its prevalence, risk factors and prognosis. The aim of this paper is to provide a critical review of the existing literature on valproate-associated parkinsonism and to discuss possible mechanisms. Literature databases were searched systematically: we identified a total of 116 patients with valproate-associated parkinsonism published in case reports, case series and systematic analyses. Prevalence rates ranged widely, between 1.4 and 75 % of patients taking valproate. There was great heterogeneity with regard to clinical presentation, age of onset, valproate dose, concomitant conditions and imaging findings. In all patients apart from three, valproate plasma concentrations were within or even below the recommended reference range when the parkinsonism occurred. Parkinsonism was reversible in the majority of patients, although recovery was often prolonged and sometimes incomplete. A dopaminergic deficit was confirmed in three of six patients investigated with dopamine transporter imaging. Seven of 14 patients who were treated with dopaminergic medication had a good response. The quality of the evidence was assessed and probability of causation was examined using the Naranjo score, which ranged from 0 to 7 (median: 5.0). Several pathophysiological mechanisms, including altered gene expression and neurotransmitter signalling, enhanced neurodegeneration or unmasking subclinical dopaminergic degeneration, could theoretically lead to valproate-associated parkinsonism. Further studies are warranted to elucidate this entity and its underlying pathophysiology

A Markov chain approach to renormalization group transformations

We aim at an explicit characterization of the renormalized Hamiltonian after decimation transformation of a one-dimensional Ising-type Hamiltonian with a nearest-neighbor interaction and a magnetic field term. To facilitate a deeper understanding of the decimation effect, we translate the renormalization flow on the Ising Hamiltonian into a flow on the associated Markov chains through the Markov-Gibbs equivalence. Two different methods are used to verify the well-known conjecture that the eigenvalues of the linearization of this renormalization transformation about the fixed point bear important information about all six of the critical exponents. This illustrates the universality property of the renormalization group map in this case.Comment: 10 page

Clinical efficacy of perampanel for partial-onset and primary generalized tonic-clonic seizures

Background and purpose: Perampanel, a selective noncompetitive antagonist at the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor, is highly effective in a wide range of experimental models. Although initially licensed as adjunctive therapy for partial seizures with or without secondary generalization in patients aged 12 years or older, the US Food and Drug Administration has recently approved its use in the treatment of primary generalized tonic–clonic seizures (PGTCS). This paper reviews the pharmacokinetics, efficacy, and tolerability of perampanel as an antiepileptic drug. / Results: After oral ingestion, perampanel is rapidly absorbed (Tmax, 0.5–2.5 hours), has a bioavailability of ~100%, and is highly protein bound (~95%) in plasma. It undergoes extensive (>90%) hepatic metabolism, primarily via cytochrome P450 3A4 (CYP3A4), with a half-life of 48 hours. Carbamazepine and other antiepileptic drugs can enhance its metabolism via induction of CYP3A4. Efficacy of perampanel in focal seizures has been extensively evaluated in Phase II and randomized, placebo-controlled Phase III trials. The efficacy in PGTCS has been reported in one class I study. In the treatment of focal seizures, perampanel showed significant dose-dependent median seizure reductions: 4 mg/d, 23%; 8 mg/d, 26%–31%; 12 mg/d, 18%–35%; and placebo, 10%–21%. The 50% responder rates were 15%–26%, 29%, 33%–38%, and 34%–36% for placebo, 4 mg/d, 8 mg/d, and 12 mg/d perampanel, respectively. Freedom from seizures was recorded in 0%–1.7% of the placebo group, 1.9% of the 2 mg group, 2.6%–4.4% of the 8 mg group, and 2.6%–6.5% of the 12 mg group. For PGTCS, the median seizure reduction was 76.5% for perampanel and 38.4% for placebo. The 50% responder rate was 64.2% for perampanel and 39.5% for placebo. Seizure freedom during maintenance phase was 30.9% for perampanel and 12.3% for placebo. Adverse effects included dose-dependent increases in the frequency of dizziness, somnolence, fatigue, irritability, falls, and probably nausea. / Conclusion: Perampanel is effective in treating both partial-onset seizures and PGTCS

Energy-based temporal neural networks for imputing missing values

Imputing missing values in high dimensional time series is a difficult problem. There have been some approaches to the problem [11,8] where neural architectures were trained as probabilistic models of the data. However, we argue that this approach is not optimal. We propose to view temporal neural networks with latent variables as energy-based models and train them for missing value recovery directly. In this paper we introduce two energy-based models. The first model is based on a one dimensional convolution and the second model utilizes a recurrent neural network. We demonstrate how ideas from the energy-based learning framework can be used to train these models to recover missing values. The models are evaluated on a motion capture dataset

Composite Correlation Quantization for Efficient Multimodal Retrieval

Efficient similarity retrieval from large-scale multimodal database is pervasive in modern search engines and social networks. To support queries across content modalities, the system should enable cross-modal correlation and computation-efficient indexing. While hashing methods have shown great potential in achieving this goal, current attempts generally fail to learn isomorphic hash codes in a seamless scheme, that is, they embed multiple modalities in a continuous isomorphic space and separately threshold embeddings into binary codes, which incurs substantial loss of retrieval accuracy. In this paper, we approach seamless multimodal hashing by proposing a novel Composite Correlation Quantization (CCQ) model. Specifically, CCQ jointly finds correlation-maximal mappings that transform different modalities into isomorphic latent space, and learns composite quantizers that convert the isomorphic latent features into compact binary codes. An optimization framework is devised to preserve both intra-modal similarity and inter-modal correlation through minimizing both reconstruction and quantization errors, which can be trained from both paired and partially paired data in linear time. A comprehensive set of experiments clearly show the superior effectiveness and efficiency of CCQ against the state of the art hashing methods for both unimodal and cross-modal retrieval

A Bayesian localised conditional auto-regressive model for estimating the health effects of air pollution

Estimation of the long-term health effects of air pollution is a challenging task, especially when modeling spatial small-area disease incidence data in an ecological study design. The challenge comes from the unobserved underlying spatial autocorrelation structure in these data, which is accounted for using random effects modeled by a globally smooth conditional autoregressive model. These smooth random effects confound the effects of air pollution, which are also globally smooth. To avoid this collinearity a Bayesian localized conditional autoregressive model is developed for the random effects. This localized model is flexible spatially, in the sense that it is not only able to model areas of spatial smoothness, but also it is able to capture step changes in the random effects surface. This methodological development allows us to improve the estimation performance of the covariate effects, compared to using traditional conditional auto-regressive models. These results are established using a simulation study, and are then illustrated with our motivating study on air pollution and respiratory ill health in Greater Glasgow, Scotland in 2011. The model shows substantial health effects of particulate matter air pollution and nitrogen dioxide, whose effects have been consistently attenuated by the currently available globally smooth models