369 research outputs found

    Testing in high-dimensional spiked models

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    We consider the five classes of multivariate statistical problems identified by James (1964), which together cover much of classical multivariate analysis, plus a simpler limiting case, symmetric matrix denoising. Each of James' problems involves the eigenvalues of E1HE^{-1}H where HH and EE are proportional to high dimensional Wishart matrices. Under the null hypothesis, both Wisharts are central with identity covariance. Under the alternative, the non-centrality or the covariance parameter of HH has a single eigenvalue, a spike, that stands alone. When the spike is smaller than a case-specific phase transition threshold, none of the sample eigenvalues separate from the bulk, making the testing problem challenging. Using a unified strategy for the six cases, we show that the log likelihood ratio processes parameterized by the value of the sub-critical spike converge to Gaussian processes with logarithmic correlation. We then derive asymptotic power envelopes for tests for the presence of a spike

    Genome-wide association analysis of GAW17 data using an empirical Bayes variable selection

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    Next-generation sequencing technologies enable us to explore rare functional variants. However, most current statistical techniques are too underpowered to capture signals of rare variants in genome-wide association studies. We propose a supervised coalescing of single-nucleotide polymorphisms to obtain gene-based markers that can stably reveal possible genetic effects related to rare alleles. We use a newly developed empirical Bayes variable selection algorithm to identify associations between studied traits and genetic markers. Using our novel method, we analyzed the three continuous phenotypes in the GAW17 data set across 200 replicates, with intriguing results

    Genome-wide case-control study in GAW17 using coalesced rare variants

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    Genome-wide association studies have successfully identified numerous loci at which common variants influence disease risks or quantitative traits of interest. Despite these successes, the variants identified by these studies have generally explained only a small fraction of the variations in the phenotype. One explanation may be that many rare variants that are not included in the common genotyping platforms may contribute substantially to the genetic variations of the diseases. Next-generation sequencing, which would better allow for the analysis of rare variants, is now becoming available and affordable; however, the presence of a large number of rare variants challenges the statistical endeavor to stably identify these disease-causing genetic variants. We conduct a genome-wide association study of Genetic Analysis Workshop 17 case-control data produced by the next-generation sequencing technique and propose that collapsing rare variants within each genetic region through a supervised dimension reduction algorithm leads to several macrovariants constructed for rare variants within each genetic region. A simultaneous association of the phenotype to all common variants and macrovariants is undertaken using a linear discriminant analysis using the penalized orthogonal-components regression algorithm. The results suggest that the proposed analysis strategy shows promise but needs further development

    The projection score - an evaluation criterion for variable subset selection in PCA visualization

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    <p>Abstract</p> <p>Background</p> <p>In many scientific domains, it is becoming increasingly common to collect high-dimensional data sets, often with an exploratory aim, to generate new and relevant hypotheses. The exploratory perspective often makes statistically guided visualization methods, such as Principal Component Analysis (PCA), the methods of choice. However, the clarity of the obtained visualizations, and thereby the potential to use them to formulate relevant hypotheses, may be confounded by the presence of the many non-informative variables. For microarray data, more easily interpretable visualizations are often obtained by filtering the variable set, for example by removing the variables with the smallest variances or by only including the variables most highly related to a specific response. The resulting visualization may depend heavily on the inclusion criterion, that is, effectively the number of retained variables. To our knowledge, there exists no objective method for determining the optimal inclusion criterion in the context of visualization.</p> <p>Results</p> <p>We present the projection score, which is a straightforward, intuitively appealing measure of the informativeness of a variable subset with respect to PCA visualization. This measure can be universally applied to find suitable inclusion criteria for any type of variable filtering. We apply the presented measure to find optimal variable subsets for different filtering methods in both microarray data sets and synthetic data sets. We note also that the projection score can be applied in general contexts, to compare the informativeness of any variable subsets with respect to visualization by PCA.</p> <p>Conclusions</p> <p>We conclude that the projection score provides an easily interpretable and universally applicable measure of the informativeness of a variable subset with respect to visualization by PCA, that can be used to systematically find the most interpretable PCA visualization in practical exploratory analysis.</p

    The Wasteland of Random Supergravities

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    We show that in a general \cal{N} = 1 supergravity with N \gg 1 scalar fields, an exponentially small fraction of the de Sitter critical points are metastable vacua. Taking the superpotential and Kahler potential to be random functions, we construct a random matrix model for the Hessian matrix, which is well-approximated by the sum of a Wigner matrix and two Wishart matrices. We compute the eigenvalue spectrum analytically from the free convolution of the constituent spectra and find that in typical configurations, a significant fraction of the eigenvalues are negative. Building on the Tracy-Widom law governing fluctuations of extreme eigenvalues, we determine the probability P of a large fluctuation in which all the eigenvalues become positive. Strong eigenvalue repulsion makes this extremely unlikely: we find P \propto exp(-c N^p), with c, p being constants. For generic critical points we find p \approx 1.5, while for approximately-supersymmetric critical points, p \approx 1.3. Our results have significant implications for the counting of de Sitter vacua in string theory, but the number of vacua remains vast.Comment: 39 pages, 9 figures; v2: fixed typos, added refs and clarification

    Reconstitution of neutral amino acid transport from partially purified membrane components from ehrlich ascites tumor cells

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    Solubilized protein fractions have been obtained from plasma membranes of Ehrlich ascites cells either by extraction with 0.5% Triton X-100 or by extraction with 2% cholate. Partial purification of the solubilized protein fraction has been obtained by utilizing a combination of ammonium sulfate precipitation and column chromatography. Leucine-binding activity has been detected in the Triton X-100 solubilized membrane fraction. The leucine-binding activity was measured by equilibrium dialysis and was saturable with high levels of leucine or phenylalanine and is not strongly effected by alanine. These properties are similar to those previously identified as System L. In addition, the cholate extracted protein fraction was partially purified and reconstituted into liposomes. Sodium dependent uptake of alanine and leucine could be demonstrated in the reconstituted vesicles. Concentrative uptake was dependent upon a sodium gradient. A membrane potential produced by valinomycin mediated potassium diffusion in the presence of sodium also stimulated amino acid transport in reconstituted liposomes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/38206/1/400070317_ftp.pd

    Wavelet Cycle Spinning Denoising of NDE Ultrasonic Signals Using a Random Selection of Shifts

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    Wavelets are a powerful tool for signal and image denoising. Most of the denoising applications in different fields were based on the thresholding of the discrete wavelet transform (DWT) coefficients. Nevertheless, DWT transform is not a time or shift invariant transform and results depend on the selected shift. Improvements on the denoising performance can be obtained using the stationary wavelet transform (SWT) (also called shift-invariant or undecimated wavelet transform). Denoising using SWT has previously shown a robust and usually better performance than denoising using DWT but with a higher computational cost. In this paper, wavelet shrinkage schemes are applied for reducing noise in synthetic and experimental non-destructive evaluation ultrasonic A-scans, using DWT and a cycle-spinning implementation of SWT. A new denoising procedure, which we call random partial cycle spinning (RPCS), is presented. It is based on a cycle-spinning over a limited number of shifts that are selected in a random way. Wavelet denoising based on DWT, SWT and RPCS have been applied to the same sets of ultrasonic A-scans and their performances in terms of SNR are compared. In all cases three well known threshold selection rules (Universal, Minimax and Sure), with decomposition level dependent selection, have been used. It is shown that the new procedure provides a good robust denoising performance, without the DWT fluctuating performance, and close to SWT but with a much lower computational cost.This work was partially supported by Spanish MCI Project DPI2011-22438San Emeterio Prieto, JL.; Rodríguez-Hernández, MA. (2015). Wavelet Cycle Spinning Denoising of NDE Ultrasonic Signals Using a Random Selection of Shifts. 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    Estimating a threshold price for CO2 emissions of buildings to improve their energy performance level. Case study of a new Spanish home

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    Energy consumption in homes produces CO2. In many countries, building regulations are being set to enable energy efficiency performance levels to be issued. In Spain, there is a regulated procedure to certify the energy performance of buildings according to their CO2 emissions. Consequently, some software tools have been design to simulate buildings and to obtain their energy consumption and CO2 emissions. In this paper the investment, maintenance and energy consumption costs are calculated for different energy performance levels and for various climatic zones, in a single-family home. According to the results, more energy efficient buildings imply higher construction and maintenance costs, which are not compensated by lower energy costs. Therefore, under current conditions, economic criteria do not support the improvement of the energy efficiency of a dwelling. Among the possible measures to promote energy efficiency, a price on CO2 emissions is to be suggested, including the social cost in the analysis. For this purpose, the cost-optimal methodology is used. In different scenarios for the discount rate y energy prices, various prices for CO2 are obtained, depending on the climatic zone and energy performance level.Ruá Aguilar, MJ.; Guadalajara Olmeda, MN. (2015). Estimating a threshold price for CO2 emissions of buildings to improve their energy performance level. Case study of a new Spanish home. Energy Efficiency. 8(2):183-203. doi:10.1007/s12053-014-9286-2S18320382AICIA. (2009). Escala de calificación energética. Edificios de nueva construcción. Madrid: Instituto para la Diversificación y Ahorro de la Energía, Ministerio de Industria, Turismo y Comercio.Al-Homoud, M. S. (2005). Performance characteristics and practical applications of common building thermal insulation materials. Building and Environment, 40(3), 353–360.Amecke, H. 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    Diabetes and pregnancy:national trends over a 15 year period

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    Aims/hypothesis: We aimed to examine time trends in national perinatal outcomes in pregnancies complicated by pre-existing type 1 or type 2 diabetes. Methods: We analysed episode-level data on all obstetric inpatient delivery events (live or stillbirth) between 1 April 1998 and 31 March 2013 (n = 813,921) using the Scottish Morbidity Record (SMR02). Pregnancies to mothers with type 1 (n = 3229) and type 2 (n = 1452) diabetes were identified from the national diabetes database (Scottish Care Information-Diabetes), and perinatal outcomes were compared among women with type 1 diabetes, type 2 diabetes and those without diabetes. Results: The number of pregnancies complicated by diabetes increased significantly, by 44% in type 1 diabetes and 90% in type 2 diabetes, across the 15 years examined, to rates of 1 in 210 and 1 in 504 deliveries, respectively. Compared with women without diabetes, delivery occurred 2.6 weeks earlier (type 1 diabetes 36.7 ± 2.3 weeks) and 2 weeks earlier (type 2 diabetes 37.3 ± 2.4 weeks), respectively, showing significant reductions for both type 1 (from 36.7 weeks to 36.4 weeks, p = 0.03) and type 2 (from 38.0 weeks to 37.2 weeks, p &lt; 0.001) diabetes across the time period. The proportions of preterm delivery were markedly increased in women with diabetes (35.3% type 1 diabetes, 21.8% type 2 diabetes, 6.1% without diabetes; p &lt; 0.0001), and these proportions increased with time for both groups (p &lt; 0.005). Proportions of elective Caesarean sections (29.4% type 1 diabetes, 30.5% type 2 diabetes, 9.6% without diabetes) and emergency Caesarean sections (38.3% type 1 diabetes, 29.1% type 2 diabetes, 14.6% without diabetes) were greatly increased in women with diabetes and increased over time except for stable rates of emergency Caesarean section in type 1 diabetes. Gestational age-, sex- and parity-adjusted z score for birthweight (1.33 ± 1.34; p &lt; 0.001) were higher in type 1 diabetes and increased over time from 1.22 to 1.47 (p &lt; 0.001). Birthweight was also increased in type 2 diabetes (0.94 ± 1.34; p &lt; 0.001) but did not alter with time. There were 65 perinatal deaths in offspring of mothers with type 1 diabetes and 39 to mothers with type 2 diabetes, representing perinatal mortality rates of 20.1 (95% CI 14.7, 24.3) and 26.9 (16.7, 32.9) per 1000 births, respectively, and rates 3.1 and 4.2 times, respectively, those observed in the non-diabetic population (p &lt; 0.001). Stillbirth rates in type 1 and type 2 diabetes were 4.0-fold and 5.1-fold that in the non-diabetic population (p &lt; 0.001). Perinatal mortality and stillbirth rates showed no significant fall over time despite small falls in the rates for the non-diabetic population. Conclusions/interpretation: Women with diabetes are receiving increased intervention in pregnancy (earlier delivery, increased Caesarean section rates), but despite this, higher birthweights are being recorded. Improvements in rates of stillbirth seen in the general population are not being reflected in changes in stillbirth or perinatal mortality in our population with diabetes

    Bayesian Wavelet Shrinkage of the Haar-Fisz Transformed Wavelet Periodogram.

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    It is increasingly being realised that many real world time series are not stationary and exhibit evolving second-order autocovariance or spectral structure. This article introduces a Bayesian approach for modelling the evolving wavelet spectrum of a locally stationary wavelet time series. Our new method works by combining the advantages of a Haar-Fisz transformed spectrum with a simple, but powerful, Bayesian wavelet shrinkage method. Our new method produces excellent and stable spectral estimates and this is demonstrated via simulated data and on differenced infant electrocardiogram data. A major additional benefit of the Bayesian paradigm is that we obtain rigorous and useful credible intervals of the evolving spectral structure. We show how the Bayesian credible intervals provide extra insight into the infant electrocardiogram data
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