Hydride generation using a metallic atomizer after microwave-assisted extraction for inorganic arsenic speciation in biological samples

AbstractThe present speciation method reports the determination of inorganic arsenic forms, using metallic furnace hydride generation atomic absorption spectrometry. The inorganic As speciation is carried out using mild conditions for hydride formation, such as slightly acid pH media (4.50) and low tetrahydridoborate(1−) concentration (0.1% (w/v)). Limits of detection and quantification of 2.0 and 6.6μgL−1 of iAs(III) are obtained using optimized conditions. Additionally, microwave-assisted extraction using water as solvent is carried out to provide the appropriate environment for As species extraction as well as impeding inter-conversion between species. With these analytical strategies, As was accurately determined (at 99.9% confidence level) in water and plankton samples

Using the Bootstrap to test for symmetry under unknown dependence

This paper considers tests for symmetry of the one-dimensional marginal distribution of fractionally integrated processes. The tests are implemented by using an autoregressive sieve bootstrap approximation to the null sampling distribution of the relevant test statistics. The sieve bootstrap allows inference on symmetry to be carried out without knowledge of either the memory parameter of the data or of the appropriate norming factor for the test statistic and its asymptotic distribution. The small-sample properties of the proposed method are examined by means of Monte Carlo experiments, and applications to real-world data are also presented

Frequentist and Bayesian measures of confidence via multiscale bootstrap for testing three regions

A new computation method of frequentist $p$-values and Bayesian posterior probabilities based on the bootstrap probability is discussed for the multivariate normal model with unknown expectation parameter vector. The null hypothesis is represented as an arbitrary-shaped region. We introduce new parametric models for the scaling-law of bootstrap probability so that the multiscale bootstrap method, which was designed for one-sided test, can also computes confidence measures of two-sided test, extending applicability to a wider class of hypotheses. Parameter estimation is improved by the two-step multiscale bootstrap and also by including higher-order terms. Model selection is important not only as a motivating application of our method, but also as an essential ingredient in the method. A compromise between frequentist and Bayesian is attempted by showing that the Bayesian posterior probability with an noninformative prior is interpreted as a frequentist $p$-value of ``zero-sided'' test

How to best sample a periodic probability distribution, or on the accuracy of Hamiltonian finding strategies

Projective measurements of a single two-level quantum mechanical system (a qubit) evolving under a time-independent Hamiltonian produce a probability distribution that is periodic in the evolution time. The period of this distribution is an important parameter in the Hamiltonian. Here, we explore how to design experiments so as to minimize error in the estimation of this parameter. While it has been shown that useful results may be obtained by minimizing the risk incurred by each experiment, such an approach is computationally intractable in general. Here, we motivate and derive heuristic strategies for experiment design that enjoy the same exponential scaling as fully optimized strategies. We then discuss generalizations to the case of finite relaxation times, T_2 < \infty.Comment: 7 pages, 2 figures, 3 appendices; Quantum Information Processing, Online First, 20 April 201

Markov basis and Groebner basis of Segre-Veronese configuration for testing independence in group-wise selections

We consider testing independence in group-wise selections with some restrictions on combinations of choices. We present models for frequency data of selections for which it is easy to perform conditional tests by Markov chain Monte Carlo (MCMC) methods. When the restrictions on the combinations can be described in terms of a Segre-Veronese configuration, an explicit form of a Gr\"obner basis consisting of moves of degree two is readily available for performing a Markov chain. We illustrate our setting with the National Center Test for university entrance examinations in Japan. We also apply our method to testing independence hypotheses involving genotypes at more than one locus or haplotypes of alleles on the same chromosome.Comment: 25 pages, 5 figure

Factor copula models for item response data

Factor or conditional independence models based on copulas are proposed for multivariate discrete data such as item responses. The factor copula models have interpretations of latent maxima/minima (in comparison with latent means) and can lead to more probability in the joint upper or lower tail compared with factor models based on the discretized multivariate normal distribution (or multidimensional normal ogive model). Details on maximum likelihood estimation of parameters for the factor copula model are given, as well as analysis of the behavior of the log-likelihood. Our general methodology is illustrated with several item response data sets, and it is shown that there is a substantial improvement on existing models both conceptually and in fit to data

Comparison of structural transformations and superconductivity in compressed Sulfur and Selenium

Density-functional calculations are presented for high-pressure structural phases of S and Se. The structural phase diagrams, phonon spectra, electron-phonon coupling, and superconducting properties of the isovalent elements are compared. We find that with increasing pressure, Se adopts a sequence of ever more closely packed structures (beta-Po, bcc, fcc), while S favors more open structures (beta-Po, simple cubic, bcc). These differences are shown to be attributable to differences in the S and Se core states. All the compressed phases of S and Se considered are calculated to have weak to moderate electron-phonon coupling strengths consistent with superconducting transition temperatures in the range of 1 to 20 K. Our results compare well with experimental data on the beta-Po --> bcc transition pressure in Se and on the superconducting transition temperature in beta-Po S. Further experiments are suggested to search for the other structural phases predicted at higher pressures and to test theoretical results on the electron-phonon interaction and superconducting properties

Relationships Between Archimedean Copulas and Morgenstern Utility Functions

The (additive) generator of an Archimedean copula is a strictly decreasing and convexfunction, while Morgenstern utility functions (applying to risk aversion decision makers) arenondecreasing and concave. In this presentation, relationships between generators and utilityfunctions are established. For some well known Archimedean copula families, links betweenthe generator and the corresponding utility function are demonstrated.Some new copulafamilies are derived from classes of utility functions which appeared in the literature, andtheir properties are discussed. It is shown how dependence properties of an Archimedeancopula translate into properties of the utility function from whichthey are constructed

Towards Machine Wald

The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed \emph{by humans} because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to \emph{think} as \emph{humans} have the ability to do when faced with uncertainty is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tend to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with Decision Theory, Machine Learning, Bayesian Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty Quantification and Information Based Complexity.Comment: 37 page