Comparing diagnostic tests with missing data

When missing data occur in studies designed to compare the accuracy of diagnostic tests, a common, though naive, practice is to base the comparison of sensitivity, specificity, as well as of positive and negative predictive values on some subset of the data that fits into methods implemented in standard statistical packages. Such methods are usually valid only under the strong missing completely at random (MCAR) assumption and may generate biased and less precise estimates. We review some models that use the dependence structure of the completely observed cases to incorporate the information of the partially categorized observations into the analysis and show how they may be fitted via a two-stage hybrid process involving maximum likelihood in the first stage and weighted least squares in the second. We indicate how computational subroutines written in R may be used to fit the proposed models and illustrate the different analysis strategies with observational data collected to compare the accuracy of three distinct non-invasive diagnostic methods for endometriosis. The results indicate that even when the MCAR assumption is plausible, the naive partial analyses should be avoided.

Hyperfunctions and (analytic) hypoellipticity

In this work we discuss the problem of smooth and analytic regularity for hyperfunction solutions to linear partial differential equations with analytic coefficients. In particular we show that some well known ""sum of squares"" operators, which satisfy Hormander`s condition and consequently are hypoelliptic, admit hyperfunction solutions that are not smooth (in particular they are not distributions).

A family of Schroeder-Bernstein type theorems for Banach spaces

Motivated by a characterization of the complemented subspaces in Banach spaces X isomorphic to their squares X-2, we introduce the concept of P-complemented subspaces in Banach spaces. In this way, the well-known Pelczynski`s decomposition method can be seen as a Schroeder-Bernstein type theorem. Then, we give a complete description of the Schroeder-Bernstein type theorems for this new notion of complementability. By contrast, some very elementary questions on P-complementability are refinements of the Square-Cube Problem closely connected with some Banach spaces introduced by W.T. Gowers and B. Maurey in 1997. (C) 2007 Elsevier Inc. All rights reserved.

Bias correction in a multivariate normal regression model with general parameterization

This paper derives the second-order biases Of maximum likelihood estimates from a multivariate normal model where the mean vector and the covariance matrix have parameters in common. We show that the second order bias can always be obtained by means of ordinary weighted least-squares regressions. We conduct simulation studies which indicate that the bias correction scheme yields nearly unbiased estimators. (C) 2009 Elsevier B.V. All rights reserved.

The reduction of Cu(II)/neocuproine complexes by some polyphenols: Total polyphenols determination in wine samples

A new method is presented for spectrophotometric determination of total polyphenols content in wine. The procedure is a modified CUPRAC method based on the reduction of Cu(II), in hydroethanolic medium (pH 7.0) in the presence of neocuproine (2,9-dimethyl-1,10-phenanthroline), by polyphenols, yielding a Cu(I) complexes with maximum absorption peak at 450 nm. The absorbance values are linear (r = 0.998, n = 6) with tannic acid concentrations from 0.4 to 3.6 mu mol L(-1). The limit of detection obtained was 0.41 mu mol L(-1) and relative standard deviation 1.2% (1 mu mol L(-1); n = 8). Recoveries between 80% and 110% (mean value of 95%) were calculated for total polyphenols determination in 14 commercials and 2 synthetic wine samples (with and without sulphite). The proposed procedure is about 1.5 more sensitive than the official Folin-Ciocalteu method. The sensitivities of both methods were compared by the analytical responses of several polyphenols tested in each method. (C) 2010 Elsevier Ltd. All rights reserved.

Cellulose swelling by protic solvents: which properties of the biopolymer and the solvent matter?

The question posed in the title has been addressed by studying the swelling of celluloses at 20 C by twenty protic solvents, including water; linear- and branched-chain aliphatic alcohols; unsaturated aliphatic alcohols, and alkoxyalcohols. The biopolymers investigated included microcrystalline cellulose, MC, native and never-dried mercerized cotton cellulose, cotton and M-cotton, and native and never-dried mercerized eucalyptus cellulose, eucalyptus and M-eucalyptus, respectively. In most cases, better correlations with the physico-chemical properties of the solvents were obtained when the swelling was expressed as number of moles of solvent/anhydroglucose unit, nSw, rather than as % increase in sample weight. The descriptors employed in these correlations included, where available, Hildebrand`s solubility parameters, Gutmann`s acceptor and donor numbers, solvent molar volume, V(S), as well as solvatochromic parameters. The latter, employed for the first time for correlating the swelling of biopolymers, included empirical solvent polarity, E(T)(30), solvent ""acidity"", alpha(S), ""basicity"", beta(S), and dipolarity/polarizability, pi(S)*, respectively. Small regression coefficients and large sums of the squares of the residues were obtained when values of nSw were correlated with two solvent parameters. Much better correlations were obtained with three solvent parameters. The most statistically significant descriptor in the correlation equation depends on the cellulose, being pi(S)* for MC, cotton, and eucalyptus, and V(S) for M-cotton and M-eucalyptus. The best correlations were obtained with the same set of four parameters for all celluloses, namely, solvent pKa (or alpha(S)) beta(S), pi(S)*, and V(S), respectively. These results indicate that the supra-molecular structure of the biopolymer, in particular the average sizes of crystallites and micro-pores, and the presence of its chains in parallel (cellulose I) or anti-parallel (cellulose II) arrangements control its swelling. At least for the present biopolymer/solvent systems, use of solvatochromic parameters is a superior alternative to Hildebrand`s solubility parameters and/or Gutmann`s acceptor and donor numbers. The relevance of these results to the accessibility of the hydroxyl groups of cellulose, hence to its reactivity, is briefly discussed.

A quantum chemical study on a set of non-imidazole H(3) antihistamine molecules

Molecular orbital calculations were carried out on a set of 28 non-imidazole H(3) antihistamine compounds using the Hartree-Fock method in order to investigate the possible relationships between electronic structural properties and binding affinity for H3 receptors (pK(i)). It was observed that the frontier effective-for-reaction molecular orbital (FERMO) energies were better correlated with pK(i) values than highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy values. Exploratory data analysis through hierarchical cluster (HCA) and principal component analysis (PCA) showed a separation of the compounds in two sets, one grouping the molecules with high pK(i) values, the other gathering low pK(i) value compounds. This separation was obtained with the use of the following descriptors: FERMO energies (epsilon(FERMO)), charges derived from the electrostatic potential on the nitrogen atom (N(1)), electronic density indexes for FERMO on the N(1) atom (Sigma((FERMO))c(i)(2)). and electrophilicity (omega`). These electronic descriptors were used to construct a quantitative structure-activity relationship (QSAR) model through the partial least-squares (PLS) method with three principal components. This model generated Q(2) = 0.88 and R(2) = 0.927 values obtained from a training set and external validation of 23 and 5 molecules, respectively. After the analysis of the PLS regression equation and the values for the selected electronic descriptors, it is suggested that high values of FERMO energies and of Sigma((FERMO))c(i)(2), together with low values of electrophilicity and pronounced negative charges on N(1) appear as desirable properties for the conception of new molecules which might have high binding affinity. 2010 Elsevier Inc. All rights reserved.

Inlärning och öppet-fält aktivitet hos förskolebarn

The purpose of this study was to analyze the impact of learning on open-field activity among pre-school children varying from 3 to 5 years old. Altogether 25 children, 13 girls and 12 boys, entered the test from three different preschools in Dalarna. Six of these children represented the control group. The children were asked to learn 2 tasks, 1 visual memory task and 1 spatial constructing-kit task. Before, between and after the tasks, the children were allowed to move freely in the open field. The control group did not solve any learning tasks and only entered the open field, which was divided into 18 equally large squares, where the children’s activities were observed. The children’s learning times as well as their spontaneous open-field activity and wall-seeking behaviour were registered. The result showed as a general rule that the learning time was reduced between each session. However, the visual memory task increased the children’s spontaneous open-field behaviour more and decreased their wall seeking to a greater extent than the spatial-construction learning task.

Appropriate choice of aggregation operators in fuzzy decision support systems

Fuzzy logic provides a mathematical formalism for a unified treatment of vagueness and imprecision that are ever present in decision support and expert systems in many areas. The choice of aggregation operators is crucial to the behavior of the system that is intended to mimic human decision making. This paper discusses how aggregation operators can be selected and adjusted to fit empirical data—a series of test cases. Both parametric and nonparametric regression are considered and compared. A practical application of the proposed methods to electronic implementation of clinical guidelines is presented

Prediction error property of the Lasso Estimator and its generalization

The lasso procedure is an estimator-shrinkage and variable selection method. This paper shows that there always exists an interval of tuning parameter values such that the corresponding mean squared prediction error for the lasso estimator is smaller than for the ordinary least squares estimator. For an estimator satisfying some condition such as unbiasedness, the paper defines the corresponding generalized lasso estimator. Its mean squared prediction error is shown to be smaller than that of the estimator for values of the tuning parameter in some interval. This implies that all unbiased estimators are not admissible. Simulation results for five models support the theoretical results.

Improving the performance of equalization in communication systems

In this paper, research on exploring the potential of several popular equalization techniques while overcoming their disadvantages has been conducted. First, extensive literature survey on equalization is conducted. The focus has been placed on several popular linear equalization algorithm such as the conventional least-mean-square (LMS) algorithm, the recursive least squares (RLS) algorithm, the fi1tered-X LMS algorithm and their development. The approach in analysing the performance of the filtered-X LMS Algorithm, a heuristic method based on linear time-invariant operator theory is provided to analyse the robust perfonnance of the filtered-X structure. It indicates that the extra filter could enhance the stability margin of the corresponding non filtered X structure. To overcome the slow convergence problem while keeping the simplicity of the LMS based algorithms, an H2 optimal initialization is proposed.

Methods of construction of OWA operators from data

This paper investigates the problem of obtaining the weights of the ordered weighted aggregation (OWA) operators from observations. The problem is formulated as a restricted least squares and uniform approximation problems. We take full advantage of the linearity of the problem. In the former case, a well known technique of non-negative least squares is used. In a case of uniform approximation, we employ a recently developed cutting angle method of global optimisation. Both presented methods give results superior to earlier approaches, and do not require complicated nonlinear constructions. Additional restrictions, such as degree of orness of the operator, can be easily introduced

A new method for locating the global optimum : application of the cutting angle method to molecular structure prediction

Many problems in chemistry depend on the ability to identify the global minimum or maximum of a function. Examples include applications in chemometrics, optimization of reaction or operating conditions, and non-linear least-squares analysis. This paper presents the results of the application of a new method of deterministic global optimization, called the cutting angle method (CAM), as applied to the prediction of molecular geometries. CAM is shown to be competitive with other global optimization techniques for several benchmark molecular conformation problem. CAM is a general method that can also be applied to other computational problems involving global minima, global maxima or finding the roots of nonlinear equations.

Application of fast and robust equalization in communication technology

In this paper, the authors explore the potential of several popular equalization techniques while overcoming their disadvantages. First, extensive literature survey on equalization is conducted. The focus is on popular linear equalization algorithms such as the conventional least-mean-square (LMS) algorithm , The recursive least-squares (RLS) algorithm, the filtered-X LMS algorithm and their development. To overcome the slow convergence problem while keeping the simplicity of the LMS based algorithms, an H2 optimal initialization is proposed.

Institutional ownership and firm performance: evidence from Finland

This paper investigates the relationship between different classes of institutional investors and firm performance. Using industry level data from Finland, which is characterized by various institutional investors who own multiple ownership stakes in different firms across a broad spectrum of industries, the paper exhibits two novelties. First, unlike previous studies which treated institutional investors as a monolithic group, we segment them in classes. Second, we recognize the joint determination of firm performance and institutional ownership. We account for this issue in the context of a system of equations, using three stage least squares methodology. The empirical results suggest a significant two-way feedback between firm performance and institutional equity ownership. However, this effect is not symmetric. We find that institutional investors with likely investment and business ties with firms have adverse (negative) effect on firm performance and the impact is very significant in comparison to the negative effect of firm performance on institutional ownership.