Some tests for the covariance matrix with fewer observations than the dimension under non-normality

This article analyzes whether some existing tests for the p×p covariance matrix Σ of the N independent identically distributed observation vectors work under non-normality. We focus on three hypotheses testing problems: (1) testing for sphericity, that is, the covariance matrix Σ is proportional to an identity matrix I_p; (2) the covariance matrix Σ is an identity matrix I_p; and (3) the covariance matrix is a diagonal matrix. It is shown that the tests proposed by Srivastava (2005) for the above three problems are robust under the non-normality assumption made in this article irrespective of whether N≤p or N≥p, but (N,p)→∞, and N/p may go to zero or infinity. Results are asymptotic and it may be noted that they may not hold for finite (N,p).     

Conditional information criteria for selecting variables in linear mixed models

In this paper, we consider the problem of selecting the variables of the fixed effects in the linear mixed models where the random effects are present and the observation vectors have been obtained from many clusters. As the variable selection procedure, here we use the Akaike Information Criterion, AIC. In the context of the mixed linear models, two kinds of AIC have been proposed: marginal AIC and conditional AIC. In this paper, we derive three versions of conditional AIC depending upon different estimators of the regression coefficients and the random effects. Through the simulation studies, it is shown that the proposed conditional AIC’s are superior to the marginal and conditional AIC’s proposed in the literature in the sense of selecting the true model. Finally, the results are extended to the case when the random effects in all the clusters are of the same dimension but have a common unknown covariance matrix.     

Voltage- and Power-Conversion Performance of Bi-functional ZrO2 : Er3+/ Yb3+ Assisted and Co-sensitized Dye Sensitized Solar Cells for Internet of Things Applications

Giant power conversion efficiency is achieved by using bifunction ZrO₂ : Er³⁺/Yb³⁺assisted co-sensitised dye-sensitized solar cells. The evolution of the crystalline structure and its microstructure are examined by X-ray diffraction, scanning electron microscopy studies. The bi-functional behaviour of ZrO₂ : Er³⁺/Yb³⁺ as upconversion, light scattering is confirmed by emission and diffused reflectance studies. The bi-function ZrO₂ : Er³⁺/Yb³⁺ (pH=3) assisted photoanode is co-sensitized by use of N719 dye, squaraine SPSQ2 dye and is sandwiched with Platinum based counter electrode. The fabricated DSSC exhibited a giant power conversion efficiency of 12.35 % with V_OC of 0.71 V, J_SC of 27.06 mA/cm², FF of 0.63. The results, which motivated the development of a small DSSC module, gave 6.21 % and is used to drive a tiny electronic motor in indoor and outdoor lighting conditions. Small-area DSSCs connected in series have found that a V_OC of 4.52 V is sufficient to power up Internet of Things (IoT) devices.     

Evolution of fuzzy classifiers using genetic programming

In this paper, we propose a genetic programming (GP) based approach to evolve fuzzy rule based classifiers. For a c-class problem, a classifier consists of c trees. Each tree, T _i , of the multi-tree classifier represents a set of rules for class i. During the evolutionary process, the inaccurate/inactive rules of the initial set of rules are removed by a cleaning scheme. This allows good rules to sustain and that eventually determines the number of rules. In the beginning, our GP scheme uses a randomly selected subset of features and then evolves the features to be used in each rule. The initial rules are constructed using prototypes, which are generated randomly as well as by the fuzzy k-means (FKM) algorithm. Besides, experiments are conducted in three different ways: Using only randomly generated rules, using a mixture of randomly generated rules and FKM prototype based rules, and with exclusively FKM prototype based rules. The performance of the classifiers is comparable irrespective of the type of initial rules. This emphasizes the novelty of the proposed evolutionary scheme. In this context, we propose a new mutation operation to alter the rule parameters. The GP scheme optimizes the structure of rules as well as the parameters involved. The method is validated on six benchmark data sets and the performance of the proposed scheme is found to be satisfactory.     

Multivariate analysis of variance with fewer observations than the dimension

In this article, we consider the problem of testing a linear hypothesis in a multivariate linear regression model which includes the case of testing the equality of mean vectors of several multivariate normal populations with common covariance matrix Σ, the so-called multivariate analysis of variance or MANOVA problem. However, we have fewer observations than the dimension of the random vectors. Two tests are proposed and their asymptotic distributions under the hypothesis as well as under the alternatives are given under some mild conditions. A theoretical comparison of these powers is made.     

An average discrepancy for optimal vertex‐modified number‐theoretic rules

Number‐theoretic rules are particularly suited for the approximation of multidimensional integrals in which the integrands are periodic. When the integrands are not periodic, then a vertex‐modified variant has been proposed. Error bounds for such vertex‐modified rules may be obtained in terms of the L ₂ discrepancy. In s dimensions these vertex‐modified rules contain 2^s weights which may be chosen optimally so that the discrepancy is minimized. We obtain an expression for the squared L ₂ discrepancy of optimal vertex‐modified rules. This expression is used to derive an expression for the average of the squared L ₂ discrepancy for optimal vertex‐modified number‐theoretic rules. Values of this average are then compared with the corresponding average for normal number‐theoretic rules and the expected value for Monte Carlo rules. This revised version was published online in June 2006 with corrections to the Cover Date.     

Multiple imputation and other resampling schemes for imputing missing observations

The problem of imputing missing observations under the linear regression model is considered. It is assumed that observations are missing at random and all the observations on the auxiliary or independent variables are available. Estimates of the regression parameters based on singly and multiply imputed values are given. Jackknife as well as bootstrap estimates of the variance of the singly imputed estimator of the regression parameters are given. These estimators are shown to be consistent estimators. The asymptotic distributions of the imputed estimators are also given to obtain interval estimates of the parameters of interest. These interval estimates are then compared with the interval estimates obtained from multiple imputation. It is shown that singly imputed estimators perform at least as good as multiply imputed estimators. A new nonparametric multiply imputed estimator is proposed and shown to perform as good as a multiply imputed estimator under normality. The singly imputed estimator, however, still remains at least as good as a multiply imputed estimator.     

Insiemi irriducibili di seminorme e spazi localmente convessi

Sunto. La nozione di insieme irriducibile di seminorme secondo F. Treves, viene ripresa ed utilizzata per studiare in una nuova prospettiva la struttura degli spazi localmente convessi. Entrata in Redazione il 17 luglio 1971. Lavoro eseguito nell'ambito delle attività del Gruppo Nazionale per l'Analisi Funzionale e sue Applicazioni (G.N.A.F.A.) del C.N.R.     

Estimation of the precision matrix of a singular Wishart distribution and its application in high-dimensional data

In this article, the Stein-Haff identity is established for a singular Wishart distribution with a positive definite mean matrix but with the dimension larger than the degrees of freedom. This identity is then used to obtain estimators of the precision matrix improving on the estimator based on the Moore-Penrose inverse of the Wishart matrix under the Efron-Morris loss function and its variants. Ridge-type empirical Bayes estimators of the precision matrix are also given and their dominance properties over the usual one are shown using this identity. Finally, these precision estimators are used in a quadratic discriminant rule, and it is shown through simulation that discriminant methods based on the ridge-type empirical Bayes estimators provide higher correct classification rates.