A compound class of Weibull and power series distributions

In this paper we introduce the Weibull power series (WPS) class of distributions which is obtained by compounding Weibull and power series distributions where the compounding procedure follows same way that was previously carried out by Adamidis and Loukas (1998) This new class of distributions has as a particular case the two-parameter exponential power series (EPS) class of distributions (Chahkandi and Gawk 2009) which contains several lifetime models such as exponential geometric (Adamidis and Loukas 1998) exponential Poisson (Kus 2007) and exponential logarithmic (Tahmasbi and Rezaei 2008) distributions The hazard function of our class can be increasing decreasing and upside down bathtub shaped among others while the hazard function of an EPS distribution is only decreasing We obtain several properties of the WPS distributions such as moments order statistics estimation by maximum likelihood and inference for a large sample Furthermore the EM algorithm is also used to determine the maximum likelihood estimates of the parameters and we discuss maximum entropy characterizations under suitable constraints Special distributions are studied in some detail Applications to two real data sets are given to show the flexibility and potentiality of the new class of distributions (C) 2010 Elsevier B V All rights reserved

Evidences for charge-transfer complex formation in the benzene adsorption on sulfated TiO2-a resonance Raman spectroscopy investigation

Benzene adsorbed on highly acidic sulfated TiO2 (S-TiO2) shows an intriguing resonance Raman (RR) effect, with excitation in the blue-violet region. There are very interesting spectral features: the preferential enhancement of the e(2g) mode (1595 cm(-1)) in relation to the a(1g) mode (ring-breathing mode at 995 cm(-1)) and the appearance of bands at 1565 and 1514 cm(-1). The band at 1565 cm(-1) is probably one of the components of the e(2g) split band, originally a doubly degenerate mode (8a, 8b) in neat benzene, and the band at 1514 cm(-1) is assigned to the 19a mode, an inactive mode in neat benzene. These facts indicate a lowering of symmetry in adsorbed benzene, which may be caused by a strong interaction between S-TiO2 and the benzene molecule with formation of a benzene to Ti (IV) charge transfer (CT) complex or by the formation of a benzene radical cation species. However, the RR spectra of the adsorbed benzene cannot be assigned to the benzene radical cation because the observed wavenumber of the ring-breathing mode does not have the value expected for this species. Moreover, it was found by ESR measurements that the amount of radicals was very low, and so it was concluded that a CT complex is the species that originates the RR spectra. The most favorable intensification of the band at 1595 cm(-1) in the RR spectra of benzene/S-TiO2 at higher excitation energy corroborates this hypothesis, as an absorption band in this energy range, assigned to a CT transition, is observed. Copyright (C) 2008 John Wiley & Sons, Ltd.

Thermo-solvatochromism of merocyanine polarity probes - What are the consequences of increasing probe lipophilicity through annelation?

The question raised in the title has been answered by comparing the solvatochromism of two series of polarity probes, the lipophilicities of which were increased either by increasing the length of an alkyl group (R) attached to a fixed pyridine-based structure or through annelation (i.e., by fusing benzene rings onto a central pyridine-based structure). The following novel solvatochromic probes were synthesized: 2,6-dibromo-4-[(E)-2-(1-methylquinolinium-4-yl)ethenyl]-phenolate (MeQMBr(2)) and 2,6-dibromo-4-[(E)-2-(1-methyl-acridinium-4- yl) ethenyl)]phenolate (MeAMBr(2) The solvatochromic behavior of these probes, along with that of 2,6dibromo-4-[(E)-2-(1-methylpyridinium-4-yl)ethenyl]phenol-ate(MePMBr(2)) was analyzed in terms of increasing probe lipophilicity, through annelation. Values of the empirical solvent polarity scale [E(T)(MePMBr(2))] in kcalmol(-1) correlated linearly with ET(30), the corresponding values for the extensively employed probe 2,6-diphenyl-4-(2,4,6-triphenylpyridinium-1-yl)phenolate (RB). On the other hand, the nonlinear correlations of ET(MeQMBr(2)) or ET(MeAMBr(2)) with E(T)(30) are described by second-order polynomials. Possible reasons for this behavior include: i) self-aggregation of the probe, ii) photoinduced cis/trans isomerization of the dye, and iii) probe structure- and solvent-dependent contributions of the quinonoid and zwitterionic limiting formulas to the ground and excited states of the probe. We show that mechanisms (i) and (ii) are not operative under the experimental conditions employed; experimental evidence (NMR) and theoretical calculations are presented to support the conjecture that the length of the central ethenylic bond in the dye increases in the order MeAMBr(2) > MeQMBr(2) > MePMBr(2), That is, the contribution of the zwitterionic limiting formula predominates for the latter probe, as is also the case for RB, this being the reason for the observed linear correlation between the ET(MePMBr2) and the ET(30) scales. The effect of increasing probe lipophilicity on solvatochromic behavior therefore depends on the strategy employed. Increasing the length of R affects solvatochromism much less than annelation, because the former structural change hardly perturbs the energy of the intramolecular charge-transfer transition responsible for solvatochromism. The thermo-solvatochromic behavior (effect of temperature on solvatochromism) of the three probes was studied in mixtures of water with propanol and/or with DMSO. The solvation model used explicitly considers the presence of three ""species"" in the system: bulk solution and probe solvation shell [namely, water (W), organic solvent (Solv)], and solvent-water hydrogen-bonded aggregate (Solv-W). For aqueous propanol, the probe is efficiently solvated by Solv-W; the strong interaction of DMSO with W drastically decreases the efficiency of Solv-W in solvating the probe, relative to its precursor solvents. Temperature increases resulted in desolvation of the probes, due to the concomitant reduction in the structured characters of the components of the binary mixtures.

Ion pair stabilization effects on a series of procaine structural analogs

In this work, a series of 10 structural procaine analogs have been synthesized in order to investigate the structural features affecting the stability of ion pair formation and its influence on the lipophilicity of ionizable compounds. The structural variation within this series was focused on the terminal nitrogen substituents and on the intermediate chain linkage nature. The hydrophobic parameters log P(n) and log P(i) (partition coefficient of the neutral and ionic species, respectively), as well as the ionization constants pK(a) and pK(a)(oct), were obtained from log D-pH profiles measured at pH values ranging from 2 to 12. The difference between log P(i) and log P(n) values (i.e. difflog P) of each prepared compound was considered a measure of the stability of ion pair formation. In this set, the difflog P values varied nearly over one log unit, ranging from -2.40 to -3.37. It has been observed that the presence of hydrogen bonding groups (especially donor) and low steric hindrance around the terminal amine ionizable group increases the relative lipophilicity of the ionic species as compared to the corresponding neutral species. These results were interpreted as due to the increased stability of ion pairs of the compounds bearing these structural features. (C) 2010 Elsevier B.V. All rights reserved.

Stabilizing Nonstationary Electrochemical Time Series

Electrochemical systems are ideal working-horses for studying oscillatory dynamics. Experimentally obtained time series, however, are usually associated with a spontaneous drift in some uncontrollable parameter that triggers transitions among different oscillatory patterns, despite the fact that all controllable parameters are kept constant. Herein we present an empirical method to stabilize experimental potential time series. The method consists of applying a negative galvanodynamic sweep to compensate the spontaneous drift and was tested for the oscillatory electro-oxidation of methanol on platinum. For a wide range of applied currents, the base system presents spontaneous transitions from quasi-harmonic to mixed mode oscillations. Temporal patterns were stabilized by galvanodynamic sweeps at different rates. The procedure resulted in a considerable increase in the number of oscillatory cycles from 5 to 20 times, depending on the specific temporal pattern. The spontaneous drift has been associated with uncompensated oscillations, in which the coverage of some adsorbed species are not reestablished after one cycle; i.e., there is a net accumulation and/or depletion of adsorbed species during oscillations. We interpreted the rate of the galvanodynamic sweep in terms of the time scales of the poisoning processes that underlies the uncompensated oscillations and thus the spontaneous slow drift.

Insights into the Molecular Requirements for the Anti-obesity Activity of a Series of CB1 Ligands

Two-dimensional and 3D quantitative structure-activity relationships studies were performed on a series of diarylpyridines that acts as cannabinoid receptor ligands by means of hologram quantitative structure-activity relationships and comparative molecular field analysis methods. The quantitative structure-activity relationships models were built using a data set of 52 CB1 ligands that can be used as anti-obesity agents. Significant correlation coefficients (hologram quantitative structure-activity relationships: r 2 = 0.91, q 2 = 0.78; comparative molecular field analysis: r 2 = 0.98, q 2 = 0.77) were obtained, indicating the potential of these 2D and 3D models for untested compounds. The models were then used to predict the potency of an external test set, and the predicted (calculated) values are in good agreement with the experimental results. The final quantitative structure-activity relationships models, along with the information obtained from 2D contribution maps and 3D contour maps, obtained in this study are useful tools for the design of novel CB1 ligands with improved anti-obesity potency.

Financial time series analysis : Chaos and neurodynamics approach

This work aims at combining the Chaos theory postulates and Artificial Neural Networks classification and predictive capability, in the field of financial time series prediction. Chaos theory, provides valuable qualitative and quantitative tools to decide on the predictability of a chaotic system. Quantitative measurements based on Chaos theory, are used, to decide a-priori whether a time series, or a portion of a time series is predictable, while Chaos theory based qualitative tools are used to provide further observations and analysis on the predictability, in cases where measurements provide negative answers. Phase space reconstruction is achieved by time delay embedding resulting in multiple embedded vectors. The cognitive approach suggested, is inspired by the capability of some chartists to predict the direction of an index by looking at the price time series. Thus, in this work, the calculation of the embedding dimension and the separation, in Takens‘ embedding theorem for phase space reconstruction, is not limited to False Nearest Neighbor, Differential Entropy or other specific method, rather, this work is interested in all embedding dimensions and separations that are regarded as different ways of looking at a time series by different chartists, based on their expectations. Prior to the prediction, the embedded vectors of the phase space are classified with Fuzzy-ART, then, for each class a back propagation Neural Network is trained to predict the last element of each vector, whereas all previous elements of a vector are used as features.

Do crude oil price changes affect economic growth of India, Pakistan and Bangladesh? : A multivariate time series analysis

This paper analyzes empirically the effect of crude oil price change on the economic growth of Indian-Subcontinent (India, Pakistan and Bangladesh). We use a multivariate Vector Autoregressive analysis followed by Wald Granger causality test and Impulse Response Function (IRF). Wald Granger causality test results show that only India’s economic growth is significantly affected when crude oil price decreases. Impact of crude oil price increase is insignificantly negative for all three countries during first year. In second year, impact is negative but smaller than first year for India, negative but larger for Bangladesh and positive for Pakistan.

Forecasting with Vector Nonlinear Time Series Models

This work concerns forecasting with vector nonlinear time series models when errorsare correlated. Point forecasts are numerically obtained using bootstrap methods andillustrated by two examples. Evaluation concentrates on studying forecast equality andencompassing. Nonlinear impulse responses are further considered and graphically sum-marized by highest density region. Finally, two macroeconomic data sets are used toillustrate our work. The forecasts from linear or nonlinear model could contribute usefulinformation absent in the forecasts form the other model.

Common Features in Vector Nonlinear Time Series Models

This thesis consists of four manuscripts in the area of nonlinear time series econometrics on topics of testing, modeling and forecasting nonlinear common features. The aim of this thesis is to develop new econometric contributions for hypothesis testing and forecasting in these area. Both stationary and nonstationary time series are concerned. A definition of common features is proposed in an appropriate way to each class. Based on the definition, a vector nonlinear time series model with common features is set up for testing for common features. The proposed models are available for forecasting as well after being well specified. The first paper addresses a testing procedure on nonstationary time series. A class of nonlinear cointegration, smooth-transition (ST) cointegration, is examined. The ST cointegration nests the previously developed linear and threshold cointegration. An Ftypetest for examining the ST cointegration is derived when stationary transition variables are imposed rather than nonstationary variables. Later ones drive the test standard, while the former ones make the test nonstandard. This has important implications for empirical work. It is crucial to distinguish between the cases with stationary and nonstationary transition variables so that the correct test can be used. The second and the fourth papers develop testing approaches for stationary time series. In particular, the vector ST autoregressive (VSTAR) model is extended to allow for common nonlinear features (CNFs). These two papers propose a modeling procedure and derive tests for the presence of CNFs. Including model specification using the testing contributions above, the third paper considers forecasting with vector nonlinear time series models and extends the procedures available for univariate nonlinear models. The VSTAR model with CNFs and the ST cointegration model in the previous papers are exemplified in detail,and thereafter illustrated within two corresponding macroeconomic data sets.

An analysis of linkage between foreign direct investment and GDP per Capita in Pakistan : A time series analysis

This study aims to investigate the relation between foreign direct investment (FDI) and per capita gross domestic product (GDP) in Pakistan. The study is based on a basic Cobb-Douglas production function. Population over age 15 to 64 is used as a proxy for labor in the investigation. The other variables used are gross capital formation, technological gap and a dummy variable measuring among other things political stability. We find positive correlation between GDP per capita in Pakistan and two variables, FDI and population over age 15 to 64. The GDP gap (gap between GDP of USA and GDP of Pakistan) is negatively correlated with GDP per capita as expected. Political instability, economic crisis, wars and polarization in the society have no significant impact on GDP per capita in the long run.

Time-series properties and empirical evidence of growth and infraestructure: revised version

After more than forty years studying growth, there are two classes of growth models that have emerged: exogenous and endogenous growth models. Since both try to mimic the same set of long-run stylized facts, they are observationally equivalent in some respects. Our goals in this paper are twofold First, we discuss the time-series properties of growth models in a way that is useful for assessing their fit to the data. Second, we investigate whether these two models successfully conforms to U.S. post-war data. We use cointegration techniques to estimate and test long-run capital elasticities, exogeneity tests to investigate the exogeneity status of TFP, and Granger-causality tests to examine temporal precedence of TFP with respect to infrastructure expenditures. The empirical evidence is robust in confirming the existence of a unity long-run capital elasticity. The analysis of TFP reveals that it is not weakly exogenous in the exogenous growth model Granger-causality test results show unequivocally that there is no evidence that TFP for both models precede infrastructure expenditures not being preceded by it. On the contrary, we find some evidence that infras- tructure investment precedes TFP. Our estimated impact of infrastructure on TFP lay rougbly in the interval (0.19, 0.27).