A new approach to evaluate imperfection sensitivity in asymmetric bifurcation buckling analysis

A direct procedure for the evaluation of imperfection sensitivity in bifurcation problems is presented. The problems arise in the context of the general theory of elastic stability for discrete structural systems, in which the energy criterion of stability of structures and the total potential energy formulation are employed. In cases of bifurcation buckling the sensitivity of the critical load with respect to an imperfection parameter e is singular at the state given by epsilon =0, so that, a regular perturbation expansion of the solution is not possible. In this work we describe a direct procedure to obtain the relations between the critical loads, the generalized coordinates at the critical state, the eigenvector, and the amplitude of the imperfection, using singular perturbation analysis. The expansions are assumed in terms of arbitrary powers of the imperfection parameter, so that both exponents and coefficients of the expansion are unknown. The solution of the series exponents is obtained by searching the least degenerate solution. The formulation is here applied to asymmetric bifurcations, for which explicit expressions of the coefficients are obtained. The use of the method is illustrated by a simple example, which allows consideration of the main features of the formulation.

Wheat Yield Loss in a Two Species Competition with Emex australis and Emex spinosa

Emex australis and E. spinosa are significant weed species in wheat and other crops. Information on the extent of competition of the Emex species will be helpful to access yield losses in wheat. Field experiments were conducted to quantify the interference of tested weed densities each as single or mixture of both at 1:1 on their growth and yield, wheat yield components and wheat grain yield losses in two consecutive years. Dry weight of both weed species increased from 3-6 g m-2 with every additional plant of weed, whereas seed number and weight per plant decreased with increasing density of either weed. Both weed species caused considerable decrease in yield components like spike bearing tillers, number of grains per spike, 1000-grain weight of wheat with increasing density population of the weeds. Based on non-linear hyperbolic regression model equation, maximum yield loss at asymptotic weed density was estimated to be 44 and 62% with E. australis, 56 and 70% with E. spinosa and 63 and 72% with mixture of both species at 1:1 during both year of study, respectively. It was concluded that E. spinosa has more competition effects on wheat crop as compared to E. australis.

Molecular characteristics of Machado-Joseph disease mutation in 25 newly described Brazilian families

Machado-Joseph disease (MJD) is a form of autosomal dominant spinocerebellar ataxia first described in North-American patients originating from the Portuguese islands of the Azores. Clinically this disorder is characterized by late onset progressive ataxia with associated features, such as: ophthalmoplegia, pyramidal and extrapyramidal signs and distal muscular atrophies. The causative mutation is an expansion of a CAG repeat in the coding region of the MJD1 gene. We have identified 25 unrelated families segregating the MJD mutation during a large collaborative study of spinocerebellar ataxias in Brazil. In the present study a total of 62 family members were genotyped for the CAG repeat in the MJD1 gene, as well as 63 non-MJD individuals (126 normal chromosomes), used as normal controls. We observed a wide gap between the size range of the normal and expanded CAG repeats: the normal allele had from 12 to 33 CAGs (mean = 23 CAGs), whereas the expanded alleles ranged from 66 to 78 CAGs (mean = 71.5 CAGs). There were no differences in CAG tract length according to gender of affected individuals or transmitting parent. We observed a significant negative correlation between age at onset of the disease and length of the CAG tract in the expended allele (r = -0.6, P = 0.00006); however, the size of the expanded CAG repeat could explain only about 40% of the variability in age at onset (r2 = 0.4). There was instability of the expanded CAG tract during transmission from parent to offspring, both expansions and contractions were observed; however, there was an overall tendency for expansion, with a mean increase of +2.4 CAGs. The tendency for expansion appeared to the greater in paternal (mean increase of +3.5 CAGs) than in maternal transmissions (mean increase of +1.3 CAGs). Anticipation was observed in all transmissions in which ages at onset for parent and offspring were known; however, anticipation was not always associated with an increase in the expanded CAG repeat length. Our results indicate that the molecular diagnosis of MJD can be confirmed or excluded in all suspected individuals, since alleles of intermediary size were not observed.

Relationship of cytokines and cytokine signaling to immunodeficiency disorders in the mouse

The contributions of cytokines to the development and progression of disease in a mouse model of retrovirus-induced immunodeficiency (MAIDS) are controversial. Some studies have indicated an etiologic role for type 2 cytokines, while others have emphasized the importance of type 1 cytokines. We have used mice deficient in expression of IL-4, IL-10, IL-4 and IL-10, IFN-g, or ICSBP - a transcriptional protein involved in IFN signaling - to examine their contributions to this disorder. Our results demonstrate that expression of type 2 cytokines is an epiphenomenon of infection and that IFN-g is a driving force in disease progression. In addition, exogenously administered IL-12 prevents many manifestations of disease while blocking retrovirus expression. Interruption of the IFN signaling pathways in ICSBP-/- mice blocks induction of MAIDS. Predictably, ICSBP-deficient mice exhibit impaired responses to challenge with several other viruses. This immunodeficiency is associated with impaired production of IFN-g and IL-12. Unexpectedly, however, the ICSBP-/- mice also develop a syndrome with many similarities to chronic myelogenous leukemia in humans. The chronic phase of this disease is followed by a fatal blast crisis characterized by clonal expansions of undifferentiated cells. ICSBP is thus an important determinant of hematopoietic growth and differentiation as well as a prominent signaling molecule for IFNs

Efficient Optimization Algorithms for Nonlinear Data Analysis

Identification of low-dimensional structures and main sources of variation from multivariate data are fundamental tasks in data analysis. Many methods aimed at these tasks involve solution of an optimization problem. Thus, the objective of this thesis is to develop computationally efficient and theoretically justified methods for solving such problems. Most of the thesis is based on a statistical model, where ridges of the density estimated from the data are considered as relevant features. Finding ridges, that are generalized maxima, necessitates development of advanced optimization methods. An efficient and convergent trust region Newton method for projecting a point onto a ridge of the underlying density is developed for this purpose. The method is utilized in a differential equation-based approach for tracing ridges and computing projection coordinates along them. The density estimation is done nonparametrically by using Gaussian kernels. This allows application of ridge-based methods with only mild assumptions on the underlying structure of the data. The statistical model and the ridge finding methods are adapted to two different applications. The first one is extraction of curvilinear structures from noisy data mixed with background clutter. The second one is a novel nonlinear generalization of principal component analysis (PCA) and its extension to time series data. The methods have a wide range of potential applications, where most of the earlier approaches are inadequate. Examples include identification of faults from seismic data and identification of filaments from cosmological data. Applicability of the nonlinear PCA to climate analysis and reconstruction of periodic patterns from noisy time series data are also demonstrated. Other contributions of the thesis include development of an efficient semidefinite optimization method for embedding graphs into the Euclidean space. The method produces structure-preserving embeddings that maximize interpoint distances. It is primarily developed for dimensionality reduction, but has also potential applications in graph theory and various areas of physics, chemistry and engineering. Asymptotic behaviour of ridges and maxima of Gaussian kernel densities is also investigated when the kernel bandwidth approaches infinity. The results are applied to the nonlinear PCA and to finding significant maxima of such densities, which is a typical problem in visual object tracking.

The conservative physiology of the immune system

Current immunological opinion disdains the necessity to define global interconnections between lymphocytes and regards natural autoantibodies and autoreactive T cells as intrinsically pathogenic. Immunological theories address the recognition of foreignness by independent clones of lymphocytes, not the relations among lymphocytes or between lymphocytes and the organism. However, although extremely variable in cellular/molecular composition, the immune system preserves as invariant a set of essential relations among its components and constantly enacts contacts with the organism of which it is a component. These invariant relations are reflected, for example, in the life-long stability of profiles of reactivity of immunoglobulins formed by normal organisms (natural antibodies). Oral contacts with dietary proteins and the intestinal microbiota also result in steady states that lack the progressive quality of secondary-type reactivity. Autoreactivity (natural autoantibody and autoreactive T cell formation) is also stable and lacks the progressive quality of clonal expansion. Specific immune responses, currently regarded as the fundament of the operation of the immune system, may actually result from transient interruptions in this stable connectivity among lymphocytes. More permanent deficits in interconnectivity result in oligoclonal expansions of T lymphocytes, as seen in Omenn's syndrome and in the experimental transplantation of a suboptimal diversity of syngeneic T cells to immunodeficient hosts, which also have pathogenic consequences. Contrary to theories that forbid autoreactivity as potentially pathogenic, the physiology of the immune system is conservative and autoreactive. Pathology derives from failures of these conservative mechanisms.

Moisture adsorption characteristics of ginger slices

The moisture adsorption characteristics of dried ginger slices was studied to determine the effect of storage conditions on moisture adsorption for the purpose of shelf life prediction, selection of appropriate packaging materials, evaluate the goodness-of-fit of sorption models, and determine the thermodynamics of moisture adsorption for application in drying. There was a highly significant effect (p < 0.05) of water activity (a w), temperature, and pre-treatment on the equilibrium moisture content (EMC) of the dried ginger slices. At constant a w, the EMC decreased as temperature increased. The EMC of all samples increased as the a w increased at constant temperature. The sorbed moisture of the unpeeled ginger slices was higher than the peeled while those of unblanched samples were higher than the blanched. Henderson equation allows more accurate predictions about the isotherms with the lowest %RMS, and therefore, it describes best the adsorption data followed by GAB, Oswin, and Halsey models in that order. The monolayer moisture generally decreased with temperature for all samples. The isosteric heat decreased with moisture content approaching the asymptotic value or the latent heat of vaporization of pure water (∆Hst = 0) while the entropy of sorption was observed to increase with moisture content.

A note on inflation targeting and economic growth in Brazil

This paper analyzes the relation between monetary policy and economic performance in Brazil during the period 1999-2006. In particular, it discusses the growth effects of the inflation targeting regime through its effects on aggregate demand. It is argued that monetary policy under IT reacts in a procyclical and asymmetric way to fluctuations in economic activity (too "tight" during recessions, not so "loose" during expansions). Such pattern may generate a downward bias in aggregate demand, with negative real effects on output growth and employment. Our results suggest that monetary policy has been procyclical and asymmetrical in Brazil under inflation targeting. The main economic policy implication of this study is that central banks should consider more seriously the real effects of monetary policy on output and employment.

Towards automated derivation in the theory of allegories

We provide an algorithm that automatically derives many provable theorems in the equational theory of allegories. This was accomplished by noticing properties of an existing decision algorithm that could be extended to provide a derivation in addition to a decision certificate. We also suggest improvements and corrections to previous research in order to motivate further work on a complete derivation mechanism. The results presented here are significant for those interested in relational theories, since we essentially have a subtheory where automatic proof-generation is possible. This is also relevant to program verification since relations are well-suited to describe the behaviour of computer programs. It is likely that extensions of the theory of allegories are also decidable and possibly suitable for further expansions of the algorithm presented here.

Simulation-Based Finite and Large Sample Tests in Multivariate Regressions.

In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems. For the case of uniform linear hypotheses, we present exact distributional invariance results concerning several standard test criteria. These include Wilks' likelihood ratio (LR) criterion as well as trace and maximum root criteria. The normality assumption is not necessary for most of the results to hold. Implications for inference are two-fold. First, invariance to nuisance parameters entails that the technique of Monte Carlo tests can be applied on all these statistics to obtain exact tests of uniform linear hypotheses. Second, the invariance property of the latter statistic is exploited to derive general nuisance-parameter-free bounds on the distribution of the LR statistic for arbitrary hypotheses. Even though it may be difficult to compute these bounds analytically, they can easily be simulated, hence yielding exact bounds Monte Carlo tests. Illustrative simulation experiments show that the bounds are sufficiently tight to provide conclusive results with a high probability. Our findings illustrate the value of the bounds as a tool to be used in conjunction with more traditional simulation-based test methods (e.g., the parametric bootstrap) which may be applied when the bounds are not conclusive.

Exact Tests for Contemporaneous Correlation of Disturbances in Seemingly Unrelated Regressions.

This paper proposes finite-sample procedures for testing the SURE specification in multi-equation regression models, i.e. whether the disturbances in different equations are contemporaneously uncorrelated or not. We apply the technique of Monte Carlo (MC) tests [Dwass (1957), Barnard (1963)] to obtain exact tests based on standard LR and LM zero correlation tests. We also suggest a MC quasi-LR (QLR) test based on feasible generalized least squares (FGLS). We show that the latter statistics are pivotal under the null, which provides the justification for applying MC tests. Furthermore, we extend the exact independence test proposed by Harvey and Phillips (1982) to the multi-equation framework. Specifically, we introduce several induced tests based on a set of simultaneous Harvey/Phillips-type tests and suggest a simulation-based solution to the associated combination problem. The properties of the proposed tests are studied in a Monte Carlo experiment which shows that standard asymptotic tests exhibit important size distortions, while MC tests achieve complete size control and display good power. Moreover, MC-QLR tests performed best in terms of power, a result of interest from the point of view of simulation-based tests. The power of the MC induced tests improves appreciably in comparison to standard Bonferroni tests and, in certain cases, outperforms the likelihood-based MC tests. The tests are applied to data used by Fischer (1993) to analyze the macroeconomic determinants of growth.

Simulation-Based Finite-Sample Tests for Heteroskedasticity and ARCH Effects.

A wide range of tests for heteroskedasticity have been proposed in the econometric and statistics literature. Although a few exact homoskedasticity tests are available, the commonly employed procedures are quite generally based on asymptotic approximations which may not provide good size control in finite samples. There has been a number of recent studies that seek to improve the reliability of common heteroskedasticity tests using Edgeworth, Bartlett, jackknife and bootstrap methods. Yet the latter remain approximate. In this paper, we describe a solution to the problem of controlling the size of homoskedasticity tests in linear regression contexts. We study procedures based on the standard test statistics [e.g., the Goldfeld-Quandt, Glejser, Bartlett, Cochran, Hartley, Breusch-Pagan-Godfrey, White and Szroeter criteria] as well as tests for autoregressive conditional heteroskedasticity (ARCH-type models). We also suggest several extensions of the existing procedures (sup-type of combined test statistics) to allow for unknown breakpoints in the error variance. We exploit the technique of Monte Carlo tests to obtain provably exact p-values, for both the standard and the new tests suggested. We show that the MC test procedure conveniently solves the intractable null distribution problem, in particular those raised by the sup-type and combined test statistics as well as (when relevant) unidentified nuisance parameter problems under the null hypothesis. The method proposed works in exactly the same way with both Gaussian and non-Gaussian disturbance distributions [such as heavy-tailed or stable distributions]. The performance of the procedures is examined by simulation. The Monte Carlo experiments conducted focus on : (1) ARCH, GARCH, and ARCH-in-mean alternatives; (2) the case where the variance increases monotonically with : (i) one exogenous variable, and (ii) the mean of the dependent variable; (3) grouped heteroskedasticity; (4) breaks in variance at unknown points. We find that the proposed tests achieve perfect size control and have good power.

Logique et tests d'hypotheses: reflexions sur les problemes mal poses en econometrie

Dans ce texte, nous analysons les développements récents de l’économétrie à la lumière de la théorie des tests statistiques. Nous revoyons d’abord quelques principes fondamentaux de philosophie des sciences et de théorie statistique, en mettant l’accent sur la parcimonie et la falsifiabilité comme critères d’évaluation des modèles, sur le rôle de la théorie des tests comme formalisation du principe de falsification de modèles probabilistes, ainsi que sur la justification logique des notions de base de la théorie des tests (tel le niveau d’un test). Nous montrons ensuite que certaines des méthodes statistiques et économétriques les plus utilisées sont fondamentalement inappropriées pour les problèmes et modèles considérés, tandis que de nombreuses hypothèses, pour lesquelles des procédures de test sont communément proposées, ne sont en fait pas du tout testables. De telles situations conduisent à des problèmes statistiques mal posés. Nous analysons quelques cas particuliers de tels problèmes : (1) la construction d’intervalles de confiance dans le cadre de modèles structurels qui posent des problèmes d’identification; (2) la construction de tests pour des hypothèses non paramétriques, incluant la construction de procédures robustes à l’hétéroscédasticité, à la non-normalité ou à la spécification dynamique. Nous indiquons que ces difficultés proviennent souvent de l’ambition d’affaiblir les conditions de régularité nécessaires à toute analyse statistique ainsi que d’une utilisation inappropriée de résultats de théorie distributionnelle asymptotique. Enfin, nous soulignons l’importance de formuler des hypothèses et modèles testables, et de proposer des techniques économétriques dont les propriétés sont démontrables dans les échantillons finis.

The Bootstrap of Mean for Dependent Heterogeneous Arrays.

Presently, conditions ensuring the validity of bootstrap methods for the sample mean of (possibly heterogeneous) near epoch dependent (NED) functions of mixing processes are unknown. Here we establish the validity of the bootstrap in this context, extending the applicability of bootstrap methods to a class of processes broadly relevant for applications in economics and finance. Our results apply to two block bootstrap methods: the moving blocks bootstrap of Künsch ( 989) and Liu and Singh ( 992), and the stationary bootstrap of Politis and Romano ( 994). In particular, the consistency of the bootstrap variance estimator for the sample mean is shown to be robust against heteroskedasticity and dependence of unknown form. The first order asymptotic validity of the bootstrap approximation to the actual distribution of the sample mean is also established in this heterogeneous NED context.

Nonparametric Instrumental Regression

The focus of the paper is the nonparametric estimation of an instrumental regression function P defined by conditional moment restrictions stemming from a structural econometric model : E[Y-P(Z)|W]=0 and involving endogenous variables Y and Z and instruments W. The function P is the solution of an ill-posed inverse problem and we propose an estimation procedure based on Tikhonov regularization. The paper analyses identification and overidentification of this model and presents asymptotic properties of the estimated nonparametric instrumental regression function.