Detecting abnormal and collusive bids in capped tendering

Recent developments in the area of Bid Tender Forecasting have enabled bidders to implement new types of easy-to-use tools for increasing their chances of winning contracts. Although these new tools (such as iso-Score Curve Graphs, Scoring Probability Graphs, and Position Probability Graphs) are designed for bidders in capped tendering (tenders with an upper price limit), some of their principles can also be applied by a Contracting Authority to detect which bidders do not follow a standard pattern, that is, their bids are extremely high or low. Since a collusive bid generally needs to be sufficiently high or low to make an impact on the bid distribution, any person in charge of supervising capped tenders can be alerted to any bidder that might be involved in a cartel after identifying the same abnormal behavior in a series of tenders through simple calculations and a new type of graph.

Improving the estimation of probability of bidder participation in procurement auctions

Anticipating the number and identity of bidders has significant influence in many theoretical results of the auction itself and bidders' bidding behaviour. This is because when a bidder knows in advance which specific bidders are likely competitors, this knowledge gives a company a head start when setting the bid price. However, despite these competitive implications, most previous studies have focused almost entirely on forecasting the number of bidders and only a few authors have dealt with the identity dimension qualitatively. Using a case study with immediate real-life applications, this paper develops a method for estimating every potential bidder's probability of participating in a future auction as a function of the tender economic size removing the bias caused by the contract size opportunities distribution. This way, a bidder or auctioner will be able to estimate the likelihood of a specific group of key, previously identified bidders in a future tender.

Fatigue and workability in Brazilian textile companies in different corporate social responsibility score groups

The aim of this study was to evaluate working conditions in the textile industry for different stages of Corporate Social Responsibility (CSR) development, and workers` perception of fatigue and workability. A cross-sectional study was undertaken with 126 workers in the production areas of five Brazilian textile plants. The corporate executive officers and managers of each company provided their personal evaluations of CSR. Companies were divided into 2 groups (higher and lower) of CSR scores. Workers completed questionnaires on fatigue, workability and working conditions. Ergonomic job analysis showed similar results for working conditions, independent of their CSR score. Multivariate analysis models were developed for fatigue and workability, indicating that they are both associated to factors related to working conditions and individual workers` characteristics and life styles. Work organization, (what, how, when, where and for how long the work is done), is also an associated factor for fatigue. This study suggests that workers` opinions should be taken into greater consideration when companies develop their CSR programs, in particular for those relating to working conditions. Relevance to industry: This paper underlines the importance of considering working conditions and workers` opinions of them, work organization and individual workers` characteristics and life styles in order to restore or to maintain workability and to reduce fatigue, independently of how developed a company may be in the field of Corporate Social Responsibility. (C) 2010 Elsevier B.V. All rights reserved.

Effect of inbreeding on growth and reproductive traits of Nellore cattle in Brazil

The objective of this study was to identify and quantify the influence of F (inbreeding coefficient) on weaning weight (WW), weight gain from weaning to 18 months of age (WG345), finishing visual score (precocity) at 18 months of age, muscling visual score at 18 months of age (MUS), hip height (HH), scrotal circumference at 18 months of age (SC), heifer probability of pregnancy at 14 months of age (PP14), and stayability (STAY) in Brazilian Nellore cattle. The complete pedigree included 417,552 animals born between 1984 and 2007 on 12 farms located in the states of Mato Grosso do Sul, Sao Paulo and Bahia. Following the observation of a statistically significant effect (P<0.05) of the covariates individual inbreeding coefficient (F) and maternal inbreeding coefficient, regression analysis of each trait, adjusted for all other effects, was performed as a function of the linear and quadratic effect of F and maternal F (when significant). Inbreeding negatively affected all traits studied (P<0.05), except for muscling. A quadratic effect of individual F on WW, WG345, HH and PP14, and a quadratic effect of maternal F on WG345 and HH were observed. Levels of inbreeding higher than 7-11% affected negatively growth and reproductive performance of Nellore cattle. Therefore, inbreeding should be avoided, except for purposes of genetic breeding whose main objective is the fixation of certain alleles in the population. (C) 2010 Elsevier B.V. All rights reserved.

Metabolic acidosis in patients with severe sepsis and septic shock: A longitudinal quantitative study

Objective: To describe the composition of metabolic acidosis in patients with severe sepsis and septic shock at intensive care unit admission and throughout the first 5 days of intensive care unit stay. Design: Prospective, observational study. Setting: Twelve-bed intensive care unit. Patients: Sixty patients with either severe sepsis or septic shock. Interventions: None. Measurements and Main Results: Data were collected until 5 days after intensive care unit admission. We studied the contribution of inorganic ion difference, lactate, albumin, phosphate, and strong ion gap to metabolic acidosis. At admission, standard base excess was -6.69 +/- 4.19 mEq/L in survivors vs. -11.63 +/- 4.87 mEq/L in nonsurvivors (p < .05); inorganic ion difference (mainly resulting from hyperchloremia) was responsible for a decrease in standard base excess by 5.64 +/- 4.96 mEq/L in survivors vs. 8.94 +/- 7.06 mEq/L in nonsurvivors (p < .05); strong ion gap was responsible for a decrease in standard base excess by 4.07 +/- 3.57 mEq/L in survivors vs. 4.92 +/- 5.55 mEq/L in nonsurvivors with a nonsignificant probability value; and lactate was responsible for a decrease in standard base excess to 1.34 +/- 2.07 mEq/L in survivors vs. 1.61 +/- 2.25 mEq/L in nonsurvivors with a nonsignificant probability value. Albumin had an important alkalinizing effect in both groups; phosphate had a minimal acid-base effect. Acidosis in survivors was corrected during the study period as a result of a decrease in lactate and strong ion gap levels, whereas nonsurvivors did not correct their metabolic acidosis. In addition to Acute Physiology and Chronic Health Evaluation 11 score and serum creatinine level, inorganic ion difference acidosis magnitude at intensive care unit admission was independently associated with a worse outcome. Conclusions: Patients with severe sepsis and septic shock exhibit a complex metabolic acidosis at intensive care unit admission, caused predominantly by hyperchloremic acidosis, which was more pronounced in nonsurvivors. Acidosis resolution in survivors was attributable to a decrease in strong ion gap and lactate levels. (Crit Care Med 2009; 37:2733-2739)

Poly-bagging predictors for classification modelling for credit scoring

Credit scoring modelling comprises one of the leading formal tools for supporting the granting of credit. Its core objective consists of the generation of a score by means of which potential clients can be listed in the order of the probability of default. A critical factor is whether a credit scoring model is accurate enough in order to provide correct classification of the client as a good or bad payer. In this context the concept of bootstraping aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the fitted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper we propose a new bagging-type variant procedure, which we call poly-bagging, consisting of combining predictors over a succession of resamplings. The study is derived by credit scoring modelling. The proposed poly-bagging procedure was applied to some different artificial datasets and to a real granting of credit dataset up to three successions of resamplings. We observed better classification accuracy for the two-bagged and the three-bagged models for all considered setups. These results lead to a strong indication that the poly-bagging approach may promote improvement on the modelling performance measures, while keeping a flexible and straightforward bagging-type structure easy to implement. (C) 2011 Elsevier Ltd. All rights reserved.

Hypotheses Testing on a Multivariate Null Intercept Errors-in-Variables Model

Considering the Wald, score, and likelihood ratio asymptotic test statistics, we analyze a multivariate null intercept errors-in-variables regression model, where the explanatory and the response variables are subject to measurement errors, and a possible structure of dependency between the measurements taken within the same individual are incorporated, representing a longitudinal structure. This model was proposed by Aoki et al. (2003b) and analyzed under the bayesian approach. In this article, considering the classical approach, we analyze asymptotic test statistics and present a simulation study to compare the behavior of the three test statistics for different sample sizes, parameter values and nominal levels of the test. Also, closed form expressions for the score function and the Fisher information matrix are presented. We consider two real numerical illustrations, the odontological data set from Hadgu and Koch (1999), and a quality control data set.

Inference and local influence assessment in skew-normal null intercept measurement error model

In this article, we discuss inferential aspects of the measurement error regression models with null intercepts when the unknown quantity x (latent variable) follows a skew normal distribution. We examine first the maximum-likelihood approach to estimation via the EM algorithm by exploring statistical properties of the model considered. Then, the marginal likelihood, the score function and the observed information matrix of the observed quantities are presented allowing direct inference implementation. In order to discuss some diagnostics techniques in this type of models, we derive the appropriate matrices to assessing the local influence on the parameter estimates under different perturbation schemes. The results and methods developed in this paper are illustrated considering part of a real data set used by Hadgu and Koch [1999, Application of generalized estimating equations to a dental randomized clinical trial. Journal of Biopharmaceutical Statistics, 9, 161-178].

Size and power properties of some tests in the Birnbaum-Saunders regression model

The Birnbaum-Saunders distribution has been used quite effectively to model times to failure for materials subject to fatigue and for modeling lifetime data. In this paper we obtain asymptotic expansions, up to order n(-1/2) and under a sequence of Pitman alternatives, for the non-null distribution functions of the likelihood ratio, Wald, score and gradient test statistics in the Birnbaum-Saunders regression model. The asymptotic distributions of all four statistics are obtained for testing a subset of regression parameters and for testing the shape parameter. Monte Carlo simulation is presented in order to compare the finite-sample performance of these tests. We also present two empirical applications. (C) 2010 Elsevier B.V. All rights reserved.

Measurement error models with a general class of error distribution

In general, the normal distribution is assumed for the surrogate of the true covariates in the classical error model. This paper considers a class of distributions, which includes the normal one, for the variables subject to error. An estimation approach yielding consistent estimators is developed and simulation studies reported.

Properties and Inference on the Skew-Curved-Symmetric Family of Distributions

In this article, we study some results related to a specific class of distributions, called skew-curved-symmetric family of distributions that depends on a parameter controlling the skewness and kurtosis at the same time. Special elements of this family which are studied include symmetric and well-known asymmetric distributions. General results are given for the score function and the observed information matrix. It is shown that the observed information matrix is always singular for some special cases. We illustrate the flexibility of this class of distributions with an application to a real dataset on characteristics of Australian athletes.

Influence diagnostics for linear models with first-order autoregressive elliptical errors

We introduce in this paper the class of linear models with first-order autoregressive elliptical errors. The score functions and the Fisher information matrices are derived for the parameters of interest and an iterative process is proposed for the parameter estimation. Some robustness aspects of the maximum likelihood estimates are discussed. The normal curvatures of local influence are also derived for some usual perturbation schemes whereas diagnostic graphics to assess the sensitivity of the maximum likelihood estimates are proposed. The methodology is applied to analyse the daily log excess return on the Microsoft whose empirical distributions appear to have AR(1) and heavy-tailed errors. (C) 2008 Elsevier B.V. All rights reserved.

A note on influence diagnostics in nonlinear mixed-effects elliptical models

This paper provides general matrix formulas for computing the score function, the (expected and observed) Fisher information and the A matrices (required for the assessment of local influence) for a quite general model which includes the one proposed by Russo et al. (2009). Additionally, we also present an expression for the generalized leverage on fixed and random effects. The matrix formulation has notational advantages, since despite the complexity of the postulated model, all general formulas are compact, clear and have nice forms. (C) 2010 Elsevier B.V. All rights reserved.

Local power of some tests in exponential family nonlinear models

In this paper we obtain asymptotic expansions up to order n(-1/2) for the nonnull distribution functions of the likelihood ratio, Wald, score and gradient test statistics in exponential family nonlinear models (Cordeiro and Paula, 1989), under a sequence of Pitman alternatives. The asymptotic distributions of all four statistics are obtained for testing a subset of regression parameters and for testing the dispersion parameter, thus generalising the results given in Cordeiro et al. (1994) and Ferrari et al. (1997). We also present Monte Carlo simulations in order to compare the finite-sample performance of these tests. (C) 2010 Elsevier B.V. All rights reserved.

From Data Snore, to Data Score: Communicating Library Assessment Data Visually through Infographics

This presentation was offered as part of the CUNY Library Assessment Conference, Reinventing Libraries: Reinventing Assessment, held at the City University of New York in June 2014.