Power properties if invariant tests for spatial autocorrelation in linear regression

This paper derives some exact power properties of tests for spatial autocorrelation in the context of a linear regression model. In particular, we characterize the circumstances in which the power vanishes as the autocorrelation increases, thus extending the work of Krämer (2005). More generally, the analysis in the paper sheds new light on how the power of tests for spatial autocorrelation is affected by the matrix of regressors and by the spatial structure. We mainly focus on the problem of residual spatial autocorrelation, in which case it is appropriate to restrict attention to the class of invariant tests, but we also consider the case when the autocorrelation is due to the presence of a spatially lagged dependent variable among the regressors. A numerical study aimed at assessing the practical relevance of the theoretical results is included

Parameter estimation based on stacked regression and evolutionary algorithms

A new parameter-estimation algorithm, which minimises the cross-validated prediction error for linear-in-the-parameter models, is proposed, based on stacked regression and an evolutionary algorithm. It is initially shown that cross-validation is very important for prediction in linear-in-the-parameter models using a criterion called the mean dispersion error (MDE). Stacked regression, which can be regarded as a sophisticated type of cross-validation, is then introduced based on an evolutionary algorithm, to produce a new parameter-estimation algorithm, which preserves the parsimony of a concise model structure that is determined using the forward orthogonal least-squares (OLS) algorithm. The PRESS prediction errors are used for cross-validation, and the sunspot and Canadian lynx time series are used to demonstrate the new algorithms.

Application of logistic regression for fault analysis in an industrial printing process

A statistical technique for fault analysis in industrial printing is reported. The method specifically deals with binary data, for which the results of the production process fall into two categories, rejected or accepted. The method is referred to as logistic regression, and is capable of predicting future fault occurrences by the analysis of current measurements from machine parts sensors. Individual analysis of each type of fault can determine which parts of the plant have a significant influence on the occurrence of such faults; it is also possible to infer which measurable process parameters have no significant influence on the generation of these faults. Information derived from the analysis can be helpful in the operator's interpretation of the current state of the plant. Appropriate actions may then be taken to prevent potential faults from occurring. The algorithm is being implemented as part of an applied self-learning expert system.

Who benefits from reducing the cost of formality? Quantile regression discontinuity analysis

This paper studies the effects of increasing formality via tax reduction and simplification schemes on micro-firm performance. It uses the 1997 Brazilian SIMPLES program. We develop a simple theoretical model to show that SIMPLES has an impact only on a segment of the micro-firm population, for which the effect of formality on firm performance can be identified, and that can be analyzed along the single dimensional quantiles of the conditional firm revenues. To estimate the effect of formality, we use an econometric approach that compares eligible and non-eligible firms, born before and after SIMPLES in a local interval about the introduction of SIMPLES. We use an estimator that combines both quantile regression and the regression discontinuity identification strategy. The empirical results corroborate the positive effect of formality on microfirms' performance and produce a clear characterization of who benefits from these programs.

An elastic net orthogonal forward regression algorithm

In this paper we propose an efficient two-level model identification method for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic net parameter estimation. The two regularization parameters in the elastic net are optimized using a particle swarm optimization (PSO) algorithm at the upper level by minimizing the leave one out (LOO) mean square error (LOOMSE). Illustrative examples are included to demonstrate the effectiveness of the new approaches.

Mutation of toxB and a truncated version of the efa-1 gene in Escherichia coli O157 : H7 influences the expression and secretion of locus of enterocyte effacement-encoded proteins but not intestinal colonization in calves or sheep

Enterohemorrhagic Escherichia coli (EHEC) strains comprise a broad group of bacteria, some of which cause attaching and effacing (AE) lesions and enteritis in humans and animals. Non-O157:H7 EHEC strains contain the gene efa-1 (referred to in previous publications as efa1), which influences adherence to cultured epithelial cells. An almost identical gene in enteropathogenic E. coli (lifA) mediates the inhibition of lymphocyte proliferation and proinflammatory cytokine synthesis. We have shown previously that significantly lower numbers of EHEC 05 and 0111 efa-1 mutants are shed in feces following experimental infection in calves and that these mutants exhibit reduced adherence to intestinal epithelia compared with isogenic wild-type strains. E. coli O157:H7 strains lack efa-1 but encode a homolog on the pO157 plasmid (toxB/l7095) and contain a truncated version of the efa-1 gene (efa-1'/z4332 in O island 122 of the EDL933 chromosome). Here we report that E. coli O157:H7 toxB and efa-1' single and double mutants exhibit reduced adherence to cultured epithelial cells and show reduced expression and secretion of proteins encoded by the locus of enterocyte effacement (LEE), which plays a key role in the host-cell interactions of EHEC. The activity of LEE1, LEE4, and LEE5 promoters was not significantly altered in E. coli O157:H7 strains harboring toxB or efa-1' mutations, indicating that the effect on the expression of LEE-encoded secreted proteins occurs at a posttranscriptional level. Despite affecting type III secretion, mutation of toxB and efa-1' did not significantly affect the course of fecal shedding of E. coli O157:H7 following experimental inoculation of 10- to 14-day-old calves or 6-week-old sheep. Mutation of tir caused a significant reduction in fecal shedding of E. coli O157:H7 in calves, indicating that the formation of AE lesions is important for colonization of the bovine intestine.

Analysis of the variability of auditory brainstem response components through linear regression

(ABR) is of fundamental importance to the investiga- tion of the auditory system behavior, though its in- terpretation has a subjective nature because of the manual process employed in its study and the clinical experience required for its analysis. When analyzing the ABR, clinicians are often interested in the identi- fication of ABR signal components referred to as Jewett waves. In particular, the detection and study of the time when these waves occur (i.e., the wave la- tency) is a practical tool for the diagnosis of disorders affecting the auditory system. In this context, the aim of this research is to compare ABR manual/visual analysis provided by different examiners. Methods: The ABR data were collected from 10 normal-hearing subjects (5 men and 5 women, from 20 to 52 years). A total of 160 data samples were analyzed and a pair- wise comparison between four distinct examiners was executed. We carried out a statistical study aiming to identify significant differences between assessments provided by the examiners. For this, we used Linear Regression in conjunction with Bootstrap, as a me- thod for evaluating the relation between the responses given by the examiners. Results: The analysis sug- gests agreement among examiners however reveals differences between assessments of the variability of the waves. We quantified the magnitude of the ob- tained wave latency differences and 18% of the inves- tigated waves presented substantial differences (large and moderate) and of these 3.79% were considered not acceptable for the clinical practice. Conclusions: Our results characterize the variability of the manual analysis of ABR data and the necessity of establishing unified standards and protocols for the analysis of these data. These results may also contribute to the validation and development of automatic systems that are employed in the early diagnosis of hearing loss.

Cross-layer design of enhanced AMC with truncated ARQ protocols

Using a cross-layer approach, two enhancement techniques applied for adaptive modulation and coding (AMC) with truncated automatic repeat request (T-ARQ) are investigated, namely, aggressive AMC (A-AMC) and constellation rearrangement (CoRe). Aggressive AMC selects the appropriate modulation and coding schemes (MCS) to achieve higher spectral efficiency, profiting from the feasibility of using different MCSs for retransmitting a packet, whereas in the CoRe-based AMC, retransmissions of the same data packet are performed using different mappings so as to provide different degrees of protection to the bits involved, thus achieving mapping diversity gain. The performance of both schemes is evaluated in terms of average spectral efficiency and average packet loss rate, which are derived in closed-form considering transmission over Nakagami-m fading channels. Numerical results and comparisons are provided. In particular, it is shown that A-AMC combined with T-ARQ yields higher spectral efficiency than the AMC-based conventional scheme while keeping the achieved packet loss rate closer to the system's requirement, and that it can achieve larger spectral efficiency objectives than that of the scheme using AMC along with CoRe.

The stochastic component in choice and regression to the mean

In this article, we illustrate experimentally an important consequence of the stochastic component in choice behaviour which has not been acknowledged so far. Namely, its potential to produce ‘regression to the mean’ (RTM) effects. We employ a novel approach to individual choice under risk, based on repeated multiple-lottery choices (i.e. choices among many lotteries), to show how the high degree of stochastic variability present in individual decisions can distort crucially certain results through RTM effects. We demonstrate the point in the context of a social comparison experiment.

Elastic net orthogonal forward regression

An efficient two-level model identification method aiming at maximising a model׳s generalisation capability is proposed for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic net parameter estimation. The two regularisation parameters in the elastic net are optimised using a particle swarm optimisation (PSO) algorithm at the upper level by minimising the leave one out (LOO) mean square error (LOOMSE). There are two elements of original contributions. Firstly an elastic net cost function is defined and applied based on orthogonal decomposition, which facilitates the automatic model structure selection process with no need of using a predetermined error tolerance to terminate the forward selection process. Secondly it is shown that the LOOMSE based on the resultant ENOFR models can be analytically computed without actually splitting the data set, and the associate computation cost is small due to the ENOFR procedure. Consequently a fully automated procedure is achieved without resort to any other validation data set for iterative model evaluation. Illustrative examples are included to demonstrate the effectiveness of the new approaches.