Statnote 17: using a regression line for prediction and calibration

Two types of prediction problem can be solved using a regression line viz., prediction of the ‘population’ regression line at the point ‘x’ and prediction of an ‘individual’ new member of the population ‘y1’ for which ‘x1’ has been measured. The second problem is probably the most commonly encountered and the most relevant to calibration studies. A regression line is likely to be most useful for calibration if the range of values of the X variable is large, if there is a good representation of the ‘x,y’ values across the range of X, and if several estimates of ‘y’ are made at each ‘x’. It is poor statistical practice to use a regression line for calibration or prediction beyond the limits of the data.

Statnote 19: non-linear regression: fitting an exponential curve to data

Non-linear relationships are common in microbiological research and often necessitate the use of the statistical techniques of non-linear regression or curve fitting. In some circumstances, the investigator may wish to fit an exponential model to the data, i.e., to test the hypothesis that a quantity Y either increases or decays exponentially with increasing X. This type of model is straight forward to fit as taking logarithms of the Y variable linearises the relationship which can then be treated by the methods of linear regression.

Data analysis methods in optometry. Part 5: correlation

1. Pearson's correlation coefficient only tests whether the data fit a linear model. With large numbers of observations, quite small values of r become significant and the X variable may only account for a minute proportion of the variance in Y. Hence, the value of r squared should always be calculated and included in a discussion of the significance of r. 2. The use of r assumes that a bivariate normal distribution is present and this assumption should be examined prior to the study. If Pearson's r is not appropriate, then a non-parametric correlation coefficient such as Spearman's rs may be used. 3. A significant correlation should not be interpreted as indicating causation especially in observational studies in which there is a high probability that the two variables are correlated because of their mutual correlations with other variables. 4. In studies of measurement error, there are problems in using r as a test of reliability and the ‘intra-class correlation coefficient’ should be used as an alternative. A correlation test provides only limited information as to the relationship between two variables. Fitting a regression line to the data using the method known as ‘least square’ provides much more information and the methods of regression and their application in optometry will be discussed in the next article.

Data analysis methods in optometry Part 7: multiple linear regression

Multiple regression analysis is a complex statistical method with many potential uses. It has also become one of the most abused of all statistical procedures since anyone with a data base and suitable software can carry it out. An investigator should always have a clear hypothesis in mind before carrying out such a procedure and knowledge of the limitations of each aspect of the analysis. In addition, multiple regression is probably best used in an exploratory context, identifying variables that might profitably be examined by more detailed studies. Where there are many variables potentially influencing Y, they are likely to be intercorrelated and to account for relatively small amounts of the variance. Any analysis in which R squared is less than 50% should be suspect as probably not indicating the presence of significant variables. A further problem relates to sample size. It is often stated that the number of subjects or patients must be at least 5-10 times the number of variables included in the study.5 This advice should be taken only as a rough guide but it does indicate that the variables included should be selected with great care as inclusion of an obviously unimportant variable may have a significant impact on the sample size required.

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.

Variable impedance bearings for large rotating machinery

This thesis describes an investigation which was carried out under the Interdisciplinary Higher Degres (IHD) Scheme of The University of Aston in Birmingham. The investigation, which involved joint collaboration between the IHD scheme, the Department of Mechanical Engineering, and G.E.C. Turbine Generators Limited, was concerned with hydrostatic bearing characteristics and of how hydrostatic bearings could be used to enable turbine generator rotor support impedances to be controlled to give an improved rotor dynamic response. Turbine generator rotor critical speeds are determined not only by the mass and flexibility of the rotor itself, which are relatively easily predicted, but also by the dynamic characteristics of the bearing oil film, pedestal, and foundations. It is because of the difficulty in accurately predicting the rotor support characteristics that the designer has a problem in ensuring that a rotor's normal running speed is not close to one of its critical speeds. The consequence of this situation is that some rotors do have critical speeds close to their normal running speed and the resulting high levels of vibration cause noise, high rotor stresses, and a shortening of bearing life. A combined theoretical and experimental investigation of the effects of mounting the normal rotor journal bearing in a hydrostatic bearing was carried out. The purpose of the work was to show that by changing the oil flow resistance offered by capillaries connecting accumulators to the hydrostatic bearing, the overall rotor support characteristics could be tuned to enable rotor critical speeds to be moved at will. Testing of a combined journal and hydrostatic bearing has confirmed the theory of its operation and a theoretical study of a full size machine showed that its critical speed could be moved by over 350 rpm and that its rotor vibration at running speed could be reduced by 80%.

Statnote 25: Stepwise multiple regression

An investigator may also wish to select a small subset of the X variables which give the best prediction of the Y variable. In this case, the question is how many variables should the regression equation include? One method would be to calculate the regression of Y on every subset of the X variables and choose the subset that gives the smallest mean square deviation from the regression. Most investigators, however, prefer to use a ‘stepwise multiple regression’ procedure. There are two forms of this analysis called the ‘step-up’ (or ‘forward’) method and the ‘step-down’ (or ‘backward’) method. This Statnote illustrates the use of stepwise multiple regression with reference to the scenario introduced in Statnote 24, viz., the influence of climatic variables on the growth of the crustose lichen Rhizocarpon geographicum (L.)DC.

Mathematical modelling of patient risks in a hospital environment, using multiple regression analysis

The aim of this research work was primarily to examine the relevance of patient parameters, ward structures, procedures and practices, in respect of the potential hazards of wound cross-infection and nasal colonisation with multiple resistant strains of Staphylococcus aureus, which it is thought might provide a useful indication of a patient's general susceptibility to wound infection. Information from a large cross-sectional survey involving 12,000 patients from some 41 hospitals and 375 wards was collected over a five-year period from 1967-72, and its validity checked before any subsequent analysis was carried out. Many environmental factors and procedures which had previously been thought (but never conclusively proved) to have an influence on wound infection or nasal colonisation rates, were assessed, and subsequently dismissed as not being significant, provided that the standard of the current range of practices and procedures is maintained and not allowed to deteriorate. Retrospective analysis revealed that the probability of wound infection was influenced by the patient's age, duration of pre-operative hospitalisation, sex, type of wound, presence and type of drain, number of patients in ward, and other special risk factors, whilst nasal colonisation was found to be influenced by the patient's age, total duration of hospitalisation, sex, antibiotics, proportion of occupied beds in the ward, average distance between bed centres and special risk factors. A multi-variate regression analysis technique was used to develop statistical models, consisting of variable patient and environmental factors which were found to have a significant influence on the risks pertaining to wound infection and nasal colonisation. A relationship between wound infection and nasal colonisation was then established and this led to the development of a more advanced model for predicting wound infections, taking advantage of the additional knowledge of the patient's state of nasal colonisation prior to operation.

Electromagnetic interference from variable speed motor drives

A methodology is presented which can be used to produce the level of electromagnetic interference, in the form of conducted and radiated emissions, from variable speed drives, the drive that was modelled being a Eurotherm 583 drive. The conducted emissions are predicted using an accurate circuit model of the drive and its associated equipment. The circuit model was constructed from a number of different areas, these being: the power electronics of the drive, the line impedance stabilising network used during the experimental work to measure the conducted emissions, a model of an induction motor assuming near zero load, an accurate model of the shielded cable which connected the drive to the motor, and finally the parasitic capacitances that were present in the drive modelled. The conducted emissions were predicted with an error of +/-6dB over the frequency range 150kHz to 16MHz, which compares well with the limits set in the standards which specify a frequency range of 150kHz to 30MHz. The conducted emissions model was also used to predict the current and voltage sources which were used to predict the radiated emissions from the drive. Two methods for the prediction of the radiated emissions from the drive were investigated, the first being two-dimensional finite element analysis and the second three-dimensional transmission line matrix modelling. The finite element model took account of the features of the drive that were considered to produce the majority of the radiation, these features being the switching of the IGBT's in the inverter, the shielded cable which connected the drive to the motor as well as some of the cables that were present in the drive.The model also took account of the structure of the test rig used to measure the radiated emissions. It was found that the majority of the radiation produced came from the shielded cable and the common mode currents that were flowing in the shield, and that it was feasible to model the radiation from the drive by only modelling the shielded cable. The radiated emissions were correctly predicted in the frequency range 30MHz to 200MHz with an error of +10dB/-6dB. The transmission line matrix method modelled the shielded cable which connected the drive to the motor and also took account of the architecture of the test rig. Only limited simulations were performed using the transmission line matrix model as it was found to be a very slow method and not an ideal solution to the problem. However the limited results obtained were comparable, to within 5%, to the results obtained using the finite element model.

Placenta growth factor-1 exerts time-dependent stabilization of adherens junctions following VEGF-induced vascular permeability

Increased vascular permeability is an early event characteristic of tissue ischemia and angiogenesis. Although VEGF family members are potent promoters of endothelial permeability the role of placental growth factor (PlGF) is hotly debated. Here we investigated PlGF isoforms 1 and 2 and present in vitro and in vivo evidence that PlGF-1, but not PlGF-2, can inhibit VEGF-induced permeability but only during a critical window post-VEGF exposure. PlGF-1 promotes VE-cadherin expression via the trans-activating Sp1 and Sp3 interaction with the VE-cadherin promoter and subsequently stabilizes transendothelial junctions, but only after activation of endothelial cells by VEGF. PlGF-1 regulates vascular permeability associated with the rapid localization of VE-cadherin to the plasma membrane and dephosphorylation of tyrosine residues that precedes changes observed in claudin 5 tyrosine phosphorylation and membrane localization. The critical window during which PlGF-1 exerts its effect on VEGF-induced permeability highlights the importance of the translational significance of this work in that PLGF-1 likely serves as an endogenous anti-permeability factor whose effectiveness is limited to a precise time point following vascular injury. Clinical approaches that would pattern nature's approach would thus limit treatments to precise intervals following injury and bring attention to use of agents only during therapeutic windows.

Gaussian process quantile regression using expectation propagation

Direct quantile regression involves estimating a given quantile of a response variable as a function of input variables. We present a new framework for direct quantile regression where a Gaussian process model is learned, minimising the expected tilted loss function. The integration required in learning is not analytically tractable so to speed up the learning we employ the Expectation Propagation algorithm. We describe how this work relates to other quantile regression methods and apply the method on both synthetic and real data sets. The method is shown to be competitive with state of the art methods whilst allowing for the leverage of the full Gaussian process probabilistic framework.

A survey based analysis of IT adoption and 3PLs' performance

Purpose: In today's competitive scenario, effective supply chain management is increasingly dependent on third-party logistics (3PL) companies' capabilities and performance. The dissemination of information technology (IT) has contributed to change the supply chain role of 3PL companies and IT is considered an important element influencing the performance of modern logistics companies. Therefore, the purpose of this paper is to explore the relationship between IT and 3PLs' performance, assuming that logistics capabilities play a mediating role in this relationship. Design/methodology/approach: Empirical evidence based on a questionnaire survey conducted on a sample of logistics service companies operating in the Italian market was used to test a conceptual resource-based view (RBV) framework linking IT adoption, logistics capabilities and firm performance. Factor analysis and ordinary least square (OLS) regression analysis have been used to test hypotheses. The focus of the paper is multidisciplinary in nature; management of information systems, strategy, logistics and supply chain management approaches have been combined in the analysis. Findings: The results indicate strong relationships among data gathering technologies, transactional capabilities and firm performance, in terms of both efficiency and effectiveness. Moreover, a positive correlation between enterprise information technologies and 3PL financial performance has been found. Originality/value: The paper successfully uses the concept of logistics capabilities as mediating factor between IT adoption and firm performance. Objective measures have been proposed for IT adoption and logistics capabilities. Direct and indirect relationships among variables have been successfully tested. © Emerald Group Publishing Limited.

Optimal design for correlated processes with input-dependent noise

Optimal design for parameter estimation in Gaussian process regression models with input-dependent noise is examined. The motivation stems from the area of computer experiments, where computationally demanding simulators are approximated using Gaussian process emulators to act as statistical surrogates. In the case of stochastic simulators, which produce a random output for a given set of model inputs, repeated evaluations are useful, supporting the use of replicate observations in the experimental design. The findings are also applicable to the wider context of experimental design for Gaussian process regression and kriging. Designs are proposed with the aim of minimising the variance of the Gaussian process parameter estimates. A heteroscedastic Gaussian process model is presented which allows for an experimental design technique based on an extension of Fisher information to heteroscedastic models. It is empirically shown that the error of the approximation of the parameter variance by the inverse of the Fisher information is reduced as the number of replicated points is increased. Through a series of simulation experiments on both synthetic data and a systems biology stochastic simulator, optimal designs with replicate observations are shown to outperform space-filling designs both with and without replicate observations. Guidance is provided on best practice for optimal experimental design for stochastic response models. © 2013 Elsevier Inc. All rights reserved.

Visual field severity indices demonstrate dose-dependent visual loss from Vigabatrin therapy

Purpose: The aims of this study were to develop an algorithm to accurately quantify Vigabatrin (VGB)-induced central visual field loss and to investigate the relationship between visual field loss and maximum daily dose, cumulative dose and duration of dose. Methods: The sample comprised 31 patients (mean age 37.9 years; SD 14.4 years) diagnosed with epilepsy and exposed to VGB. Each participant underwent standard automated static visual field examination of the central visual field. Central visual field loss was determined using continuous scales quantifying severity in terms of area and depth of defect and additionally by symmetry of defect between the two eyes. A simultaneous multiple regression model was used to explore the relationship between these visual field parameters and the drug predictor variables. Results: The regression model indicated that maximum VGB dose was the only factor to be significantly correlated with individual eye severity (right eye: p = 0.020; left eye: p = 0.012) and symmetry of visual field defect (p = 0.024). Conclusions: Maximum daily dose was the single most reliable indicator of those patients likely to exhibit visual field defects due to VGB. These findings suggest that high maximum dose is more likely to result in visual field defects than high cumulative doses or those of long duration.

Enhanced information transmission with signal-dependent noise in an array of nonlinear elements

We have investigated information transmission in an array of threshold units that have signal-dependent noise and a common input signal. We demonstrate a phenomenon similar to stochastic resonance and suprathreshold stochastic resonance with additive noise and show that information transmission can be enhanced by a nonzero level of noise. By comparing system performance to one with additive noise we also demonstrate that the information transmission of weak signals is significantly better with signal-dependent noise. Indeed, information rates are not compromised even for arbitrary small input signals. Furthermore, by an appropriate selection of parameters, we observe that the information can be made to be (almost) independent of the level of the noise, thus providing a robust method of transmitting information in the presence of noise. These result could imply that the ability of hair cells to code and transmit sensory information in biological sensory systems is not limited by the level of signal-dependent noise. © 2007 The American Physical Society.