Bootstrap prediction intervals for autoregressive time series

The calculation of interval forecasts for highly persistent autoregressive (AR) time series based on the bootstrap is considered. Three methods are considered for countering the small-sample bias of least-squares estimation for processes which have roots close to the unit circle: a bootstrap bias-corrected OLS estimator; the use of the Roy–Fuller estimator in place of OLS; and the use of the Andrews–Chen estimator in place of OLS. All three methods of bias correction yield superior results to the bootstrap in the absence of bias correction. Of the three correction methods, the bootstrap prediction intervals based on the Roy–Fuller estimator are generally superior to the other two. The small-sample performance of bootstrap prediction intervals based on the Roy–Fuller estimator are investigated when the order of the AR model is unknown, and has to be determined using an information criterion.

Prediction of Staphylococcus aureus antimicrobial resistance by whole-genome sequencing

Whole-genome sequencing (WGS) could potentially provide a single platform for extracting all the information required to predict an organism’s phenotype. However, its ability to provide accurate predictions has not yet been demonstrated in large independent studies of specific organisms. In this study, we aimed to develop a genotypic prediction method for antimicrobial susceptibilities. The whole genomes of 501 unrelated Staphylococcus aureus isolates were sequenced, and the assembled genomes were interrogated using BLASTn for a panel of known resistance determinants (chromosomal mutations and genes carried on plasmids). Results were compared with phenotypic susceptibility testing for 12 commonly used antimicrobial agents (penicillin, methicillin, erythromycin, clindamycin, tetracycline, ciprofloxacin, vancomycin, trimethoprim, gentamicin, fusidic acid, rifampin, and mupirocin) performed by the routine clinical laboratory. We investigated discrepancies by repeat susceptibility testing and manual inspection of the sequences and used this information to optimize the resistance determinant panel and BLASTn algorithm. We then tested performance of the optimized tool in an independent validation set of 491 unrelated isolates, with phenotypic results obtained in duplicate by automated broth dilution (BD Phoenix) and disc diffusion. In the validation set, the overall sensitivity and specificity of the genomic prediction method were 0.97 (95% confidence interval [95% CI], 0.95 to 0.98) and 0.99 (95% CI, 0.99 to 1), respectively, compared to standard susceptibility testing methods. The very major error rate was 0.5%, and the major error rate was 0.7%. WGS was as sensitive and specific as routine antimicrobial susceptibility testing methods. WGS is a promising alternative to culture methods for resistance prediction in S. aureus and ultimately other major bacterial pathogens.

Multiprobabilistic prediction in early medical diagnoses

This paper describes the methodology of providing multiprobability predictions for proteomic mass spectrometry data. The methodology is based on a newly developed machine learning framework called Venn machines. Is allows to output a valid probability interval. The methodology is designed for mass spectrometry data. For demonstrative purposes, we applied this methodology to MALDI-TOF data sets in order to predict the diagnosis of heart disease and early diagnoses of ovarian cancer and breast cancer. The experiments showed that probability intervals are narrow, that is, the output of the multiprobability predictor is similar to a single probability distribution. In addition, probability intervals produced for heart disease and ovarian cancer data were more accurate than the output of corresponding probability predictor. When Venn machines were forced to make point predictions, the accuracy of such predictions is for the most data better than the accuracy of the underlying algorithm that outputs single probability distribution of a label. Application of this methodology to MALDI-TOF data sets empirically demonstrates the validity. The accuracy of the proposed method on ovarian cancer data rises from 66.7 % 11 months in advance of the moment of diagnosis to up to 90.2 % at the moment of diagnosis. The same approach has been applied to heart disease data without time dependency, although the achieved accuracy was not as high (up to 69.9 %). The methodology allowed us to confirm mass spectrometry peaks previously identified as carrying statistically significant information for discrimination between controls and cases.

Likelihood prediction for generalized linear mixed models under covariate uncertainty

This paper presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction is explained through a series of examples; from a classical one to more complicated ones. The examples show, in simple cases, that the likelihood prediction (LP) coincides with already known best frequentist practice such as the best linear unbiased predictor. The paper outlines a way to deal with the covariate uncertainty while producing predictive inference. Using a Poisson error-in-variable generalized linear model, it has been shown that in complicated cases LP produces better results than already know methods.

Prediction error property of the Lasso Estimator and its generalization

The lasso procedure is an estimator-shrinkage and variable selection method. This paper shows that there always exists an interval of tuning parameter values such that the corresponding mean squared prediction error for the lasso estimator is smaller than for the ordinary least squares estimator. For an estimator satisfying some condition such as unbiasedness, the paper defines the corresponding generalized lasso estimator. Its mean squared prediction error is shown to be smaller than that of the estimator for values of the tuning parameter in some interval. This implies that all unbiased estimators are not admissible. Simulation results for five models support the theoretical results.

Prediction of functional oucomes in stroke inpatients receiving rehabilitation

Background and Purpose: Early identification of predictive factors relevant to functional outcomes for stroke patients is important to the establishment of an effective continuing care program. The objective of this studywas to identify the predictive factors related to functional outcome at discharge after stroke rehabilitation therapy. Methods: 105 first-time stroke patients admitted to the inpatient rehabilitation department of a university-based medical center were recruited for this prospective study. The functional outcomes of the patients were assessed at admission and at discharge using the Functional Independence Measure (FIM). Severity of stroke was determined using the Canadian Neurological Scale (CNS). Age, gender, side of hemiplegia (SIDE), type of stroke (TYPE), onset to admission interval (OAI), and length of rehabilitation stay (LORS) were also included as predictor variables. Results: The mean (′SD) FIM score at discharge (76.6 ′ 26.4) correlated strongly (r = 0.78, p < 0.001) with the admission FIM score (56.3 ′ 24.1), moderately (r = 0.46, p < 0.001) with the admission CNS score (6.1 ′ 2.2), negatively (r = -0.38, p < 0.001) with age (63.2 ′ 12.3 years), negatively (r = -0.26, p = 0.009) with OAI (24.2 ′ 16.0 days), and negatively (r = -0.29, p = 0.002) with LORS (34.7 ′ 16.8 ays). Stepwise regression analyses indicated that admission FIM score, age, and admission CNS score were the stronge predictors of functional outcome and accounted for 66% of the total variation in discharge FIM total score. The admission FIM score was the best predictor and accounted for 61% of the variation. Conclusions: The findings of this study imply that the admission FIM scores for inpatients receiving stroke rehabilitation can be used to predict functional outcomes at discharge from hospital.

The interval between sexual encounters affects male courtship tactics in a desert-dwelling fish

Courtship displays are often important in determining male mating success but can also be costly. Thus, instead of courting females indiscriminately, males might be expected to adjust their signalling effort strategically. Theory, however, predicts that such adjustments should depend on the rate with which males encounter females, a prediction that has been subject to very little empirical testing. Here, we investigate the effects of female encounter rate on male courtship intensity by manipulating the time interval between sequential presentations of large (high quality) and small (low quality) females in a fish, the Australian desert goby Chlamydogobius eremius. Males that were presented with a small female immediately after a large female reduced their courtship intensity significantly. However, males courted large and small females with equal intensity if the interval between the sequential presentations was longer. Our results suggest that mate encounter rate is an important factor shaping male reproductive decisions and, consequently, the evolutionary potential of sexual selection.

Short term load forecasting using interval type-2 fuzzy logic systems

Accurate Short Term Load Forecasting (STLF) is essential for a variety of decision making processes. However, forecasting accuracy may drop due to presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. This paper proposes the application of Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) for the problem of STLF. IT2 FLSs, with extra degrees of freedom, are an excellent tool for handling prevailing uncertainties and improving the prediction accuracy. Experiments conducted with real datasets show that IT2 FLS models appropriately approximate future load demands with an acceptable accuracy. Furthermore, they demonstrate an encouraging degree of accuracy superior to feedforward neural networks used in this study.

SAS macros for point and interval estimation of area under the receiver operating characteristic curve for non-proportional and proportional hazards Weibull models

Aims and objectives  For prediction of risk of cardiovascular end points using survival models the proportional hazards assumption is often not met. Thus, non-proportional hazards models are more appropriate for developing risk prediction equations in such situations. However, computer program for evaluating the prediction performance of such models has been rarely addressed. We therefore developed SAS macro programs for evaluating the discriminative ability of a non-proportional hazards Weibull model developed by Anderson (1991) and that of a proportional hazards Weibull model using the area under receiver operating characteristic (ROC) curve.

Method  Two SAS macro programs for non-proportional hazards Weibull model using Proc NLIN and Proc NLP respectively and model validation using area under ROC curve (with its confidence limits) were written with SAS IML language. A similar SAS macro for proportional hazards Weibull model was also written.

Results  The computer program was applied to data on coronary heart disease incidence for a Framingham population cohort. The five risk factors considered were current smoking, age, blood pressure, cholesterol and obesity. The predictive ability of the non-proportional hazard Weibull model was slightly higher than that of its proportional hazard counterpart. An advantage of SAS Proc NLP in terms of the example provided here is that it provides significance level for the parameter estimates whereas Proc NLIN does not.

Conclusion  The program is very useful for evaluating the predictive performance of non-proportional and proportional hazards Weibull models.

Neural network and interval type-2 fuzzy system for stock price forecasting

Stock price forecast has long been received special attention of investors and financial institutions. As stock prices are changeable over time and increasingly uncertain in modern financial markets, their forecasting becomes more important than ever before. A hybrid approach consisting of two components, a neural network and a fuzzy logic system, is proposed in this paper for stock price prediction. The first component of the hybrid, i.e. a feedforward neural network (FFNN), is used to select inputs that are highly relevant to the dependent variables. An interval type-2 fuzzy logic system (IT2 FLS) is employed as the second component of the hybrid forecasting method. The IT2 FLS’s parameters are initialized through deployment of the k-means clustering method and they are adjusted by the genetic algorithm. Experimental results demonstrate the efficiency of the FFNN input selection approach as it reduces the complexity and increase the accuracy of the forecasting models. In addition, IT2 FLS outperforms the widely used type-1 FLS and FFNN models in stock price forecasting. The combination of the FFNN and the IT2 FLS produces dominant forecasting accuracy compared to employing only the IT2 FLSs without the FFNN input selection.

Prediction intervals for electricity load forecasting using neural networks

Most of the research in time series is concerned with point forecasting. In this paper we focus on interval forecasting and its application for electricity load prediction. We extend the LUBE method, a neural network-based method for computing prediction intervals. The extended method, called LUBEX, includes an advanced feature selector and an ensemble of neural networks. Its performance is evaluated using Australian electricity load data for one year. The results showed that LUBEX is able to generate high quality prediction intervals, using a very small number of previous lag variables and having acceptable training time requirements. The use of ensemble is shown to be critical for the accuracy of the results.

Mass spectrometry-based proteomic data for cancer diagnosis using interval type-2 fuzzy system

An interval type-2 fuzzy logic system is introduced for cancer diagnosis using mass spectrometry-based proteomic data. The fuzzy system is incorporated with a feature extraction procedure that combines wavelet transform and Wilcoxon ranking test. The proposed feature extraction generates feature sets that serve as inputs to the type-2 fuzzy classifier. Uncertainty, noise and outliers that are common in the proteomic data motivate the use of type-2 fuzzy system. Tabu search is applied for structure learning of the fuzzy classifier. Experiments are performed using two benchmark proteomic datasets for the prediction of ovarian and pancreatic cancer. The dominance of the suggested feature extraction as well as type-2 fuzzy classifier against their competing methods is showcased through experimental results. The proposed approach therefore is helpful to clinicians and practitioners as it can be implemented as a medical decision support system in practice.

The Accuracy of National Body Fat Cutoff Levels in the Prediction of Elevated Blood Pressure among Brazilian Male Adolescents

Objective: To assess viability of the development of percentage body fat cutoffs based on blood pressure values in Brazilian adolescents.Methods: A cross-sectional study was conducted with a sample of 358 male subjects from 8 to 18 years old. Blood pressure was measured by the oscilometric method, and body composition was measured by dual-energy X-ray absorptiometry (DXA).Results: For the identification of elevated blood pressure, these nationally developed body fat cutoffs presented relative accuracy. The cutoffs were significantly associated with elevated blood pressure [odds ratio = 5.91 (95% confidence interval: 3.54-9.86)].Conclusions: Development of national body fat cutoffs is viable, because presence of high accuracy is an indication of elevated blood pressure.

Prediction of the rate of decline in fetal hemoglobin levels between first and second transfusions in red cell alloimmune disease

Objective To determine variables that predict the rate of decline in fetal hemoglobin levels in alloimmune disease. Method Retrospective review of singleton pregnancies that underwent first and second intrauterine transfusions for treatment of fetal anemia because of maternal Rh alloimmunization in a tertiary referral center. Results Forty-one first intrauterine transfusions were performed at 26.1?weeks (standard deviation, SD, 4.6), mean volume of blood transfused was 44.4?mL (SD 23.5) and estimated feto-placental volume expansion was 51.3% (SD 14.5%). Between first and second transfusion, hemoglobin levels reduced on average 0.40?g/dl/day (SD 0.25). Stepwise multiple regression analysis demonstrated that this rate significantly correlated with hemoglobin levels after the first transfusion, the interval between both procedures, and middle cerebral artery systolic velocity before the second transfusion. Conclusion The rate of decline in fetal hemoglobin levels between first and second transfusions in alloimmune disease can be predicted by a combination of hemoglobin levels after the first transfusion, interval between both procedures, and middle cerebral artery systolic velocity before the second transfusion. (C) 2012 John Wiley & Sons, Ltd.