Assessing the accuracy and dispersion of real estate investment forecasts

Existing empirical evidence has frequently observed that professional forecasters are conservative and display herding behaviour. Whilst a large number of papers have considered equities as well as macroeconomic series, few have considered the accuracy of forecasts in alternative asset classes such as real estate. We consider the accuracy of forecasts for the UK commercial real estate market over the period 1999-2011. The results illustrate that forecasters display a tendency to under-estimate growth rates during strong market conditions and over-estimate when the market is performing poorly. This conservatism not only results in smoothed estimates but also implies that forecasters display herding behaviour. There is also a marked difference in the relative accuracy of capital and total returns versus rental figures. Whilst rental growth forecasts are relatively accurate, considerable inaccuracy is observed with respect to capital value and total returns.

The accuracy of HADS and GHQ-12 in detecting psychiatric morbidity in breast cancer patients

Objective: Psychological problems should be identified in breast cancer patients proactively if doctors and nurses are to help them cope with the challenges imposed by their illness. Screening is one possible way to identify emotional problems proactively. Self-report questionnaires can be useful alternatives to carrying out psychiatric interviews during screening, because interviewing a large number of patients can be impractical due to limited resources. Two such measures are the Hospital Anxiety and Depression Scale (HADS) and the General Health Questionnaire-12 (GHQ-12). Method: The present study aimed to compare the performance of the GHQ-12, and the HADS Unitary Scale and its subscales to that of the Schedule for Affective Disorders and Schizophrenia (SADS) in identifying patients with affective disorders, including DSM major depression and generalized anxiety disorder. The sample consisted of 296 female breast cancer patients who underwent surgery for breast cancer a year previously. Results: A small number of patients (11%) were identified as having DSM major depression or generalized anxiety disorder based on SADS score. The findings indicate that the optimal thresholds in detecting generalized anxiety disorder and DSM major depression with the HADS anxiety and depression subscales were ≥ 8 and ≥ 7, with 93.3% and 77.3% sensitivity, respectively, and 77.9% and 87.1% specificity, respectively. They also had a 21% and 36% positive predictive value, respectively. Using the HADS Unitary Scale the optimal threshold for detecting affective disorders was ≥ 12, with 88.9% sensitivity, 80.7% specificity, and a 35% positive predictive value. In detecting affective disorders, the optimal threshold on the GHQ-12 was ≥ 2, with 77.8% sensitivity and 70.2% specificity. This scale also had a 24% positive predictive value. In detecting generalized anxiety disorder and DSM major depression, the optimal thresholds on the GHQ-12 were ≥ 2 and ≥ 4 with 73.3% and 77.3% sensitivity, respectively, and 67.5% and 82% specificity, respectively. The scale also had 12% and 29% positive predictive values, respectively. Conclusion: The HADS Unitary Scale and its subscales were effective in identifying affective disorders. They can be used as screening measures in breast cancer patients. The GHQ-12 was less accurate in detecting affective disorders than the HADS, but it can also be used as a screening instrument to detect affective disorders, generalized anxiety disorder, and DSM major depression.

Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology

Genome-wide association studies (GWAS) have been widely used in genetic dissection of complex traits. However, common methods are all based on a fixed-SNP-effect mixed linear model (MLM) and single marker analysis, such as efficient mixed model analysis (EMMA). These methods require Bonferroni correction for multiple tests, which often is too conservative when the number of markers is extremely large. To address this concern, we proposed a random-SNP-effect MLM (RMLM) and a multi-locus RMLM (MRMLM) for GWAS. The RMLM simply treats the SNP-effect as random, but it allows a modified Bonferroni correction to be used to calculate the threshold p value for significance tests. The MRMLM is a multi-locus model including markers selected from the RMLM method with a less stringent selection criterion. Due to the multi-locus nature, no multiple test correction is needed. Simulation studies show that the MRMLM is more powerful in QTN detection and more accurate in QTN effect estimation than the RMLM, which in turn is more powerful and accurate than the EMMA. To demonstrate the new methods, we analyzed six flowering time related traits in Arabidopsis thaliana and detected more genes than previous reported using the EMMA. Therefore, the MRMLM provides an alternative for multi-locus GWAS.

A method of evaluating the accuracy of human body thermoregulation models

Human Body Thermoregulation Models have been widely used in the field of human physiology or thermal comfort studies. However there are few studies on the evaluation method for these models. This paper summarises the existing evaluation methods and critically analyses the flaws. Based on that, a method for the evaluating the accuracy of the Human Body Thermoregulation models is proposed. The new evaluation method contributes to the development of Human Body Thermoregulation models and validates their accuracy both statistically and empirically. The accuracy of different models can be compared by the new method. Furthermore, the new method is not only suitable for the evaluation of Human Body Thermoregulation Models, but also can be theoretically applied to the evaluation of the accuracy of the population-based models in other research fields.

An approximate dynamic programming approach for improving accuracy of lossy data compression by Bloom filters

Bloom filters are a data structure for storing data in a compressed form. They offer excellent space and time efficiency at the cost of some loss of accuracy (so-called lossy compression). This work presents a yes-no Bloom filter, which as a data structure consisting of two parts: the yes-filter which is a standard Bloom filter and the no-filter which is another Bloom filter whose purpose is to represent those objects that were recognised incorrectly by the yes-filter (that is, to recognise the false positives of the yes-filter). By querying the no-filter after an object has been recognised by the yes-filter, we get a chance of rejecting it, which improves the accuracy of data recognition in comparison with the standard Bloom filter of the same total length. A further increase in accuracy is possible if one chooses objects to include in the no-filter so that the no-filter recognises as many as possible false positives but no true positives, thus producing the most accurate yes-no Bloom filter among all yes-no Bloom filters. This paper studies how optimization techniques can be used to maximize the number of false positives recognised by the no-filter, with the constraint being that it should recognise no true positives. To achieve this aim, an Integer Linear Program (ILP) is proposed for the optimal selection of false positives. In practice the problem size is normally large leading to intractable optimal solution. Considering the similarity of the ILP with the Multidimensional Knapsack Problem, an Approximate Dynamic Programming (ADP) model is developed making use of a reduced ILP for the value function approximation. Numerical results show the ADP model works best comparing with a number of heuristics as well as the CPLEX built-in solver (B&B), and this is what can be recommended for use in yes-no Bloom filters. In a wider context of the study of lossy compression algorithms, our researchis an example showing how the arsenal of optimization methods can be applied to improving the accuracy of compressed data.

Accuracy of Periapical Radiography and Cone-Beam Computed Tomography Scans in Diagnosing Apical Periodontitis Using Histopathological Findings as a Gold Standard

Introduction: The aim of this study was to evaluate the accuracy of two imaging methods in diagnosing apical periodontitis (AP) using histopathological findings as a gold standard. Methods: The periapex of 83 treated or untreated roots of dogs` teeth was examined using periapical radiography (PR), cone-beam computed tomography (CBCT) scans, and histology. Sensitivity, specificity, predictive values, and accuracy of PR and CBCT diagnosis were calculated. Results: PR detected AP in 71% of roots, a CBCT scan detected AP in 84%, and AP was histologically diagnosed in 93% (p = 0.001). Overall, sensitivity was 0.77 and 0.91 for PR and CBCT, respectively. Specificity was 1 for both. Negative predictive value was 0.25 and 0.46 for PR and CBCT, respectively. Positive predictive value was 1 for both. Diagnostic accuracy (true positives + true negatives) was 0.78 and 0.92 for PR and CBCT (p = 0.028), respectively. Conclusion: A CBCT scan was more sensitive in detecting AP compared with PR, which was more likely to miss AP when it was still present. (J Endod 2009;35:1009-1012)

Ex Vivo Accuracy of an Apex Locator Using Digital Signal Processing in Primary Teeth

The purpose of this study was to evaluate ex vivo the accuracy an electronic apex locator during root canal length determination in primary molars. Methods: One calibrated examiner determined the root canal length in 15 primary molars (total=34 root canals) with different stages of root resorption. Root canal length was measured both visually, with the placement of a K-file 1 mm short of the apical foramen or the apical resorption bevel, and electronically using an electronic apex locator (Digital Signal Processing). Data were analyzed statistically using the intraclass correlation (ICC) test. Results: Comparing the actual and electronic root canal length measurements in the primary teeth showed a high correlation (ICC=0.95) Conclusions: The Digital Signal Processing apex locator is useful and accurate for apex foramen location during root canal length measurement in primary molars. (Pediatr Dent 200937:320-2) Received April 75, 2008 vertical bar Lost Revision August 21, 2008 vertical bar Revision Accepted August 22, 2008

Ex vivo evaluation of the accuracy of two electronic apex locators during root canal length determination in primary teeth

Aim To evaluate ex vivo the accuracy of two electronic apex locators during root canal length determination in primary incisor and molar teeth with different stages of physiological root resorption. Methodology One calibrated examiner determined the root canal length in 17 primary incisors and 16 primary molars (total of 57 root canals) with different stages of root resorption based on the actual canal length and using two electronic apex locators. Root canal length was measured both visually, with the placement of a K-file 1 mm short of the apical foramen or the apical resorption bevel, and electronically using two electronic apex locators (Root ZX II - J. Morita Corp. and Mini Apex Locator - SybronEndo) according to the manufacturers` instructions. Data were analysed statistically using the intraclass correlation (ICC) test. Results Comparison of the actual root canal length and the electronic root canal length measurements revealed high correlation (ICC = 0.99), regardless of the tooth type (single-rooted and multi-rooted teeth) or the presence/absence of physiological root resorption. Conclusions Root ZX II and Mini Apex Locator proved useful and accurate for apex foramen location during root canal length measurement in primary incisors and molars.

Accuracy of the iPex multi-frequency electronic apex locator in primary molars: an ex vivo study

P>Aim To evaluate ex vivo the accuracy of the iPex multi-frequency electronic apex locator (NSK Ltd, Tokyo, Japan) for working length determination in primary molar teeth. Methodology One calibrated examiner determined the working length in 20 primary molar teeth (total of 33 root canals). Working length was measured both visually, with the placement of a K-file 1 mm short of the apical foramen or the most coronal limit of root resorption, and electronically using the electronic apex locator iPex, according to the manufacturers` instructions. Data were analysed statistically using the intraclass correlation (ICC) test. Results Comparison of the actual and the electronic measurements revealed high correlation (ICC = 0.99) between the methods, regardless of the presence or absence of physiological root resorption. Conclusions In this laboratory study, the iPex accurately identified the apical foramen or the apical opening location for working length measurement in primary molar teeth.

Bayesian network classifiers: Beyond classification accuracy

This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing the tradeoff between the precise probability estimates produced by time consuming unrestricted Bayesian networks and the computational efficiency of Naive Bayes (NB) classifiers. The proposed approach is based on the fundamental principles of the Heuristic Search Bayesian network learning. The Markov Blanket concept, as well as a proposed ""approximate Markov Blanket"" are used to reduce the number of nodes that form the Bayesian network to be induced from data. Consequently, the usually high computational cost of the heuristic search learning algorithms can be lessened, while Bayesian network structures better than NB can be achieved. The resulting algorithms, called DMBC (Dynamic Markov Blanket Classifier) and A-DMBC (Approximate DMBC), are empirically assessed in twelve domains that illustrate scenarios of particular interest. The obtained results are compared with NB and Tree Augmented Network (TAN) classifiers, and confinn that both proposed algorithms can provide good classification accuracies and better probability estimates than NB and TAN, while being more computationally efficient than the widely used K2 Algorithm.

Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for large-scale systems. Nonetheless, a critical obstacle, which needs to be overcome in MPC, is the large computational burden when a large-scale system is considered or a long prediction horizon is involved. In order to solve this problem, we use an adaptive prediction accuracy (APA) approach that can reduce the computational burden almost by half. The proposed MPC scheme with this scheme is tested on the northern Dutch water system, which comprises Lake IJssel, Lake Marker, the River IJssel and the North Sea Canal. The simulation results show that by using the MPC-APA scheme, the computational time can be reduced to a large extent and a flood protection problem over longer prediction horizons can be well solved.

Forecasting accuracy and estimation uncertainty using VAR models with short- and long-term economic restrictions: a Monte-Carlo study

Using vector autoregressive (VAR) models and Monte-Carlo simulation methods we investigate the potential gains for forecasting accuracy and estimation uncertainty of two commonly used restrictions arising from economic relationships. The Örst reduces parameter space by imposing long-term restrictions on the behavior of economic variables as discussed by the literature on cointegration, and the second reduces parameter space by imposing short-term restrictions as discussed by the literature on serial-correlation common features (SCCF). Our simulations cover three important issues on model building, estimation, and forecasting. First, we examine the performance of standard and modiÖed information criteria in choosing lag length for cointegrated VARs with SCCF restrictions. Second, we provide a comparison of forecasting accuracy of Ötted VARs when only cointegration restrictions are imposed and when cointegration and SCCF restrictions are jointly imposed. Third, we propose a new estimation algorithm where short- and long-term restrictions interact to estimate the cointegrating and the cofeature spaces respectively. We have three basic results. First, ignoring SCCF restrictions has a high cost in terms of model selection, because standard information criteria chooses too frequently inconsistent models, with too small a lag length. Criteria selecting lag and rank simultaneously have a superior performance in this case. Second, this translates into a superior forecasting performance of the restricted VECM over the VECM, with important improvements in forecasting accuracy ñreaching more than 100% in extreme cases. Third, the new algorithm proposed here fares very well in terms of parameter estimation, even when we consider the estimation of long-term parameters, opening up the discussion of joint estimation of short- and long-term parameters in VAR models.

Improvement on the sales forecast accuracy for a fast growing company by the best combination of historical data usage and clients segmentation

Industrial companies in developing countries are facing rapid growths, and this requires having in place the best organizational processes to cope with the market demand. Sales forecasting, as a tool aligned with the general strategy of the company, needs to be as much accurate as possible, in order to achieve the sales targets by making available the right information for purchasing, planning and control of production areas, and finally attending in time and form the demand generated. The present dissertation uses a single case study from the subsidiary of an international explosives company based in Brazil, Maxam, experiencing high growth in sales, and therefore facing the challenge to adequate its structure and processes properly for the rapid growth expected. Diverse sales forecast techniques have been analyzed to compare the actual monthly sales forecast, based on the sales force representatives’ market knowledge, with forecasts based on the analysis of historical sales data. The dissertation findings show how the combination of both qualitative and quantitative forecasts, by the creation of a combined forecast that considers both client´s demand knowledge from the sales workforce with time series analysis, leads to the improvement on the accuracy of the company´s sales forecast.