Method of numerical correction of errors occasioned by delay of records during the monitoring of environmental variables of interest for animal production

Thermal and air conditions inside animal facilities change during the day due to the influence of the external environment. For statistical and geostatistical analyses to be representative, a large number of points spatially distributed in the facility area must be monitored. This work suggests that the time variation of environmental variables of interest for animal production, monitored within animal facility, can be modeled accurately from discrete-time records. The aim of this study was to develop a numerical method to correct the temporal variations of these environmental variables, transforming the data so that such observations are independent of the time spent during the measurement. The proposed method approached values recorded with time delays to those expected at the exact moment of interest, if the data were measured simultaneously at the moment at all points distributed spatially. The correction model for numerical environmental variables was validated for environmental air temperature parameter, and the values corrected by the method did not differ by Tukey's test at 5% significance of real values recorded by data loggers.

Treatment delay and radiological errors in patients with bone metastases

During routine investigations, we are surprised to find that therapy for bone metastases is sometimes delayed for a considerable period of time. To determine the extent of this delay and its causes, we reviewed the medical records of symptomatic patients seen at our hospital who had been recently diagnosed as having bone metastases for the last four years. The treatment delay was defined as the interval between presentation with symptoms and definitive treatment for bone metastases. The diagnostic delay was defined as the interval between presentation with symptoms and diagnosis of bone metastases. The results of diagnostic radiological examinations were also reviewed for errors. The study population included 76 males and 34 females with a median age of 66 years. Most bone metastases were diagnosed radiologically. Over 75% of patients were treated with radiotherapy. The treatment delay ranged from 2 to 307 days, with a mean of 53.3 days. In 490 radiological studies reviewed, we identified 166 (33.9%) errors concerning 62 (56.4%) patients. The diagnostic delay was significantly longer for patients with radiological errors than for patients without radiological errors (P < 0.001), and much of it was due to radiological errors. In conclusion, the treatment delay in patients with symptomatic bone metastases was much longer than expected, and much of it was caused by radiological errors. Considerable efforts should therefore be made to more carefully examine the radiological studies in order to ensure prompt treatment of bone metastases.

Characteristic Approach to Predicting Analysts’ Forecast Errors in the Finnish Market

Extensive literature shows that analysts’ forecasts and recommendations are often biased. Thus, it is important for the financial market to be able to recognize this bias to be able to correctly valuate public companies. This thesis uses characteristic approach, which was introduced by So (2013, pp. 615-640), to forecast analysts’ forecast errors and tests if predictable forecast error is fully incorporated into share prices. Data is collected of listed Finnish companies. Thesis’ timeframe spans over ten years from 2004 to 2013 consisting of 788 firm-years. Although there is earlier evidence that the characteristic approach is able to predict analysts’ forecast errors, no support for this is found in the Finnish market. This thesis contributes to the current knowledge by showing that the characteristic approach does not work universally as such but requires development to work especially in the smaller markets.

Errors in nonword repetition: bridging short- and long-term memory

According to the working memory model, the phonological loop is the component of working memory specialized in processing and manipulating limited amounts of speech-based information. The Children's Test of Nonword Repetition (CNRep) is a suitable measure of phonological short-term memory for English-speaking children, which was validated by the Brazilian Children's Test of Pseudoword Repetition (BCPR) as a Portuguese-language version. The objectives of the present study were: i) to investigate developmental aspects of the phonological memory processing by error analysis in the nonword repetition task, and ii) to examine phoneme (substitution, omission and addition) and order (migration) errors made in the BCPR by 180 normal Brazilian children of both sexes aged 4-10, from preschool to 4th grade. The dominant error was substitution [F(3,525) = 180.47; P < 0.0001]. The performance was age-related [F(4,175) = 14.53; P < 0.0001]. The length effect, i.e., more errors in long than in short items, was observed [F(3,519) = 108.36; P < 0.0001]. In 5-syllable pseudowords, errors occurred mainly in the middle of the stimuli, before the syllabic stress [F(4,16) = 6.03; P = 0.003]; substitutions appeared more at the end of the stimuli, after the stress [F(12,48) = 2.27; P = 0.02]. In conclusion, the BCPR error analysis supports the idea that phonological loop capacity is relatively constant during development, although school learning increases the efficiency of this system. Moreover, there are indications that long-term memory contributes to holding memory trace. The findings were discussed in terms of distinctiveness, clustering and redintegration hypotheses.

Relationship between Brazilian airline pilot errors and time of day

Flight safety is one of the most important and frequently discussed issues in aviation. Recent accident inquiries have raised questions as to how the work of flight crews is organized and the extent to which these conditions may have been contributing factors to accidents. Fatigue is based on physiologic limitations, which are reflected in performance deficits. The purpose of the present study was to provide an analysis of the periods of the day in which pilots working for a commercial airline presented major errors. Errors made by 515 captains and 472 copilots were analyzed using data from flight operation quality assurance systems. To analyze the times of day (shifts) during which incidents occurred, we divided the light-dark cycle (24:00) in four periods: morning, afternoon, night, and early morning. The differences of risk during the day were reported as the ratio of morning to afternoon, morning to night and morning to early morning error rates. For the purposes of this research, level 3 events alone were taken into account, since these were the most serious in which company operational limits were exceeded or when established procedures were not followed. According to airline flight schedules, 35% of flights take place in the morning period, 32% in the afternoon, 26% at night, and 7% in the early morning. Data showed that the risk of errors increased by almost 50% in the early morning relative to the morning period (ratio of 1:1.46). For the period of the afternoon, the ratio was 1:1.04 and for the night a ratio of 1:1.05 was found. These results showed that the period of the early morning represented a greater risk of attention problems and fatigue.

The FCLT with Dependent Errors: an Helicopter Tour of the Quality of the Approximation

This note investigates the adequacy of the finite-sample approximation provided by the Functional Central Limit Theorem (FCLT) when the errors are allowed to be dependent. We compare the distribution of the scaled partial sums of some data with the distribution of the Wiener process to which it converges. Our setup is purposely very simple in that it considers data generated from an ARMA(1,1) process. Yet, this is sufficient to bring out interesting conclusions about the particular elements which cause the approximations to be inadequate in even quite large sample sizes.

Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

Correcting the Errors : A Note on Volatility Forecast Evaluation Based on High-Frequency Data and Realized Volatilities

This note develops general model-free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit the recent asymptotic distributional results in Barndorff-Nielsen and Shephard (2002a), are both easy to implement and highly accurate in empirically realistic situations. On properly accounting for the measurement errors in the volatility forecast evaluations reported in Andersen, Bollerslev, Diebold and Labys (2003), the adjustments result in markedly higher estimates for the true degree of return-volatility predictability.