Tests of sunspot number sequences: 3. Effects of regression procedures on the calibration of historic sunspot data

We use sunspot group observations from the Royal Greenwich Observatory (RGO) to investigate the effects of intercalibrating data from observers with different visual acuities. The tests are made by counting the number of groups RB above a variable cut-off threshold of observed total whole-spot area (uncorrected for foreshortening) to simulate what a lower acuity observer would have seen. The synthesised annual means of RB are then re-scaled to the full observed RGO group number RA using a variety of regression techniques. It is found that a very high correlation between RA and RB (rAB > 0.98) does not prevent large errors in the intercalibration (for example sunspot maximum values can be over 30 % too large even for such levels of rAB). In generating the backbone sunspot number (RBB), Svalgaard and Schatten (2015, this issue) force regression fits to pass through the scatter plot origin which generates unreliable fits (the residuals do not form a normal distribution) and causes sunspot cycle amplitudes to be exaggerated in the intercalibrated data. It is demonstrated that the use of Quantile-Quantile (“Q  Q”) plots to test for a normal distribution is a useful indicator of erroneous and misleading regression fits. Ordinary least squares linear fits, not forced to pass through the origin, are sometimes reliable (although the optimum method used is shown to be different when matching peak and average sunspot group numbers). However, other fits are only reliable if non-linear regression is used. From these results it is entirely possible that the inflation of solar cycle amplitudes in the backbone group sunspot number as one goes back in time, relative to related solar-terrestrial parameters, is entirely caused by the use of inappropriate and non-robust regression techniques to calibrate the sunspot data.

Tests of sunspot number sequences: 1. Using ionosonde data

More than 70 years ago it was recognised that ionospheric F2-layer critical frequencies [foF2] had a strong relationship to sunspot number. Using historic datasets from the Slough and Washington ionosondes, we evaluate the best statistical fits of foF2 to sunspot numbers (at each Universal Time [UT] separately) in order to search for drifts and abrupt changes in the fit residuals over Solar Cycles 17-21. This test is carried out for the original composite of the Wolf/Zürich/International sunspot number [R], the new “backbone” group sunspot number [RBB] and the proposed “corrected sunspot number” [RC]. Polynomial fits are made both with and without allowance for the white-light facular area, which has been reported as being associated with cycle-to-cycle changes in the sunspot number - foF2 relationship. Over the interval studied here, R, RBB, and RC largely differ in their allowance for the “Waldmeier discontinuity” around 1945 (the correction factor for which for R, RBB and RC is, respectively, zero, effectively over 20 %, and explicitly 11.6 %). It is shown that for Solar Cycles 18-21, all three sunspot data sequences perform well, but that the fit residuals are lowest and most uniform for RBB. We here use foF2 for those UTs for which R, RBB, and RC all give correlations exceeding 0.99 for intervals both before and after the Waldmeier discontinuity. The error introduced by the Waldmeier discontinuity causes R to underestimate the fitted values based on the foF2 data for 1932-1945 but RBB overestimates them by almost the same factor, implying that the correction for the Waldmeier discontinuity inherent in RBB is too large by a factor of two. Fit residuals are smallest and most uniform for RC and the ionospheric data support the optimum discontinuity multiplicative correction factor derived from the independent Royal Greenwich Observatory (RGO) sunspot group data for the same interval.

ESTIMATING SENSORY SHELF LIFE OF CHOCOLATE AND CARROT CUPCAKES USING ACCEPTANCE TESTS

This study determined the sensory shelf life of a commercial brand of chocolate and carrot cupcakes, aiming at increasing the current 120 days of shelf life to 180. Appearance, texture, flavor and overall quality of cakes stored at six different storage times were evaluated by 102 consumers. The data were analyzed by analysis of variance and linear regression. For both flavors, the texture presented a greater loss in acceptance during the storage period, showing an acceptance mean close to indifference on the hedonic scale at 120 days. Nevertheless, appearance, flavor and overall quality stayed acceptable up to 150 days. The end of shelf life was estimated at about 161 days for chocolate cakes and 150 days for carrot cakes. This study showed that the current 120 days of shelf life can be extended to 150 days for carrot cake and to 160 days for chocolate cake. However, the 180 days of shelf life desired by the company were not achieved. PRACTICAL APPLICATIONS This research shows the adequacy of using sensory acceptance tests to determine the shelf life of two food products (chocolate and carrot cupcakes). This practical application is useful because the precise determination of the shelf life of a food product is of vital importance for its commercial success. The maximum storage time should always be evaluated in the development or reformulation of new products, changes in packing or storage conditions. Once the physical-chemical and microbiological stability of a product is guaranteed, sensorial changes that could affect consumer acceptance will determine the end of the shelf life of a food product. Thus, the use of sensitive and reliable methods to estimate the sensory shelf life of a product is very important. Findings show the importance of determining the shelf life of each product separately and to avoid using the shelf time estimated for a specific product on other, similar products.

Research on Candida dubliniensis in a Brazilian yeast collection obtained from cardiac transplant, tuberculosis, and HIV-positive patients, and evaluation of phenotypic tests using agar screening methods

The aim of this study was to research Candida dubliniensis among isolates present in a Brazilian yeast collection and to evaluate the main phenotypic methods for discrimination between C. albicans and C. dubliniensis from oral cavity. A total of 200 isolates, presumptively identified as C. albicans or C. dubliniensis obtained from heart transplant patients under immunosuppressive therapy, tuberculosis patients under antibiotic therapy, HIV-positive patients under antiretroviral therapy, and healthy subjects, were analyzed using the following phenotypic tests: formation and structural arrangement of chlamydospores on corn meal agar, casein agar, tobacco agar, and sunflower seed agar; growth at 45 degrees C; and germ tube formation. All strains were analyzed by polymerase chain reaction (PCR). In a preliminary screen for C. dubliniensis, 48 of the 200 isolates on corn meal agar, 30 of the 200 on casein agar, 16 of the 200 on tobacco agar, and 15 of the 200 on sunflower seed agar produced chlamydoconidia; 27 of the 200 isolates showed no or poor growth at 45 degrees C. All isolates were positive for germ tube formation. These isolates were considered suggestive of C. dubliniensis. All of them were subjected to PCR analysis using C. dubliniensis-specific primers. C. dubliniensis isolates were not found. C. dubliniensis isolates were not recovered in this study done with immunocompromised patients. Sunflower seed agar was the medium with the smallest number of isolates of C. albicans suggestive of C. dubliniensis. None of the phenotypic methods was 100% effective for discrimination between C. albicans and C. dubliniensis. (C) 2011 Elsevier Inc. All rights reserved.

Bayesian Estimation for Performance Measures of Two Diagnostic Tests in the Presence of Verification Bias

Sensitivity and specificity are measures that allow us to evaluate the performance of a diagnostic test. In practice, it is common to have situations where a proportion of selected individuals cannot have the real state of the disease verified, since the verification could be an invasive procedure, as occurs with biopsy. This happens, as a special case, in the diagnosis of prostate cancer, or in any other situation related to risks, that is, not practicable, nor ethical, or in situations with high cost. For this case, it is common to use diagnostic tests based only on the information of verified individuals. This procedure can lead to biased results or workup bias. In this paper, we introduce a Bayesian approach to estimate the sensitivity and the specificity for two diagnostic tests considering verified and unverified individuals, a result that generalizes the usual situation based on only one diagnostic test.

Checking Completeness of Tests for Finite State Machines

In testing from a Finite State Machine (FSM), the generation of test suites which guarantee full fault detection, known as complete test suites, has been a long-standing research topic. In this paper, we present conditions that are sufficient for a test suite to be complete. We demonstrate that the existing conditions are special cases of the proposed ones. An algorithm that checks whether a given test suite is complete is given. The experimental results show that the algorithm can be used for relatively large FSMs and test suites.

Orthodontic brackets removal under shear and tensile bond strength resistance tests - a comparative test between light sources

We have investigated if a new LEDs system has enough efficient energy to promote efficient shear and tensile bonding strength resistance under standardized tests. LEDs 470 +/- 10 nm can be used to photocure composite during bracket fixation. Advantages considering resistance to tensile and shear bonding strength when these systems were used are necessary to justify their clinical use. Forty eight human extracted premolars teeth and two light sources were selected, one halogen lamp and a LEDs system. Brackets for premolar were bonded through composite resin. Samples were submitted to standardized tests. A comparison between used sources under shear bonding strength test, obtained similar results; however, tensile bonding test showed distinct results: a statistical difference at a level of 1% between exposure times (40 and 60 seconds) and even to an interaction between light source and exposure time. The best result was obtained with halogen lamp use by 60 seconds, even during re-bonding; however LEDs system can be used for bonding and re-bonding brackets if power density could be increased.

Improved score tests in symmetric linear regression models

The class of symmetric linear regression models has the normal linear regression model as a special case and includes several models that assume that the errors follow a symmetric distribution with longer-than-normal tails. An important member of this class is the t linear regression model, which is commonly used as an alternative to the usual normal regression model when the data contain extreme or outlying observations. In this article, we develop second-order asymptotic theory for score tests in this class of models. We obtain Bartlett-corrected score statistics for testing hypotheses on the regression and the dispersion parameters. The corrected statistics have chi-squared distributions with errors of order O(n(-3/2)), n being the sample size. The corrections represent an improvement over the corresponding original Rao`s score statistics, which are chi-squared distributed up to errors of order O(n(-1)). Simulation results show that the corrected score tests perform much better than their uncorrected counterparts in samples of small or moderate size.

Local influence for Student-t partially linear models

In this paper we extend partial linear models with normal errors to Student-t errors Penalized likelihood equations are applied to derive the maximum likelihood estimates which appear to be robust against outlying observations in the sense of the Mahalanobis distance In order to study the sensitivity of the penalized estimates under some usual perturbation schemes in the model or data the local influence curvatures are derived and some diagnostic graphics are proposed A motivating example preliminary analyzed under normal errors is reanalyzed under Student-t errors The local influence approach is used to compare the sensitivity of the model estimates (C) 2010 Elsevier B V All rights reserved

Size and power properties of some tests in the Birnbaum-Saunders regression model

The Birnbaum-Saunders distribution has been used quite effectively to model times to failure for materials subject to fatigue and for modeling lifetime data. In this paper we obtain asymptotic expansions, up to order n(-1/2) and under a sequence of Pitman alternatives, for the non-null distribution functions of the likelihood ratio, Wald, score and gradient test statistics in the Birnbaum-Saunders regression model. The asymptotic distributions of all four statistics are obtained for testing a subset of regression parameters and for testing the shape parameter. Monte Carlo simulation is presented in order to compare the finite-sample performance of these tests. We also present two empirical applications. (C) 2010 Elsevier B.V. All rights reserved.

Signed likelihood ratio tests in the Birnbaum-Saunders regression model

The Birnbaum-Saunders regression model is becoming increasingly popular in lifetime analyses and reliability studies. In this model, the signed likelihood ratio statistic provides the basis for testing inference and construction of confidence limits for a single parameter of interest. We focus on the small sample case, where the standard normal distribution gives a poor approximation to the true distribution of the statistic. We derive three adjusted signed likelihood ratio statistics that lead to very accurate inference even for very small samples. Two empirical applications are presented. (C) 2010 Elsevier B.V. All rights reserved.

Comparing diagnostic tests with missing data

When missing data occur in studies designed to compare the accuracy of diagnostic tests, a common, though naive, practice is to base the comparison of sensitivity, specificity, as well as of positive and negative predictive values on some subset of the data that fits into methods implemented in standard statistical packages. Such methods are usually valid only under the strong missing completely at random (MCAR) assumption and may generate biased and less precise estimates. We review some models that use the dependence structure of the completely observed cases to incorporate the information of the partially categorized observations into the analysis and show how they may be fitted via a two-stage hybrid process involving maximum likelihood in the first stage and weighted least squares in the second. We indicate how computational subroutines written in R may be used to fit the proposed models and illustrate the different analysis strategies with observational data collected to compare the accuracy of three distinct non-invasive diagnostic methods for endometriosis. The results indicate that even when the MCAR assumption is plausible, the naive partial analyses should be avoided.

Small-Sample Corrections for Score Tests in Birnbaum-Saunders Regressions

In this article, we deal with the issue of performing accurate small-sample inference in the Birnbaum-Saunders regression model, which can be useful for modeling lifetime or reliability data. We derive a Bartlett-type correction for the score test and numerically compare the corrected test with the usual score test and some other competitors.

Reliability Nonparametric Bayesian Estimation in Parallel Systems

Relevant results for (sub-)distribution functions related to parallel systems are discussed. The reverse hazard rate is defined using the product integral. Consequently, the restriction of absolute continuity for the involved distributions can be relaxed. The only restriction is that the sets of discontinuity points of the parallel distributions have to be disjointed. Nonparametric Bayesian estimators of all survival (sub-)distribution functions are derived. Dual to the series systems that use minimum life times as observations, the parallel systems record the maximum life times. Dirichlet multivariate processes forming a class of prior distributions are considered for the nonparametric Bayesian estimation of the component distribution functions, and the system reliability. For illustration, two striking numerical examples are presented.