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Background Acute pancreatitis after liver resection is a rare but serious complication, and few cases have been described in the literature. Extended lymphadenectomy, and long ischemia due to the Pringle maneuver could be responsible of post-liver resection acute pancreatitis, but the exact causes of AP after hepatectomy remain unclear. Cases presentation We report here three cases of AP after hepatectomy and we strongly hypothesize that this is due to the bile leakage white test. 502 hepatectomy were performed at our center and 3 patients (0.6%) experienced acute pancreatitis after LR and all of these three patients underwent the white test at the end of the liver resection. None underwent additionally lymphadenectomy to the liver resection. All patient had a white-test during the liver surgery. We identified distal implantation of the cystic duct in these three patients as a potential cause for acute pancreatitis. Conclusion The white test is useful for detection of bile leakage after liver resection, but we do not recommend a systematic use after LR, because severe acute pancreatitis can be lethal for the patient, especially in case of distal cystic implantation which may facilitate reflux in the main pancreatic duct.

Teacher performance pay is often introduced with the goal of reducing gaps in test scores across groups, yet little is known about how well they achieve this aim. We ask, “Do test score-based teacher incentives impact the Black–White test score gap?” Using student–teacher matched data and a difference-in-differences approach in which the performance of a teacher’s students before and after the policy is compared, we find that performance pay increases the conditional Black–White gap. The effect is particularly evident when bonuses are large, consistent with a causal response to performance pay.

In recent decades, the black–white test score disparity has decreased, and the test score disparity between children of high- versus low-income parents has increased. This study focuses on a comparison that has, to date, fallen between the separate literatures on these diverging trends: black and white students whose parents have similarly low, middle, or high incomes (i.e., same income or race within income). To do so, I draw on three nationally representative data sets on 9th or 10th graders, covering 1960 to 2009, that contain information on students’ math test scores. I find that math test score disparities between black and white students with same-income parents are to black students’ disadvantage. Although these disparities have decreased since 1960, in 2009 they remained substantively large, statistically significant, and largest between children of the highest-income parents. Furthermore, family and school characteristics that scholars commonly use to explain test score disparities by race or income account for markedly decreasing shares of race-within-income disparities over time. The study integrates the literatures on test score disparities by race and income with attention to the historical and continued structural influence of race, net of parental income, on students’ educational experiences and test score outcomes.

Given the effect that outliers can have on regression and specification testing, a vastly used robustification strategy by practitioners consists in: (i) starting the empirical analysis with an outlier detection procedure to deselect atypical data values; then (ii) continuing the analysis with the selected non-outlying observations. The repercussions of such robustifying procedure on the asymptotic properties of subsequent inferential procedures are, however, underexplored. We study the effects of such a strategy on testing for heteroscedasticity. Specifically, using weighted and marked empirical processes of residuals theory, we show that the White test implemented after the outlier detection and removal is asymptotically chi-square if the underlying errors are symmetric. In a simulation study, we show that-depending on the type of outliers-the standard White test can be either severely undersized or oversized, as well as have trivial power. The statistic applied after deselecting outliers has good finite sample properties under symmetry but can suffer from size distortions under asymmetric errors. Given these results, we devise an empirical modeling strategy to guide practitioners whose preferred approach is to remove outliers from the sample.

Background Intra-operative bile leakage testing is very important in partial liver resection in living donor liver transplant as it allows detection of bile leaking points on the cut surface and decreases postoperative bile leak which is one of the most dreadful complication following liver transplant surgery. In this study we tried to assess the Effectiveness of White test versus conventional saline test in minimizing biliary leak in partial liver resection in living donor liver transplant. Objective In this study, we assess whether the White test is better than the conventional saline test for the intraoperative detection of biliary leakage in patients who will undergo partial liver resection as living donor liver transplant. Methodology In this study, we assess whether the White test is better than the conventional saline test for the intraoperative detection of bile leakage in patients who underwent partial liver resection as a living donor liver transplant. This study included 60 patients who received partial liver resection as a living donor liver transplant. The conventional saline test (injecting an isotonic sodium chloride solution through the cystic duct) was carried out in 30 patients and the White test (injecting a fat emulsion solution through the cystic duct) was carried out in 30 patients Results Incidence of postoperative bile leakage was compared between the conventional method and the White test. Bile leakage occurred in 8 patients (26.7%) in the conventional method group and in 2 patients (6.7%) in the White test group. In addition, the White test detected intraoperative a significantly higher number of bile leakage sites compared with the conventional method. The White test is better than the conventional test for the intraoperative detection of bile leakage. Conclusion Based on our study, we recommend that surgeons investigating bile leakage sites during liver resections should use the White test instead of the conventional saline test.

Background Hepatic surgery will be always one of the most challenging surgical procedures due to its anatomical and pathophysiologic varieties. Various problems may complicate the post-operative course. Bile leak remains a serious complication after hepatectomy. Bile leaks can significantly impact morbidity, mortality, and cost of treatment. Bile leak increases risk of sepsis, need for further intervention, either radiological or return to theatre, prolongs duration of intensive care unit and hospital stay, and can precipitate liver failure and death. Objective To assess the post-operative bile leak complication after using white test versus conventional saline test Patients and Methods A prospective study on 50 Hepatectomy cases divided into two groups, the first group consist of 25 cases using white test and the second group consist of 25 cases using conventional saline test from January 2019 to January 2020, at Hepatobiliary unit of the general surgery department of Ain Shams university hospitals. Results The White test has clear advantages in comparison with other bile leakage tests: it precisely detects bile leakages, regardless of size; it does not stain the resection surface, allowing it to be washed off and repeated ad infinitum; and it is safe, quick, and inexpensive. The white test is a feasible and sensitive bile leakage test with no obvious disadvantages. It could be a possible standardized method to prevent bile leakage in major liver resection. Conclusion Now after we discussed our study and reviewed other opinions discussing the optimal methods for intraoperative bile leakage testing, we have now settled on White test using fat emulsion solution to be best of all tests.

This article explores the phased development of a time series in Eviews and Excel. The ADF test, the Jarque-Bera test, the White test, the Akaike, Schwartz criteria and the AR (p) model were applied. In this article, the adequacy and quality of the model were tested to ensure the significance of the predicted estimates.

A recent paper by Lasker et al. provides a "gold standard" test for genetic causes of the US Black/white test score gap by simple regression estimation linking high-quality intelligence test scores to African/European genetic admixture proportions precisely measured with modern DNA technology. The paper gives new, empirically powerful evidence for a large genetic component to the gap. This note critically reviews some key findings of the paper from a social science perspective.

Research as to the size of the black-white test score gap often comes to contradictory conclusions. Recent literature has affirmed that the source of these contradictions and other controversies in education economics may be due to the fact that test scores contain only ordinal information. In this paper, I propose a normalization of test scores that is invariant to monotonic transformations. Under fairly weak assumptions, this metric has interval properties and thus solves the ordinality problem. The measure can serve as a valuable robustness check to ensure that any results are not simply statistical artifacts from the choice of scale.

We propose a new omnibus test for vector white noise using the maximum absolute auto-correlations and cross-correlations of the component series. Based on the newly established approximation by the $L_\infty$-norm of a normal random vector, the critical value of the test can be evaluated by bootstrapping from a multivariate normal distribution. In contrast to the conventional white noise test, the new method is proved to be valid for testing the departure from non-IID white noise. We illustrate the accuracy and the power of the proposed test by simulation, which also shows that the new test outperforms several commonly used methods including, for example, the Lagrange multiplier test and the multivariate Box-Pierce portmanteau tests especially when the dimension of time series is high in relation to the sample size. The numerical results also indicate that the performance of the new test can be further enhanced when it is applied to the pre-transformed data obtained via the time series principal component analysis proposed by Chang, Guo and Yao (2014). The proposed procedures have been implemented in an R-package HDtest and is available online at CRAN.

In this study, the risk hedge between the Morgan Stanley Taiwan stock index (MSTI) and its underlying futures is analyzed regarding hedging cost under various hedge states using the trend and volatility involved regime-switching models compared with OLS and the naive method. The correlation between MSTI spot and futures is very high and the cointegration test between them is very significantly. The hedging result is evaluated using out-of-sample data and the realized variances and covariances, and is really satisfying in the subprime period or when both spot and future stay in a down state. In this stock index context, the risk aversion is determined to be 1. The optimal hedge ratio on average is around 0.878. The White test favors the regime-switching models in prediction. The trend and volatility-switching model performs particularly well in wealth increase.

International audience; We propose an Aitken estimator for Gini regression. The suggested A-Gini estimator is proven to be a U-statistics. Monte Carlo simulations are provided to deal with heteroskedasticity and to make some comparisons between the generalized least squares and the Gini regression. A Gini-White test is proposed and shows that a better power is obtained compared with the usual White test when outlying observations contaminate the data.

A test for time-varying correlation is developed within the framework of a dynamic conditional score (DCS) model for both Gaussian and Student t-distributions. The test may be interpreted as a Lagrange multiplier test and modified to allow for the estimation of models for time-varying volatility in the individual series. Unlike standard moment-based tests, the score-based test statistic includes information on the level of correlation under the null hypothesis and local power arguments indicate the benefits of doing so. A simulation study shows that the performance of the score-based test is strong relative to existing tests across a range of data generating processes. An application to the Hong Kong and South Korean equity markets shows that the new test reveals changes in correlation that are not detected by the standard moment-based test.

Currently, the black/white preference test has been used to evaluate anxiety-like behaviors in zebrafish. However, several inconsistent results have been reported across literature. Since animal behavior can be influenced by several environmental factors, the main goal of the present study was to investigate the influence of different water column depths and light intensities on zebrafish behavioral responses in the black/white test. On a 4cm water column depth, animals spent more time in the black than in the white compartment. However, when animals were tested in an 8cm water column, no significant difference was found. Using an inclined acrylic floor inside the aquarium, animals spent more time in the deep compartment when this was black. However, there is no difference in time spent in each compartment when the deeper compartment was white. For light intensity test, animals showed preference for the white compartment only when both compartments were illuminated with 100lx. For the others illumination settings, there was no difference in the compartment preference. In conclusion, our results suggest that variations in water column depth and light intensity can modulate zebrafish preference in the black/white test. These variations may be implicated in the discrepancies observed in literature.

Recently, the development in combining point forecasts of electricity prices obtained with different length of calibration windows have provided an extremely efficient and simple tool for improving predictive accuracy. However, the proposed methods are strongly dependent on expert knowledge and may not be directly transferred from one to another model or market. Hence, we consider a novel extension and propose to use principal component analysis (PCA) to automate the procedure of averaging over a rich pool of predictions. We apply PCA to a panel of over 650 point forecasts obtained for different calibration windows length. The robustness of the approach is evaluated with three different forecasting tasks, i.e., forecasting day-ahead prices, forecasting intraday ID3 prices one day in advance, and finally very short term forecasting of ID3 prices (i.e., six hours before delivery). The empirical results are compared using the Mean Absolute Error measure and Giacomini and White test for conditional predictive ability (CPA). The results indicate that PCA averaging not only yields significantly more accurate forecasts than individual predictions but also outperforms other forecast averaging schemes.

The objective of this research is to compare Ramsey test, White test and Terasvirta test in the identification of nonlinearity. Ramsey test is a test based on the regression specification error test. While White test and Terasvirta test are based on neural network models. The difference between White test and Terasvirta test is in determining its weight, White test based on random sampling, while Terasvirta test based on Taylor expansion. Simulation studies are carried out with various scenarios in each test by generating linear models, linear models with outliers and nonlinear models. The results of the simulation study showed that Terasvirta test had better power than Ramsey test and White test in detecting nonlinearity. Terasvirta test is also more sensitive to the presence of outliers in linear models.

Rainfall is an important component of the hydrologic cycle and is used for planning in various fields. Based on the White test it is known that some weather variables correlate non-linearly to rainfall. Meanwhile, from correlation testing it is known that the observed weather data from weather stations in a region are mutually correlated. Therefore, statistical modeling using autocorrelation and cross correlation is less appropriate because the assumption of linear correlation is not fulfilled. In this paper, a new framework is proposed for non-linear feature extraction using detrended partial crosscorrelation analysis and predictor input selection using symmetrical uncertainty as a way to determine optimal nonlinear input features in rainfall forecasting. Forecasting was performed simultaneously for 3 weather station locations in addition to taking into account the dependencies of observation time. This is called a non-linear spatio-temporal recurrent neural network. The result of the forecasting method shows that the model performed better than univariate/multivariate time series forecasting and a recurrent neural network without input selection.

The purpose of this research is to provide the evidence on the relationship between Real Exchange rate (REXR) against US dollar and macroeconomic variables in Pakistan. This study has taken real exchange rate is dependent variable and some other macroeconomic variables are as independent variables. To examine this relationship the ordinary least square regression (OLS) technique is used. This technique was used by Zamir et al. (2017). The results shows that current balance (CB) is negatively significant at 10% level, the inflation and foreign direct investment (FDI) is also negatively significant at 5, 1% respectively. But the Gross domestic product per capita (GDP) is positively significant at 5% level. The Trade openness (OP) shows no important relation with real exchange rate (REXR). Further the ARCH LM test provides result there is no serial correlation and to check the heteroskedasticity whit test is used this test shows the result there is no heteroskedasticity. This study is helpful for international investor and also to increase export for a country.

The black–white test score gap remains a measurable phenomenon in the United States. Up to this point the literature has primarily focused on the black–white achievement gap without taking into account the underlying mobility patterns of individual students as they progress from one grade to the next. However, the degree to which policy makers and educators should be concerned about the black–white test score gap should be tied to how mobile the two groups of students are through the distribution of test scores from one grade to the next. In this paper I apply two nonparametric estimators of distributional mobility to data on test scores and track black–white differences in mobility across the entire distribution of achievement. When compared to whites, blacks tend to be less upwardly mobile and more downwardly mobile for both math and reading. This pattern is particularly prominent for reading in the very early years of schooling.

In this paper, we present two test statistics for assessing white noise assumption with semi-parametric additive partial linear models. The test statistics are shown to have asymptotic normal or chi-squared distributions under the null hypothesis that the model errors belong to white noise series. By applying R, Monte Carlo experiments are conducted to examine the finite sample performance of the test statistics. Simulation results indicate that the test statistics perform satisfactorily in both estimated sizes and powers.

In the paper, a residual-based test is developed for variance components in LMM. It is distribution-free, tractable, consistent and suitable for clustered designs and crossed designs. Numerical analysis is also conducted.

Although both economists and psychometricians typically treat them as interval scales, test scores are reported using ordinal scales. Using the Early Childhood Longitudinal Study (ECLS-K) and the Children of the National Longitudinal Survey of Youth (CNLSY), we examine how order-preserving scale transformations affect the evolution of the black-white reading test score gap from kindergarten entry through third grade. Plausible transformations reverse the growth of the gap in the CNLSY and greatly reduce it in the ECLS-K during the early school years. All growth from entry through first grade and a nontrivial proportion from first to third grade probably reflects scaling decisions. © 2013 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

The study identifies and evaluates the association among corporate solvency management strategies and the corporate performance valuation in Chemical industry of Pakistan. The study uses purposive sampling or judgmental sampling for selecting 30 sample companies from the sector; covering 10 years financial statements data ranging from year 2002 to 2011. Balanced panel data is taken for the purpose of study. Levin, Lin & Chu test is used to check the stationarity of data whereas White Test is used to check the heteroskedasticity of data. Panel Least square technique with fixed effects is used to generalize the relationship between studied variables. The study observed that the performance of the chemical sector in terms of market to book value is affected by internal firm and industry specific factors related to solvency management strategic decisions. Findings of the study provide with the overview of historic performance and the potential performance of the selected sector to help policy makers including finance, economics and industry experts for

It has been known that when there is a break in the variance (unconditional heteroskedasticity) of the error term in linear regression models, a routine application of the Lagrange multiplier (LM) test for autocorrelation can cause potentially significant size distortions. We propose a new test for autocorrelation that is robust in the presence of a break in variance. The proposed test is a modified LM test based on a generalized least squares regression. Monte Carlo simulations show that the new test performs well in finite samples and it is especially comparable to other existing heteroskedasticity-robust tests in terms of size, and much better in terms of power.