Comparison of different electrocardiography with vectorcardiography transformations

This paper deals with transformations from electrocardiographic (ECG) to vectorcardiographic (VCG) leads. VCG provides better sensitivity, for example for the detection of myocardial infarction, ischemia, and hypertrophy. However, in clinical practice, measurement of VCG is not usually used because it requires additional electrodes placed on the patient's body. Instead, mathematical transformations are used for deriving VCG from 12-leads ECG. In this work, Kors quasi-orthogonal transformation, inverse Dower transformation, Kors regression transformation, and linear regression-based transformations for deriving P wave (PLSV) and QRS complex (QLSV) are implemented and compared. These transformation methods were not yet compared before, so we have selected them for this paper. Transformation methods were compared for the data from the Physikalisch-Technische Bundesanstalt (PTB) database and their accuracy was evaluated using a mean squared error (MSE) and a correlation coefficient (R) between the derived and directly measured Frank's leads. Based on the statistical analysis, Kors regression transformation was significantly more accurate for the derivation of the X and Y leads than the others. For the Z lead, there were no statistically significant differences in the medians between Kors regression transformation and the PLSV and QLSV methods. This paper thoroughly compared multiple VCG transformation methods to conventional VCG Frank's orthogonal lead system, used in clinical practice.Web of Science1914art. no. 307

The Staffing of Presidential Assistants: Their Effects on Presidential Success in the House of Representatives

This paper examines the Congressional success of United States Presidents based on the reported Congressional Quarterly Presidential Box Scores. Their individual success is examined as an effect of the senior staff member a President chooses and whether they are chosen from the campaign, personal experience, or previous administrations. It is important for a President to consider the origins of these staffers as these Assistants to the President act as the body of the President’s administration. The econometric analysis presented reveals several interesting results. First, the predominance of a President to choose staff members from his campaign shows no significant impact on his Congressional relations and success. Second, staff members chosen from personal experience have a negatively correlated hindrance on success. Finally, those members chosen for their experience in previous administrations has a positive impact on Presidential success. This research is used to supplement the existing, qualitative research on the subject through regression analysis

The Staffing of Presidential Assistants: Their Effect on Presidential Success in the House of Representatives

This paper examines the Congressional success of United States Presidents based on the reported Congressional Quarterly Presidential Box Scores. Their individual success is examined as an effect of the senior staff member a President chooses and whether they are chosen from the campaign, personal experience, or previous administrations. It is important for a President to consider the origins of these staffers as these Assistants to the President act as the body of the President’s administration. The econometric analysis presented reveals several interesting results. First, the predominance of a President to choose staff members from his campaign shows no significant impact on his Congressional relations and success. Second, staff members chosen from personal experience have a negatively correlated hindrance on success. Finally, those members chosen for their experience in previous administrations has a positive impact on Presidential success. This research is used to supplement the existing, qualitative research on the subject through regression analysis

An invalidation test for predictive models

The standard means of establishing predictive ability in hydrological models is by finding how well predictions match independent validation data. This matching may not be particularly good in some situations such as seasonal flow forecasting and the question arises as to whether a given model has any predictive capacity. A model-independent significance test of the presence of predictive ability is proposed through random permutations of the predicted values. The null hypothesis of no model predictive ability is accepted if there is a sufficiently high probability that a random reordering of the predicted values will yield a better fit to the validation data. The test can achieve significance even with poor model predictions and its value is for invalidating bad models rather than verifying good models as suitable for application. Some preliminary applications suggest that test outcomes will often be similar at the 0.05 level for standard fit measures using absolute or squared residuals. In addition to hydrological application, the test may also find use as a base quality control measure for predictive models generally

Too Many Probabilities: Statistical Evidence of Tort Causation

Medical scientific testimony is often expressed in terms of two different probabilities: 1. The increased probability of harm if a person is exposed, for example, to a toxin. 2. The observed relationship is an artifact of the experimental method. This article demonstrates that neither probability, taken alone or together, measures whether the preponderance of the evidence test is met

On the performance of US fiscal forecasts : government vs. private information

This paper contributes to shed light on the quality and performance of US fiscal forecasts. The first part inspects the causes of official (CBO) fiscal forecasts revisions between 1984 and 2016 that are due to technical, economic or policy reasons. Both individual and cumulative means of forecast errors are relatively close to zero, particularly in the case of expenditures. CBO averages indicate net average downward revenue and expenditure revisions and net average upward deficit revisions. Focusing on the causes of the technical component, we uncover that its revisions are quite unpredictable which casts doubts on inferences about fiscal policy sustainability that rely on point estimates. Comparing official with private-sector (Consensus) forecasts, despite the informational advantages CBO might have, one cannot unequivocally say that one or the other is more accurate. Evidence also seems to suggest that CBO forecasts are consistently heavily biased towards optimism while this is less the case for Consensus forecasts. Not only is the extent of information rigidity is more prevalent in CBO forecasts, but evidence also seems to indicate that Consensus forecasts dominate CBO’s in terms of information content.info:eu-repo/semantics/publishedVersio

Inference for feature selection using the Lasso with high-dimensional data

Penalized regression models such as the Lasso have proved useful for variable selection in many fields - especially for situations with high-dimensional data where the numbers of predictors far exceeds the number of observations. These methods identify and rank variables of importance but do not generally provide any inference of the selected variables. Thus, the variables selected might be the "most important" but need not be significant. We propose a significance test for the selection found by the Lasso. We introduce a procedure that computes inference and p-values for features chosen by the Lasso. This method rephrases the null hypothesis and uses a randomization approach which ensures that the error rate is controlled even for small samples. We demonstrate the ability of the algorithm to compute $p$-values of the expected magnitude with simulated data using a multitude of scenarios that involve various effects strengths and correlation between predictors. The algorithm is also applied to a prostate cancer dataset that has been analyzed in recent papers on the subject. The proposed method is found to provide a powerful way to make inference for feature selection even for small samples and when the number of predictors are several orders of magnitude larger than the number of observations. The algorithm is implemented in the MESS package in R and is freely available