scholarly journals A Re-Evaluation of the Swiss Hail Suppression Experiment Using Permutation Techniques Shows Enhancement of Hail Energies When Seeding

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1623
Author(s):  
Armin Auf der Maur ◽  
Urs Germann
Keyword(s):  
Confidence Intervals ◽  
Silver Iodide ◽  
New Method ◽  
Raw Data ◽  
Hail Suppression ◽  
Bootstrap Techniques

Grossversuch IV is a large and well documented experiment on hail suppression by silver iodide seeding. The original 1986 evaluation remained vague, although indicating a tendency to increase hail when seeding. The strategy to deal with distributions of hail energy far from normal was not optimal. The present re-evaluation sticks to the question asked and avoids both misleading transformations and unsatisfactory meteorological predictors. The raw data show an increase by about a factor of 3 for the hail energy when seeding. This is the opposite of what seeding is supposed to do. The probability to obtain such a result by chance is below 1%, calculated by permutation and bootstrap techniques applied on the raw data. Confidence intervals were approximated by bootstrapping as well as by a new method called “correlation imposed permutation” (CIP).

scholarly journals A Re-Evaluation of the Swiss Hail Suppression Experiment using Permutation Techniques shows Enhancement of Hail Energies when Seeding

2021 ◽  
Author(s):  
Armin Auf der Maur ◽  
Urs Germann
Keyword(s):  
Confidence Intervals ◽  
Silver Iodide ◽  
New Method ◽  
Raw Data ◽  
Hail Suppression ◽  
Bootstrap Techniques

Grossversuch IV is a large and well documented experiment on hail suppression by silver iodide seeding. The original 1986 evaluation remained vague, although indicating a tendency to increase hail when seeding. The strategy to deal with distributions of hail energy far from normal was not optimal. The present re-evaluation sticks to the question asked and avoids both misleading transformations and unsatisfactory meteorological predictors. The raw data show an increase by about a factor of 3 for the hail energy when seeding. This is the opposite of what seeding is supposed to do. The probability to obtain such a result by chance is below 1%, calculated by permutation and bootstrap techniques applied on the raw data. Confidence intervals were approximated by bootstrapping as well as by a new method called "correlation imposed permutation" (CIP).

A New Method for Constructing Confidence Intervals for the IndexCpm

2004 ◽  
Vol 20 (7) ◽  
pp. 651-665 ◽  
Author(s):  
Michael Perakis ◽  
Evdokia Xekalaki
Keyword(s):  
Confidence Intervals ◽  
New Method

scholarly journals SLACK AND NET TECHNICAL EFFICIENCY MEASUREMENT: A BOOTSTRAP APPROACH

2005 ◽  
Vol 04 (03) ◽  
pp. 395-410 ◽  
Author(s):  
J. RICHMOND
Keyword(s):  
Confidence Intervals ◽  
Technical Efficiency ◽  
Small Component ◽  
Empirical Application ◽  
Use Of Resources ◽  
Bootstrap Techniques ◽  
Bootstrap Approach ◽  
Efficiency Estimation ◽  
Downward Bias ◽  
Cautious Interpretation

Statistical properties of DEA methods for efficiency estimation are poorly understood and currently the best way forward must be to use bootstrap techniques. The article seeks to extend bootstrap methods to allow investigation of the properties of estimates of inefficiencies due to the slack in the use of resources as well as technical efficiency. In an empirical application, it is found that inefficiency due to slack is a small component of the overall inefficiency and that the DEA technical efficiency estimates have a small downward bias, with confidence intervals that are wide enough to suggest cautious interpretation.

scholarly journals Beyond Psychology: Prevalence of p-value and confidence interval misinterpretation across different fields

2019 ◽  
Author(s):  
Xiaokang Lyu ◽  
Yuepei Xu ◽  
Xiaofan Zhao ◽  
Xi-Nian Zuo ◽  
Hu Chuan-Peng
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Biomedical Research ◽  
Scientific Community ◽  
Scientific Literature ◽  
P Value ◽  
Raw Data ◽  
Research Fields ◽  
Research Gap ◽  
Two Indices

P-value and confidence intervals (CIs) are the most widely used statistical indices in scientific literature. Several surveys revealed that these two indices are generally misunderstood. However, existing surveys on this subject fall under psychology and biomedical research, and data from other disciplines are rare. Moreover, the confidence of researchers when constructing judgments remains unclear. To fill this research gap, we survey 1,479 researchers and students from different fields in China. Results reveal that for significant (p < .05, CI doesn’t include 0) and non-significant (p > .05, CI includes 0) conditions, most respondents, regardless of academic degrees, research fields, and stages of career, could not interpret p-value and CI accurately. Moreover, the majority of them are confident about their (inaccurate) judgments (see osf.io/mcu9q/ for raw data, materials, and supplementary analyses). Therefore, misinterpretations of p-value and CIs prevail in the whole scientific community, thus the need for statistical training in science.

scholarly journals Efficient Bootstrap Estimates for Tail Statistics

2016 ◽  
Author(s):  
Øyvind Breivik ◽  
Ole Johan Aarnes
Keyword(s):  
Confidence Intervals ◽  
Temporal Resolution ◽  
Small Subset ◽  
High Temporal Resolution ◽  
Data Set ◽  
Empirical Distributions ◽  
Bootstrap Techniques ◽  
Entire Sequence ◽  
Bootstrap Estimates ◽  
Return Value

Abstract. Bootstrap resamples can be used to investigate the tail of empirical distributions as well as return value estimates based on the extremal behaviour of the distribution. Specifically, the confidence intervals on return value estimates or bounds on in-sample tail statistics can be estimated using bootstrap techniques. However, bootstrapping from the entire data set is expensive. It is shown here that it suffices to bootstrap from a small subset consisting of the highest entries in the sequence to make estimates that are essentially identical to bootstraps from the entire sequence. Similarly, bootstrap estimates of confidence intervals of threshold return estimates are found to be well approximated by using a subset consisting of the highest entries. This has practical consequences in fields such as meteorology, oceanography and hydrology where return estimates are routinely made from very large gridded model integrations spanning decades at high temporal resolution. In such cases the computational savings are substantial.

scholarly journals The BFP (Benford-Fibonacci-Perez) Method Validates the Consistency of COVID-19 Epidemiological Data in France and Italy

2021 ◽  
Author(s):  
Jean-Claude Perez
Keyword(s):  
Intensive Care ◽  
Epidemiological Data ◽  
Trial Data ◽  
New Drugs ◽  
New Method ◽  
Basic Data ◽  
Raw Data

The Benford method can be used to detect manipulation of epidemiological or trial data during the validation of new drugs. We extend here the Benford method after having detected particular properties for the Fibonacci values 1, 2, 3, 5 and 8 of the first decimal of 10 runs of official epidemiological data published in France and Italy (positive cases, intensive care, and deaths) for the periods of March 1 to May 30, 2020 and 2021, each with 91 raw data. This new method – called “BFP” for Benford-Fibonacci-Perez - is positive in all 10 cases (i.e. 910 values) with an average of favorable cases close to 80%, which, in our opinion, would validate the reliability of these basic data.

scholarly journals A Classroom Approach to Illustrate Transformation and Bootstrap Confidence Interval Techniques Using the Poisson Distribution

2017 ◽  
Vol 6 (2) ◽  
pp. 42 ◽  
Author(s):  
Per Gösta Andersson
Keyword(s):  
Problem Solving ◽  
Confidence Interval ◽  
Confidence Intervals ◽  
Poisson Distribution ◽  
Learning Process ◽  
Bootstrap Confidence Interval ◽  
New Techniques ◽  
Teacher Student ◽  
Bootstrap Techniques ◽  
Interval Estimator

The Poisson distribution is here used to illustrate transformation and bootstrap techniques in order to construct a confidence interval for a mean. A comparison is made between the derived intervals and the Wald  and score confidence intervals. The discussion takes place in a classroom, where the teacher and the students have previously discussed and evaluated the Wald and score confidence intervals. While step by step  interactively getting acquainted  with new techniques,  the students will learn about the effects of e.g. bias and asymmetry and ways of dealing with such phenomena. The primary purpose of this teacher-student communication is therefore not to find the  best possible interval estimator for this particular case, but rather to provide a study displaying a teacher and her/his students interacting with each other in an efficient and rewarding way. The teacher has a strategy of encouraging the students to take initiatives. This is accomplished by providing the necessary background of the problem and some underlying theory after which the students are confronted with questions and problem solving. From this the learning process starts. The teacher has to be flexible according to how the students react.  The students are supposed to have studied mathematical statistics for at least two semesters. 

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