scholarly journals Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals

2013 ◽  
pp. 451-466 ◽  
Author(s):  
Kendrick Boyd ◽  
Kevin H. Eng ◽  
C. David Page
Keyword(s):  
Confidence Intervals ◽  
Point Estimates ◽  
Precision Recall Curve ◽  
Recall Curve

Hollywood Rules

2017 ◽  
pp. 1-9
Author(s):  
Karl Schmedders ◽  
Charlotte Snyder ◽  
Ute Schaedel
Keyword(s):  
Confidence Intervals ◽  
Statistical Methods ◽  
Regression Models ◽  
Functional Form ◽  
Hedge Fund ◽  
Fund Manager ◽  
Wall Street ◽  
Independent Variables ◽  
Point Estimates ◽  
Hedge Fund Manager

Wall Street hedge fund manager Kim Meyer is considering investing in an SFA (slate financing arrangement) in Hollywood. Dave Griffith, a Hollywood producer, is pitching for the investment and has conducted a broad analysis of recent movie data to determine the important drivers of a movie’s success. In order to convince Meyer to invest in an SFA, Griffith must anticipate possible questions to maximize his persuasiveness.Students will analyze the factors driving a movie’s revenue using various statistical methods, including calculating point estimates, computing confidence intervals, conducting hypothesis tests, and developing regression models (in which they must both choose the relevant set of independent variables as well as determine an appropriate functional form for the regression equation). The case also requires the interpretation of the quantitative findings in the context of the application.

scholarly journals Machine Learning Models for Diagnostic Classification of Hepatitis C Tests

2021 ◽  
Vol 10 (1) ◽  
pp. 70
Author(s):  
Oladosu Oyebisi Oladimeji ◽  
Abimbola Oladimeji ◽  
Oladimeji Olayanju
Keyword(s):  
Hepatitis C ◽  
Area Under Curve ◽  
Operating Characteristic ◽  
Matthews Correlation Coefficient ◽  
Receiver Operating Characteristic Area ◽  
Precision Recall Curve ◽  
Characteristic Area ◽  
F Measure ◽  
Machine Learning Models ◽  
Recall Curve

Introduction: Hepatitis C is a chronic infection caused by hepatitis c virus - a blood borne virus. Therefore, the infection occurs through exposure to small quantities of blood. It has been estimated by World Health Organization (WHO) to have affected 71 million people worldwide. This infection costs individual, groups and government a lot because no vaccine has been gotten yet for the treatment. This disease is likely to continue to affect more people because it’s long asymptotic phase which makes its early detection not feasible.Material and Methods: In this study, we have presented machine learning models to automatically classify the diagnosis test of hepatitis and also ranked the test features in order to know how they contribute to the classification which help in decision making process by the health care industry. The synthetic minority oversampling technique (SMOTE) was used to solve the problem of imbalance dataset.Results: The models were evaluated based on metrics such as Matthews correlation coefficient, F-measure, Precision-Recall curve and Receiver Operating Characteristic Area Under Curve.  We found that using SMOTE techniques helped raise performance of the predictive models. Also, random forest (RF) had the best performance based on Matthews correlation coefficient (0.99), F-measure (0.99), Precision-Recall curve (1.00) and Receiver Operating Characteristic Area Under Curve (0.99).Conclusion: This discovery has the potential to impact on clinical practice, when health workers aim at classifying diagnosis result of disease at its early stage.

scholarly journals Earthquake forecasting: statistics and information

2016 ◽  
Vol 58 (6) ◽  
Author(s):  
Vladimir Gertsik ◽  
Mark Kelbert ◽  
Anatoly Krichevets
Keyword(s):  
Confidence Intervals ◽  
Decision Rule ◽  
Random Fields ◽  
Axiomatic Approach ◽  
Earthquake Forecasting ◽  
Alarm System ◽  
Conditional Probabilities ◽  
Mathematical Basis ◽  
Strong Earthquakes ◽  
Point Estimates

<div class="abstract"><div class="abstract_item"><p>The paper presents a decision rule forming a mathematical basis of earthquake forecasting problem. We develop an axiomatic approach to earthquake forecasting in terms of multicomponent random fields on a lattice. This approach provides a method for constructing point estimates and confidence intervals for conditional probabilities of strong earthquakes under conditions on the levels of precursors. Also, it provides an approach for setting a multilevel alarm system and hypothesis testing for binary alarms. We use a method of comparison for different algorithms of earthquake forecasts in terms of the increase of Shannon information. ‘Forecasting’ (the calculation of the probabilities) and ‘prediction’ (the alarm declaring) of earthquakes are equivalent in this approach.</p></div></div>

Null Hypothesis Significance Testing: Ramifications, Ruminations and Recommendations

2005 ◽  
Vol 35 (1) ◽  
pp. 1-20 ◽  
Author(s):  
G. K. Huysamen
Keyword(s):  
Sample Size ◽  
Confidence Intervals ◽  
Effect Size ◽  
Null Hypothesis ◽  
Significance Testing ◽  
Population Parameter ◽  
Size Estimation ◽  
Null Hypothesis Significance Testing ◽  
Point Estimates ◽  
Size Estimates

Criticisms of traditional null hypothesis significance testing (NHST) became more pronounced during the 1960s and reached a climax during the past decade. Among others, NHST says nothing about the size of the population parameter of interest and its result is influenced by sample size. Estimation of confidence intervals around point estimates of the relevant parameters, model fitting and Bayesian statistics represent some major departures from conventional NHST. Testing non-nil null hypotheses, determining optimal sample size to uncover only substantively meaningful effect sizes and reporting effect-size estimates may be regarded as minor extensions of NHST. Although there seems to be growing support for the estimation of confidence intervals around point estimates of the relevant parameters, it is unlikely that NHST-based procedures will disappear in the near future. In the meantime, it is widely accepted that effect-size estimates should be reported as a mandatory adjunct to conventional NHST results.

Finite Sampling Properties of the Point Estimates and Confidence Intervals of the RMSEA

2003 ◽  
Vol 32 (2) ◽  
pp. 208-252 ◽  
Author(s):  
Patrick J. Curran ◽  
Kenneth A. Bollen ◽  
Feinian Chen ◽  
Pamela Paxton ◽  
James B. Kirby
Keyword(s):  
Confidence Intervals ◽  
Point Estimates ◽  
Finite Sampling

Neck Pain Participant’s Perception of Traction Forces During Chiropractic Manual Cervical Distraction

2012 ◽  
Author(s):  
M. Ram Gudavalli ◽  
Charles N. R. Henderson ◽  
Robert Vining ◽  
Lynne Carber ◽  
Avinash G. Patwardhan ◽  
...  
Keyword(s):  
Neck Pain ◽  
Confidence Intervals ◽  
Study Participant ◽  
Graphical Analysis ◽  
Motion Sensors ◽  
Traction Forces ◽  
Treatment Perceptions ◽  
Point Estimates ◽  
Force And Motion ◽  
Analysis Point

In this exploratory study, we measured applied traction forces during a chiropractic manual cervical distraction procedure for each of three “treatment” perceptions; (i) beginning to feel a stretch, (ii) stretch feels like it could be a treatment, and (iii) stretch definitely feels like a treatment. A single trained clinician performed manual cervical distraction procedures on 10 neck pain participants using a commercially available table that was embedded with force and motion sensors. Participants were prone on the table while manual distraction was applied with gradually increasing force. When the specified perception was experienced, the study participant depressed a hand switch. Data was summarized with descriptive statistics and plotted for graphical analysis. Point estimates and 95% confidence intervals were calculated for the distractive force associated with each of the 3 treatment perceptions. Mean traction forces with 95% confidence intervals, corresponding to each of the 3 perception levels were: i) beginning to feel a stretch 18.6 N (11.9–25.2 N), ii) stretch feels like it could be a treatment 25.5 N (18.3–32.6 N), and iii) stretch definitely feels like a treatment 36.2 N (26.2–46.1 N).

Sign in / Sign up

Export Citation Format

Share Document