scholarly journals Lognormal Distribution in Health Insurance: Interval Estimation Methods for the Average Cost and Correction of Truncated Values

2019 ◽  
Vol 9 (2) ◽  
pp. 101-110
Author(s):  
Marli Amorim ◽  
◽  
Joana Fernandes ◽  
Teresa Alpuim
Keyword(s):  
Health Insurance ◽  
Lognormal Distribution ◽  
Average Cost ◽  
Interval Estimation ◽  
Estimation Methods

scholarly journals Assessing the Lognormal Distribution Assumption For the Crude Odds Ratio: Implications For Point and Interval Estimation

2020 ◽  
Author(s):  
David Douglas Newstein
Keyword(s):  
Confidence Intervals ◽  
Standard Method ◽  
Lognormal Distribution ◽  
Odds Ratio ◽  
Estimation Method ◽  
Interval Estimation ◽  
Parametric Bootstrap ◽  
Sampling Distribution ◽  
Estimation Methods ◽  
Crude Odds Ratio

Abstract Background: The assumption that the sampling distribution of the crude Odds Ratio (ORcrude) is a lognormal distribution with parameters mu and sigma leads to the incorrect conclusion that the expectation of the log of ORcrude is equal to the parameter mu. Here, the standard method of point and interval estimation (I) is compared with a modified method utilizing ORstar where ln(ORstar) = ln(ORcrude )– sigma **2/2. Methods: Confidence intervals are obtained utilizing ln(ORstar) by both parametric bootstrap simulations with a percentile derived confidence interval (II), and a simple calculation done by replacing ln(ORcrude) with ln(ORstar) in the standard formula (III) as well as a method proposed by Barendregt (IV), who also noted the bias present in estimating ORtrue by ORcrude. Simulations are conducted for a “protective” exposure (ORtrue < 1) as well as for a “harmful” exposure (ORtrue >1). Results: In simulations the estimation methods (II and III) exhibited the highest level of statistical conclusion validity for their confidence intervals as indicated by one minus the coverage probability being close to alpha. Also, as demonstrated by the MC simulations, these two methods exhibited the least biased point estimates and the narrowest confidence intervals of the four estimation approaches. Conclusions: Monte Carlo simulations prove useful in validating the inferential procedures used in data analysis. In the case of the odds ratio, the standard method of point and interval estimation is based on the assumption that the crude odds ratio has a sampling distribution that is lognormal. Utilizing this assumption, as well as the formula for the expectation of this distribution function, an alternative estimation method was obtained for ORtrue (but different from a method from the earlier report (Barendregt)), that yielded point and interval estimates that MC simulations indicate are the most statistically valid.

scholarly journals Estimating Time: Comparing the Accuracy of Estimation Methods for Interval Timing

2019 ◽  
Author(s):  
Atser Damsma ◽  
Nadine Schlichting ◽  
Hedderik van Rijn ◽  
Warrick Roseboom
Keyword(s):  
Estimation Method ◽  
Interval Estimation ◽  
Reaction Times ◽  
Interval Timing ◽  
Alternative Methods ◽  
Estimation Methods ◽  
Complex Task ◽  
Target Onset ◽  
Spatial Estimation ◽  
Verbal Estimation

In interval timing experiments, motor reproduction is the predominant method used when participants are asked to estimate an interval. However, it is unknown how its accuracy, precision and efficiency compare to alternative methods, such as indicating the duration by spatial estimation on a timeline. In two experiments, we compared different interval estimation methods. In the first experiment, participants were asked to reproduce an interval by means of motor reproduction, timeline estimation, or verbal estimation. We found that, on average, verbal estimates were more accurate and precise than line estimates and motor reproductions. However, we found a bias towards familiar whole second units when giving verbal estimates. Motor reproductions were more precise, but not more accurate than timeline estimates. In the second experiment, we used a more complex task: Participants were presented a stream of digits and one target letters and were subsequently asked to reproduce both the interval to target onset and the duration of the total stream by means of motor reproduction and timeline estimation. We found that motor reproductions were more accurate, but not more precise than timeline estimates. In both experiments, timeline estimates had the lowest reaction times. Overall, our results suggest that the transformation of time into space has only a relatively minor cost. In addition, they show that each estimation method comes with its own advantages, and that the choice of estimation method depends on choices in the experimental design: for example, when using durations with integer durations verbal estimates are superior, yet when testing long durations, motor reproductions are time intensive making timeline estimates a more sensible choice.

scholarly journals Estimating Time: Comparing the Accuracy of Estimation Methods for Interval Timing

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Atser Damsma ◽  
Nadine Schlichting ◽  
Hedderik van Rijn ◽  
Warrick Roseboom
Keyword(s):  
Estimation Method ◽  
Interval Estimation ◽  
Reaction Times ◽  
Interval Timing ◽  
Alternative Methods ◽  
Estimation Methods ◽  
Complex Task ◽  
Target Onset ◽  
Spatial Estimation ◽  
Verbal Estimation

In interval timing experiments, motor reproduction is the predominant method used when participants are asked to estimate an interval. However, it is unknown how its accuracy, precision and efficiency compare to alternative methods, such as indicating the duration by spatial estimation on a timeline. In two experiments, we compared different interval estimation methods. In the first experiment, participants were asked to reproduce an interval by means of motor reproduction, timeline estimation, or verbal estimation. We found that, on average, verbal estimates were more accurate and precise than line estimates and motor reproductions. However, we found a bias towards familiar whole second units when giving verbal estimates. Motor reproductions were more precise, but not more accurate than timeline estimates. In the second experiment, we used a more complex task: Participants were presented a stream of digits and one target letter and were subsequently asked to reproduce both the interval to target onset and the duration of the total stream by means of motor reproduction and timeline estimation. We found that motor reproductions were more accurate, but not more precise than timeline estimates. In both experiments, timeline estimates had the lowest reaction times. Overall, our results suggest that the transformation of time into space has only a relatively minor cost. In addition, they show that each estimation method comes with its own advantages, and that the choice of estimation method depends on choices in the experimental design: for example, when using durations with integer durations verbal estimates are superior, yet when testing long durations, motor reproductions are time intensive making timeline estimates a more sensible choice.

scholarly journals Interval Estimation Methods for Discrete-Time Linear Time-Invariant Systems

2019 ◽  
Vol 64 (11) ◽  
pp. 4717-4724 ◽  
Author(s):  
Wentao Tang ◽  
Zhenhua Wang ◽  
Ye Wang ◽  
Tarek Raissi ◽  
Yi Shen
Keyword(s):  
Discrete Time ◽  
Linear Time ◽  
Interval Estimation ◽  
Estimation Methods ◽  
Linear Time Invariant ◽  
Linear Time Invariant Systems ◽  
Time Invariant ◽  
Time Linear

scholarly journals Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review

2021 ◽  
Vol 13 (4) ◽  
pp. 1633
Author(s):  
Vahid Nourani ◽  
Nardin Jabbarian Paknezhad ◽  
Hitoshi Tanaka
Keyword(s):  
Sustainable Management ◽  
Review Paper ◽  
Prediction Interval ◽  
Interval Estimation ◽  
Estimation Methods ◽  
Future Studies ◽  
Pros And Cons ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Meta Analyses

Despite the wide applications of artificial neural networks (ANNs) in modeling hydro-climatic processes, quantification of the ANNs’ performance is a significant matter. Sustainable management of water resources requires information about the amount of uncertainty involved in the modeling results, which is a guide for proper decision making. Therefore, in recent years, uncertainty analysis of ANN modeling has attracted noticeable attention. Prediction intervals (PIs) are one of the prevalent tools for uncertainty quantification. This review paper has focused on the different techniques of PI development in the field of hydrology and climatology modeling. The implementation of each method was discussed, and their pros and cons were investigated. In addition, some suggestions are provided for future studies. This review paper was prepared via PRISMA (preferred reporting items for systematic reviews and meta-analyses) methodology.

Sign in / Sign up

Export Citation Format

Share Document