Seismic Hazard Analysis Using the Adaptive Kernel Density Estimation Technique for Chennai City

2011 ◽  
Vol 169 (1-2) ◽  
pp. 55-69 ◽  
Author(s):  
C. K. Ramanna ◽  
G. R. Dodagoudar
Keyword(s):  
Seismic Hazard ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Hazard Analysis ◽  
Kernel Density ◽  
Seismic Hazard Analysis ◽  
Estimation Technique ◽  
Adaptive Kernel ◽  
Chennai City

On the Use of a Modified Intersection of Confidence Intervals (MICIH) Kernel Density Estimation Approach

2021 ◽  
Vol 8 (4) ◽  
pp. 309-332
Author(s):  
Efosa Michael Ogbeide ◽  
Joseph Erunmwosa Osemwenkhae
Keyword(s):  
Confidence Intervals ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Mean Squared Error ◽  
Kernel Density ◽  
Window Size ◽  
National Bureau ◽  
Squared Error ◽  
Data Density ◽  
Adaptive Kernel

Density estimation is an important aspect of statistics. Statistical inference often requires the knowledge of observed data density. A common method of density estimation is the kernel density estimation (KDE). It is a nonparametric estimation approach which requires a kernel function and a window size (smoothing parameter H). It aids density estimation and pattern recognition. So, this work focuses on the use of a modified intersection of confidence intervals (MICIH) approach in estimating density. The Nigerian crime rate data reported to the Police as reported by the National Bureau of Statistics was used to demonstrate this new approach. This approach in the multivariate kernel density estimation is based on the data. The main way to improve density estimation is to obtain a reduced mean squared error (MSE), the errors for this approach was evaluated. Some improvements were seen. The aim is to achieve adaptive kernel density estimation. This was achieved under a sufficiently smoothing technique. This adaptive approach was based on the bandwidths selection. The quality of the estimates obtained of the MICIH approach when applied, showed some improvements over the existing methods. The MICIH approach has reduced mean squared error and relative faster rate of convergence compared to some other approaches. The approach of MICIH has reduced points of discontinuities in the graphical densities the datasets. This will help to correct points of discontinuities and display adaptive density. Keywords: approach, bandwidth, estimate, error, kernel density

scholarly journals Benchmarks for detecting ‘breakthroughs’ in clinical trials: empirical assessment of the probability of large treatment effects using kernel density estimation

BMJ Open ◽  
2014 ◽  
Vol 4 (10) ◽  
pp. e005249 ◽  
Author(s):  
Branko Miladinovic ◽  
Ambuj Kumar ◽  
Rahul Mhaskar ◽  
Benjamin Djulbegovic
Keyword(s):  
Clinical Trials ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Treatment Effects ◽  
Kernel Density ◽  
Data Sources ◽  
Cooperative Groups ◽  
Publicly Funded ◽  
Adaptive Kernel ◽  
Improve Health

ObjectiveTo understand how often ‘breakthroughs,’ that is, treatments that significantly improve health outcomes, can be developed.DesignWe applied weighted adaptive kernel density estimation to construct the probability density function for observed treatment effects from five publicly funded cohorts and one privately funded group.Data Sources820 trials involving 1064 comparisons and enrolling 331 004 patients were conducted by five publicly funded cooperative groups. 40 cancer trials involving 50 comparisons and enrolling a total of 19 889 patients were conducted by GlaxoSmithKline.ResultsWe calculated that the probability of detecting treatment with large effects is 10% (5–25%), and that the probability of detecting treatment with very large treatment effects is 2% (0.3–10%). Researchers themselves judged that they discovered a new, breakthrough intervention in 16% of trials.ConclusionsWe propose these figures as the benchmarks against which future development of ‘breakthrough’ treatments should be measured.

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