scholarly journals Smooth statistical torsion angle potential derived from a large conformational database via adaptive kernel density estimation improves the quality of NMR protein structures

2012 ◽  
Vol 21 (12) ◽  
pp. 1824-1836 ◽  
Author(s):  
Guillermo A. Bermejo ◽  
G. Marius Clore ◽  
Charles D. Schwieters
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Torsion Angle ◽  
Protein Structures ◽  
Kernel Density ◽  
Adaptive Kernel

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.

scholarly journals Dengue Vector Density Incident and Its Implication to Urban Livability

2020 ◽  
Author(s):  
Nuriah Abd Majid ◽  
Muhammad Rizal Razman ◽  
Sharifah Zarina Syed Zakaria ◽  
Nurafiqah Muhamad Nazi
Keyword(s):  
Quality Of Life ◽  
Urban Area ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Urban Areas ◽  
Management Strategies ◽  
Kernel Density ◽  
Kuala Lumpur ◽  
Future Management

Abstract Background: Malaysia's population is set to reach 33.10 million by the end of 2020. About 75% of the population of Malaysia lived in urban areas and cities. The metropolitan area of Greater Kuala Lumpur had a population of more than seven million that year, making it the largest urban area in Malaysia. Kuala Lumpur as the city centre for Greater Kuala Lumpur has been ranked as Southeast Asia's second most liveable city after Singapore. The livable city imperative is relevant because Malaysia's urbanization process is moving towards harmonization with the principles of sustainable development. Livable city involves many interdependent factors contributing to the urban quality of life. With their complete physical and social infrastructures, the urban types are an essential basis for improving the quality of life of the urbanites. However, increasing population and rapid land-use changes led to the emergence of vector-borne diseases such as dengue in an urban area. Prolong dengue outbreaks will reduce livability in urban areas. Therefore, this study aims to look at the density of dengue distribution in Bandar Baru Bangi town in 2014, 2015, 2016 and 2017.Methods: The study uses data provided from the Ministry of Health Malaysia and shows the focus of dengue cases in residential and industrial areas of Bandar Baru Bangi town. Spatial analysis using Geographical Information System (GIS) was applied to identify the locality of dengue incidence within the study area. Spatial statistical analysis of dengue cases used Kernel Density Estimation to distinguish dengue hotspots from the distribution of the exact location of dengue cases reported in Bandar Baru Bangi town.Results: Kernel density estimation showed the dengue hotspots concentrated on the east of Bandar Baru Bangi town. The results found that the highest density was in 2015 was 605 to 706 points per square kilometres. This study also discovers that most of the hotspots constructed were located in the residential area of Bandar Baru Bangi.Conclusions: This study is essential to help local authorities eradicate dengue in urban areas for future management strategies; therefore, this study is vital to help local authorities eradicate dengue in urban areas for future management strategies.

Geospatial Crime Analysis to Determine Crime Density Using Kernel Density Estimation for the Indian Context

2020 ◽  
Vol 17 (1) ◽  
pp. 74-86
Author(s):  
Boppuru Rudra Prathap ◽  
K. Ramesha
Keyword(s):  
Law Enforcement ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Financial Burden ◽  
Estimation Method ◽  
Kernel Density ◽  
Law Enforcement Agencies ◽  
The Government ◽  
Geographical Locations

Crime is the most common social problem faced in a developing country. Crime affects the reputation of a nation and the quality of life of its citizens. Crime also affects the economy of the country, increasing the financial burden of the government due to the need for expenditure in the police force and judicial system. Various initiatives are taken by law enforcement to reduce the crime rate. One such initiative, real-time accurate crime predictions can help reduce the occurrence of crime. In this paper, a crime analytics platform is developed, which processes newsfeed data analysis for different types of crimes and identify crime hotspots using Kernel Density Estimation method. This system enables criminologists to understand the hidden relationships between crime and geographical locations. Interactive visualization features are available that enable law enforcement agencies to predict crime.

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