Statistical Modelling Techniques

Biometrics ◽  
1982 ◽  
Vol 38 (3) ◽  
pp. 871
Author(s):  
Paul Davies ◽  
S. S. Shapiro ◽  
A. J. Gross
Keyword(s):  
Statistical Modelling ◽  
Modelling Techniques

Use of Statistical Modelling Techniques for Simulation

1993 ◽  
Author(s):  
Ebrahim Mazharsolook ◽  
David C. Robinson ◽  
Jonathan D. Casey
Keyword(s):  
Quality Control ◽  
Statistical Modelling ◽  
Test Results ◽  
Final Test ◽  
Product Model ◽  
Design Changes ◽  
Discrete Manufacturing ◽  
Similar Product ◽  
Modelling Techniques

Abstract Statistical methods are explored for the use in modelling of discrete manufacturing. The developed methodologies based on Design of Experiments (DOE) and stepwise regression to obtain the product model are described. This model is then embedded within a software system which is used for simulation of design changes, process changes and disturbances. The software is used to predict final test results in respect of up-stream parameter changes. A case study is presented o show the implementation of this method of modelling in Quality Control of manufacture. This case study has successfully been implemented. The system is currently assisting the company in design of similar product. Feasibility of applying Artificial Intelligen (AI) techniques to Model-Based Quality Control (MBQC) is investigated. An outline of the future development of Hybrid MBQC is then presented.

Reserving for catastrophe reinsurance

1994 ◽  
Vol 121 (1) ◽  
pp. 135-160 ◽  
Author(s):  
D. H. Craighead
Keyword(s):  
Normal Development ◽  
Statistical Modelling ◽  
Development Pattern ◽  
Catastrophe Losses ◽  
Modelling Techniques ◽  
Oil Rig ◽  
Catastrophe Reinsurance ◽  
A Company

AbstractThe paper sets out the method required to be followed when estimating reserves for a Company or a Lloyd's Syndicate which has accepted reinsurance treaties that have given rise to catastrophe losses, sufficiently large to upset the normal development pattern and to affect the gross account quite differently from the net account. The losses may be caused by single factors such as aircraft crashes or oil rig disasters, or by the aggregation of claims resulting from a windstorm or an earthquake. The paper discusses two possible approaches to estimation of the gross losses; via exposure totals or via statistical modelling techniques.

Population-Level Impact of Avahan in Karnataka State, South India Using Multilevel Statistical Modelling Techniques

2013 ◽  
Vol 62 (2) ◽  
pp. 239-245 ◽  
Author(s):  
Pradeep Banandur ◽  
Uma Mahajan ◽  
Rajaram S. Potty ◽  
Shajy Isac ◽  
Thierry Duchesne ◽  
...  
Keyword(s):  
South India ◽  
Population Level ◽  
Statistical Modelling ◽  
Karnataka State ◽  
Modelling Techniques

Is there value in reviewing distribution models? A case study using critical weight range mammals in southeastern Australia

2007 ◽  
Vol 13 (4) ◽  
pp. 227
Author(s):  
Trent D. Penman ◽  
Christopher P. Slade
Keyword(s):  
Review Process ◽  
Statistical Modelling ◽  
Distribution Models ◽  
Critical Weight ◽  
Weight Range ◽  
Recovery Planning ◽  
Southeastern Australia ◽  
South Wales ◽  
Modelling Techniques ◽  
Regional Forest

Models predicting species distributions have become a common tool for wildlife management. These models were used extensively in the development of regional forest agreements (RFAs) throughout Australia. Each RFA is reviewed after it has been active for five years and one component may be to review the distribution models. Over this time there has been an increase in the number of records for many species and improvements in statistical modelling techniques. Here we prepare updated distributional models for three critical weight range mammals in the Eden Management Region in southeastern New South Wales. These models are then used to examine the value of updating models for selected species during the RFA review process. All revised models predicted greater areas of habitat as suitable, largely due to the greater number of known localities. The relative value of many sites changed, thus highlighting areas which require further or more intensive survey work. This study suggests that there is value in preparing models for some species during the RFA review process. For many species updating models can also be valuable in the development of specific research objectives or species recovery planning.

scholarly journals Predicting risk of stillbirth and preterm pregnancies with machine learning

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Aki Koivu ◽  
Mikko Sairanen
Keyword(s):  
Machine Learning ◽  
Preterm Birth ◽  
Learning Strategies ◽  
External Validation ◽  
Statistical Modelling ◽  
Gradient Boosting ◽  
Data Set ◽  
Selection Parameter ◽  
Modelling Techniques ◽  
Solid Foundation

AbstractModelling the risk of abnormal pregnancy-related outcomes such as stillbirth and preterm birth have been proposed in the past. Commonly they utilize maternal demographic and medical history information as predictors, and they are based on conventional statistical modelling techniques. In this study, we utilize state-of-the-art machine learning methods in the task of predicting early stillbirth, late stillbirth and preterm birth pregnancies. The aim of this experimentation is to discover novel risk models that could be utilized in a clinical setting. A CDC data set of almost sixteen million observations was used conduct feature selection, parameter optimization and verification of proposed models. An additional NYC data set was used for external validation. Algorithms such as logistic regression, artificial neural network and gradient boosting decision tree were used to construct individual classifiers. Ensemble learning strategies of these classifiers were also experimented with. The best performing machine learning models achieved 0.76 AUC for early stillbirth, 0.63 for late stillbirth and 0.64 for preterm birth while using a external NYC test data. The repeatable performance of our models demonstrates robustness that is required in this context. Our proposed novel models provide a solid foundation for risk prediction and could be further improved with the addition of biochemical and/or biophysical markers.

scholarly journals Composite Fasciola hepatica faecal egg sedimentation test for cattle

2019 ◽  
Vol 184 (19) ◽  
pp. 589-589 ◽  
Author(s):  
John Graham-Brown ◽  
Diana J L Williams ◽  
Philip Skuce ◽  
Ruth N Zadoks ◽  
Stuart Dawes ◽  
...  
Keyword(s):  
Fasciola Hepatica ◽  
Sampling Strategy ◽  
Statistical Modelling ◽  
Composite Sample ◽  
Sedimentation Method ◽  
Faecal Samples ◽  
Modelling Techniques ◽  
Sample Testing ◽  
Sample Method ◽  
And Control

Options for diagnosing Fasciola hepatica infection in groups of cattle are limited. Increasing the opportunities for herd-level diagnosis is important for disease monitoring, making informed treatment decisions and for flukicide efficacy testing. The sensitivity of a simple sedimentation method based on composite faecal samples for the detection of fluke eggs in cattle was assessed through a combination of experimental and statistical modelling techniques. Initially, a composite sample method previously developed for sheep was used to investigate the sensitivity of composite sample testing compared with individual counts on the same samples in cattle. Following this, an optimised, validated, qualitative (presence-absence) composite sample field test was developed for cattle. Results showed that fluke egg counts obtained from a composite sample are representative of those expected from individual counts. The optimal sampling strategy was determined to be 10 individual 10 g samples (100 g composite sample) from which a 10 g subsample is taken for sedimentation. This method yielded a diagnostic sensitivity of 0.69 (95 per cent CI 0.5 to 0.85). These results demonstrate the validity and usefulness of a composite faecal egg sedimentation method for use in the diagnosis and control of F. hepatica in groups of cattle, with the caveat that a negative test should be followed up with a second test due to limitations relating to test sensitivity.

scholarly journals Visualisation and statistical modelling techniques for the management of inventory stock levels

2016 ◽  
Vol 57 ◽  
pp. 130
Author(s):  
Winston LeMay Sweatman ◽  
James McGree ◽  
Corrie Jacobien Carstens ◽  
Kylie J. Foster ◽  
Shen Liu ◽  
...  
Keyword(s):  
Statistical Modelling ◽  
Modelling Techniques
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