Individual Claim Loss Reserving Conditioned by Case Estimates

2008 ◽  
Vol 3 (1-2) ◽  
pp. 215-256 ◽  
Author(s):  
Greg Taylor ◽  
Gráinne McGuire ◽  
James Sullivan
Keyword(s):  
Prediction Error ◽  
Current Paper ◽  
Data Set ◽  
Loss Reserve ◽  
Extensive Data ◽  
Positive Individual ◽  
Loss Reserving ◽  
Conditioning Information

ABSTRACTThis paper examines various forms of individual claim model for the purpose of loss reserving, with emphasis on the prediction error associated with the reserve. Each form of model is calibrated against a single extensive data set, and then used to generate a forecast of loss reserve and an estimate of its prediction error.The basis of this is a model of the “paids” type, in which the sizes of strictly positive individual finalised claims are expressed in terms of a small number of covariates, most of which are in some way functions of time. Such models can be found in the literature.The purpose of the current paper is to extend these to individual claim “incurreds” models. These are constructed by the inclusion of case estimates in the model's conditioning information. This form of model is found to involve rather more complexity in its structure.For the particular data set considered here, this did not yield any direct improvement in prediction error. However, a blending of the paids and incurreds models did so.

How to Assess Prognostic Models for Survival Data: A Case Study in Oncology

2003 ◽  
Vol 42 (05) ◽  
pp. 564-571 ◽  
Author(s):  
M. Schumacher ◽  
E. Graf ◽  
T. Gerds
Keyword(s):  
Test Data ◽  
Survival Data ◽  
Prediction Error ◽  
Classification Scheme ◽  
Neural Nets ◽  
Brier Score ◽  
Data Set ◽  
Independent Test ◽  
Artificial Neural

Summary Objectives: A lack of generally applicable tools for the assessment of predictions for survival data has to be recognized. Prediction error curves based on the Brier score that have been suggested as a sensible approach are illustrated by means of a case study. Methods: The concept of predictions made in terms of conditional survival probabilities given the patient’s covariates is introduced. Such predictions are derived from various statistical models for survival data including artificial neural networks. The idea of how the prediction error of a prognostic classification scheme can be followed over time is illustrated with the data of two studies on the prognosis of node positive breast cancer patients, one of them serving as an independent test data set. Results and Conclusions: The Brier score as a function of time is shown to be a valuable tool for assessing the predictive performance of prognostic classification schemes for survival data incorporating censored observations. Comparison with the prediction based on the pooled Kaplan Meier estimator yields a benchmark value for any classification scheme incorporating patient’s covariate measurements. The problem of an overoptimistic assessment of prediction error caused by data-driven modelling as it is, for example, done with artificial neural nets can be circumvented by an assessment in an independent test data set.

scholarly journals A Preliminary Phylogeny of Rhyacophilidae with Reference to Fansipangana and the Monophyly of Rhyacophila

Zoosymposia ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. 189-192
Author(s):  
JULIANNE E. MCLAUGHLIN ◽  
PAUL B. FRANDSEN ◽  
WOLFRAM MEY ◽  
STEFFEN U. PAULS
Keyword(s):  
Mitochondrial Dna ◽  
Cytochrome Oxidase ◽  
Ribosomal Rna ◽  
Additional Data ◽  
Sister Taxon ◽  
Bootstrap Support ◽  
Cytochrome Oxidase Subunit ◽  
Cytochrome Oxidase Subunit I ◽  
Data Set ◽  
Extensive Data

The phylogeny of Rhyacophilidae was explored with 28S ribosomal RNA (rRNA) and Cytochrome Oxidase Subunit I (COI) mitochondrial DNA (mtDNA). Eighty one rhyacophilids were included in the analysis. We found that although Rhyacophilidae was recovered as monophyletic, intrafamilial relationships are not well-resolved using this dataset. Bootstrap support was poor for intrageneric relationships and additional data will be required to present a more robust hypothesis. The recovered phylogeny places Fansipangana as the sister taxon of the rest of Rhyacophilidae. We found that Himalopsyche was nested inside the genus Rhyacophila with the verrula group sister to Himalopsyche and remaining Rhyacophila. These results and possible relationships should be tested with a more extensive data set.

scholarly journals An extensive data set related to micro-drilling of Al-SiC-B4C hybrid composite through µECM using GRA coupled PCA.

Data in Brief ◽  
2020 ◽  
Vol 33 ◽  
pp. 106491
Author(s):  
Venkatesh Chenrayan ◽  
Mengistu Gelaw ◽  
Chandru Manivannan ◽  
Venkatesan Rajamanickam ◽  
Ellappan Venugopal
Keyword(s):  
Hybrid Composite ◽  
Data Set ◽  
Extensive Data ◽  
Micro Drilling

scholarly journals Extensive data set analysis & prediction using R

2019 ◽  
Vol 1228 ◽  
pp. 012048
Author(s):  
Padmaja Grandhe ◽  
Vishnu Priya Damarla ◽  
Shaziya Mohammad
Keyword(s):  
Data Set ◽  
Extensive Data ◽  
Analysis Prediction

scholarly journals Springtime zooplankton size structure over the continental shelf of the Bay of Biscay

Ocean Science ◽  
2014 ◽  
Vol 10 (5) ◽  
pp. 821-835 ◽  
Author(s):  
P. Vandromme ◽  
E. Nogueira ◽  
M. Huret ◽  
Á. Lopez-Urrutia ◽  
G. González-Nuevo González ◽  
...  
Keyword(s):  
Continental Shelf ◽  
Size Distribution ◽  
Size Structure ◽  
Trophic Levels ◽  
Equivalent Diameter ◽  
Bay Of Biscay ◽  
Data Set ◽  
Explanatory Variables ◽  
Extensive Data ◽  
Zooplankton Size

Abstract. Linking lower and higher trophic levels requires special focus on the essential role played by mid-trophic levels, i.e., the zooplankton. One of the most relevant pieces of information regarding zooplankton in terms of flux of energy lies in its size structure. In this study, an extensive data set of size measurements is presented, covering parts of the western European continental shelf and slope, from the Galician coast to the Ushant front, during the springs from 2005 to 2012. Zooplankton size spectra were estimated using measurements carried out in situ with the Laser Optical Plankton Counter (LOPC) and with an image analysis of WP2 net samples (200 μm mesh size) performed following the ZooScan methodology. The LOPC counts and sizes particles within 100–2000 μm of spherical equivalent diameter (ESD), whereas the WP2/ZooScan allows for counting, sizing and identification of zooplankton from ~ 400 μm ESD. The difference between the LOPC (all particles) and the WP2/ZooScan (zooplankton only) was assumed to provide the size distribution of non-living particles, whose descriptors were related to a set of explanatory variables (including physical, biological and geographic descriptors). A statistical correction based on these explanatory variables was further applied to the LOPC size distribution in order to remove the non-living particles part, and therefore estimate the size distribution of zooplankton. This extensive data set provides relevant information about the zooplankton size distribution variability, productivity and trophic transfer efficiency in the pelagic ecosystem of the Bay of Biscay at a regional and interannual scale.

Interpolation of bathymetry data from the Sea of Galilee: A noise attenuation problem

Geophysics ◽  
2004 ◽  
Vol 69 (2) ◽  
pp. 608-616 ◽  
Author(s):  
Antoine Guitton ◽  
Jon Claerbout
Keyword(s):  
Gaussian Noise ◽  
Prediction Error ◽  
Weighting Function ◽  
Depth Map ◽  
Water Levels ◽  
Iteratively Reweighted Least Squares ◽  
Data Set ◽  
Depth Images ◽  
Ship Tracks ◽  
Non Gaussian

We process a bathymetry survey from the Sea of Galilee. This data set is contaminated with non‐Gaussian noise in the form of spikes inside the lake and at the track ends. There is drift on the depth measurements leading to vessel tracks in the preliminary depth images. The drift comes from different seasonal and human conditions during data acquisition, e.g., wind and water levels. We derive an inversion scheme that produces a much‐reduced noise map of the Sea of Galilee. This inversion scheme includes preconditioning and iteratively reweighted least squares with the proper weighting function to remove the non‐Gaussian noise. We remove the ship tracks by adding a modeling operator inside the inversion that accounts for the drift in the data. We then approximate the model covariance matrix with a prediction error filter that enhances details inside the lake. Unfortunately, the prediction error filter slightly degrades the frequency content of the final depth map. Our images of the Sea of Galilee show ancient shorelines and, inside the lake, rifting features.

The Relationship of Alternative Negligence Rules to Litigation Behavior and Tort Claim Disposition

1992 ◽  
Vol 17 (01) ◽  
pp. 63-87 ◽  
Author(s):  
Stuart Low ◽  
Janet Kiholm Smith
Keyword(s):  
Statistical Evidence ◽  
Data Set ◽  
Extensive Data ◽  
Contributory Negligence ◽  
Accident Injury ◽  
Relationship Of ◽  
Comparative Negligence ◽  
The Relationship ◽  
Modified Forms

Recently most states have abandoned the traditional tort defense of contributory negligence and substituted a form of comparative negligence. Using an extensive data set of auto accident injury claims, we provide evidence on the relationship between negligence rules and claimants' litigation decisions to retain attorneys, file lawsuits and litigate versus settle out of court. Litigation choices appear to be rational responses to the varying incentives created by alternative tort standards. We find that in contrast to comparative negligence, claims arising under comparative negligence are associated with greater probabilities of attorney involvement, higher average award levels, and longer delays in securing payment. Only 37% of claims involving attorneys in contributory negligence states result in a lawsuit being filed compared to 49% and 47% under the pure and modified forms of comparative negligence, respectively. The study provides the first statistical evidence on the litigation costs of the new forms of comparative negligence.

scholarly journals RiskLogitboost Regression for Rare Events in Binary Response: An Econometric Approach

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 579
Author(s):  
Jessica Pesantez-Narvaez ◽  
Montserrat Guillen ◽  
Manuela Alcañiz
Keyword(s):  
Prediction Error ◽  
Rare Events ◽  
Learning Algorithm ◽  
Rare Event ◽  
Generalized Least Squares ◽  
Third Party ◽  
Binary Response ◽  
Data Set ◽  
Rare Class ◽  
Econometric Approach

A boosting-based machine learning algorithm is presented to model a binary response with large imbalance, i.e., a rare event. The new method (i) reduces the prediction error of the rare class, and (ii) approximates an econometric model that allows interpretability. RiskLogitboost regression includes a weighting mechanism that oversamples or undersamples observations according to their misclassification likelihood and a generalized least squares bias correction strategy to reduce the prediction error. An illustration using a real French third-party liability motor insurance data set is presented. The results show that RiskLogitboost regression improves the rate of detection of rare events compared to some boosting-based and tree-based algorithms and some existing methods designed to treat imbalanced responses.

scholarly journals Advanced Deep Learning Framework for Stock Value Prediction

2019 ◽  
Vol 8 (10) ◽  
pp. 2358-2367 ◽  
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Prediction Error ◽  
Economic Value ◽  
Attractive Feature ◽  
Data Set ◽  
Value Prediction ◽  
Learning Framework ◽  
Stock Value ◽  
Deep Learning Neural Network

The main attractive feature to stock market is speedy growth of stock economic value in short yoke of time. The investor analyses the demonstration, estimated value and growth of organizations before investing money in market. The analysis may not be enough by using conventional process or some available methods suggested by different researches. In present days large number of stocks are available in market it is very difficult to study each stock by help of very few suggested foretelling methods. To know the anticipated stock value we need some advanced prediction technology for stock market. This paper introduce an advanced skillful method to plan and analyze the different organizers stock execution in market and prognosticate best suitable stock by predicting close price of stock. The projected arrangement is based on multilayer deep learning neural Network optimized by Adam optimizer. Recent 6 years (2010-2016) data of different organizations are applied to the model to demonstrate the skillfulness of the projected proficient method. From result it has been ascertained that the projected framework is best suited to all different data set of various sectors. The prediction error is very minimal as visible from outcome graph of framework

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