Optimal threshold for static 99R (original) (raw)

Alice in actuarial-land: through the looking glass of changing Static-99 norms

The journal of the American Academy of Psychiatry and the Law, 2010

The Static-99, an actuarial rating method, is employed to conduct sexual violence risk assessment in legal contexts. The proponents of the Static-99 dismiss clinical judgment as not empirical. Two elements must be present to apply an actuarial risk model to a specific individual: sample representativeness and uniform measurement of outcome. This review demonstrates that both of these elements are lacking in the normative studies of the Static-99 and its revised version, the Static-99R. Studies conducted since the publication of the Static-99 have not replicated the original norms. Sexual recidivism rates for the same Static-99 score vary widely, from low to high, depending on the sample used. A hypothetical case example is presented to illustrate how the solitary application of the Static-99 or Static-99R recidivism rates to the exclusion of salient clinical factors for identifying sexual dangerousness can have serious consequences for public safety.

On criteria for evaluating models of absolute risk

Biostatistics, 2005

SUMMARY Absolute risk is the probability that an individual who is free of a given disease at an initial age, a, will develop that disease in the subsequent interval (a,t]. Absolute risk is reduced by mortality from com-peting risks. Models of absolute risk that depend on covariates ...

Value-at-Risk Estimates from a SETAR Model

2016

A self-exciting threshold autoregressive (SETAR) model will be fitted to PSEi and value-at-risk estimates would be computed. Backtesting procedures would be employed to assess the accuracy of the estimates and compared with estimates derived from two other approaches to VaR estimation.

The Log-logistic Regression Model with a Threshold Stress

TEMA - Tendências em Matemática Aplicada e Computacional, 2011

In this paper we propose an accelerated lifetime test model with threshold stress under a Log-logistic distribution to express the behavior of lifetimes and a general stress-response relationship. We present a sampling-based inference procedure of the model based on Markov Chain Monte Carlo techniques. We assume proper but vague priors for the parameters of interest. The methodology is illustrated on an artifitial and real lifetime data set.

On the appropriate choice of the risk model

Applied Mathematical Sciences

Recently the classical actuarial risk model for the evaluation of the ruin probability has been generalized to include dependency relations between the claim occurrences times and the claim amounts. For instance it has been incorporated into the stochastic model that the distributions of the times between consecutive claim occurrences times may depend on the last previous claim amount [1] or that claim sizes depend on the past of the point process of instants when claims are presented [2,3]. Results on ruin probabilities and related quantities have been published in several papers under such assumptions. This evolution implies that in a concrete application we have to choose between different versions of the actuarial risk model. This choice should be performed in a reasonable and objective way taking into account our empirical knowledge of the risk process. This leads us naturally to develop statistical tests able to distinguish certain classes of marked point processes. In order to assure the optimal use of the data it seems indicated to look for locally most powerful tests [4] and a clever choice of the period of observation of the risk process greatly facilitates the task and leads to simple procedures which are easy to implement. Keywords: Actuarial risk models, dependencies between claim occurrences times and claim amounts, (locally) most powerful statistical tests, embedded renewal processes, choice of the period of observation of the risk process

401 Exposure Dependent Modeling of Percent of Ultimate Loss Development Curves

2013

This paper presents a loss development model in which exposure period dependence is fundamental to the structure of the model. The basic idea is that an exposure period, such as an accident year or policy year, gives rise to a particular distribution of accident date lags, where the accident date lag is the time elapsed from the start of the exposure pedod till the accident date. The paper shows how to derive the density of the accident date lag from a familiar parallelogram diagram. A fairly general theory of development is then presented and simplified under certain conditions to arrive at a total development random variable whose cumulative distribution is related to the usual percent of ultimate development curve. After presenting the theory, the paper tums to practical applications. Simulation is used to generate consistent pattems for different exposure pedods. A convenient accident period development formula is derived and then used to fit and convert factors. The average dat...

CODATA Symposium on Risk Models and Applications Kiev, Ukraine, Oct. 5, 2008

2008

This international symposium was held at Kiev National Technical University, organized by CODATA in connection with the 21 st International CODATA Conference following the symposium and in cooperation with GI TC 4.6 WG on Risk Management. The symposium homepage is http://www.codata-germany.org/RMA\_2008 This Symposium was dedicated to the data science and information system aspects of Risk Model Structure, Implementation, and Application on a very interdisciplinary level. It gave a very good overview of risk models and application from lots of different perspectives.

thresholdmodeling: A Python package for modeling excesses over a threshold using the Peak-Over-Threshold Method and the Generalized Pareto Distribution

Journal of Open Source Software

Extreme value analysis has emerged as one of the most important disciplines for the applied sciences when dealing with reduced datasets and when the main idea is to extrapolate the observations over a given time. By using a threshold model with an asymptotic characterization, it is posible to work with the Generalized Pareto Distribution (GPD) (Coles, 2001) and use it to model the stochastic behavior of a process at an unusual level, either a maximum or minimum. For example, consider a large dataset of wind velocity in Florida, USA, during a certain period of time. It is possible to model this process and to quantify extreme events' probability, for example hurricanes, which are maximum observations of wind velocity, in a time of interest using the return value tool.