A Markov multiple state model for epidemic and insurance modelling

A stop-loss risk index

(2001). “Principal Applications of Bayesian Methods in Actuarial Science: A Perspective”, Udi E. Makov, October 2001. North American Actuarial Journal: Vol. 5, No. 4, pp. 69-72.

093080 (E53) Stochastic analysis of the interaction between investment and insurance risks : Parker G., North American Actuarial Journal, Vol. 1, Nr. 2, 1997, pp. 55–84

(2010). “Pricing Asian Options and Equity-Indexed Annuities with Regime Switching by the Trinomial Tree Method”, Fei Lung Yuen and Hailiang Yang, April, 2010. North American Actuarial Journal: Vol. 14, No. 2, pp. 272-277.

The purpose of this paper is to identify a workhorse mortality model for the adult age range (i.e., excluding the accident hump and younger ages). It applies the “general procedure” (GP) of Hunt & Blake [(2014), North American Actuarial Journal, 18, 116–138] to identify an age-period model that fits the data well before adding in a cohort effect that captures the residual year-of-birth effects arising in the original age-period model. The resulting model is intended to be suitable for a variety of populations, but economises on the number of period effects in comparison with a full implementation of the GP. We estimate the model using two different iterative maximum likelihood (ML) approaches – one Partial ML and the other Full ML – that avoid the need to specify identifiability constraints.

(2005). “Pensions and Capital Structure: Why Hold Equities in the Pension Fund?”, John Ralfe, Cliff Speed, and Jon Palin, July 2004. North American Actuarial Journal: Vol. 9, No. 4, pp. 125-130.

093074 (E50) Corporate hedging in the insurance industry: The use of financial derivatives by U.S. insurers : Cummins J.D., Philips R.D., Smith S.D.,North American Actuarial Journal, Volume 1, Nbr. 1, 1997, pp. 13–49

(1998). “Utility Functions: From Risk Theory to Finance”, Hans U. Gerber and Gerard Pafum, July 1998. North American Actuarial Journal: Vol. 2, No. 3, pp. 94-95.

(2000). “The Lee-Carter Method for Forecasting Mortality, with Various Extensions and Applications“, Ronald Lee, January 2000. North American Actuarial Journal: Vol. 4, No. 1, pp. 91-93.

(1999). “Economic Valuation Models for Insurers,” David F. Babbel and Craig Merrill, July 1998. North American Actuarial Journal: Vol. 3, No. 3, pp. 145-152.

(1998). “Credibility Using a Loss Function from Spline Theory”, Virginia R. Young, January 1998. North American Actuarial Journal: Vol. 2, No. 1, pp. 114-116.

The current definition of sinus tachycardia is a heart rate >100 beats per minute (bpm).1 This limit was set arbitrarily when heart rate was not yet regarded as a risk factor for cardiovascular disease, probably with the main purpose of distinguishing between a disease state (fever, thyrotoxicosis, anemia, congestive heart failure, etc) and a normal condition. In recent years, interest has been aroused by the awareness that fast heart rate is a potent precursor of hypertension, atherosclerosis, and their sequelae.2 3 4 5 6 7 8 9 10 In addition, many leading epidemiological studies have shown that tachycardia is associated with an increased risk of death from cardiovascular and noncardiovascular causes. This relationship has been found in general populations,3 4 5 6 7 in elderly individuals,9 and in hypertensive cohorts.10 In all of these studies, the heart rate value above that in which a significant increase in risk was observed was below the 100 bpm threshold (Table 1⇓). Only in the study by Levy et al3 was tachycardia defined as a heart rate >99 bpm, but in that study the cutoff between normal and high heart rate was chosen arbitrarily, and the highest heart rate value measured during the examination was taken to define the subject’s heart rate. In all the other studies, the threshold level between normal and fast heart rate was between 79 and 90 bpm. View this table: Table 1. Heart Rate Values Above Which a Significant Increase in Risk Was Found: Data From 9 Epidemiological Studies The normalcy limits of a clinical variable can be established according to different criteria. For many parameters, such as most biochemical indexes, the 95% confidence interval is calculated to identify the upper normal limit of the variable. This statistical approach does not appear suitable for those clinical variables …

The aim of this work is to present a method to compute the expected amount of annual claims in the health insurance. (A more detailed analysis of the method can be found in Kovářová (1998).) We will be especially concerned with the permanent health insurance and compare our numerical results with those computed and published for the permanent health insurance in C.M.I.R. (1991). At the same time we will give a general methodology applicable not only to the permanent health insurance but for other types of health insurance as well.The methods used today can be divided into three groups: the method of the decrement tables, the Manchester Unity method and the multiple state model. We would like to describe a simple method to compute from the data of the multiple state model the bases for the Manchester United and for the decrement tables methods.

Comparison of deterministic and stochastic SIS and SIR models in discrete time

The Economic Function of Underfunded Pension Plans