scholarly journals On Risk Processes with Stochastic Intensity function

1971 ◽  
Vol 6 (2) ◽  
pp. 116-128
Author(s):  
Jan Grandell
Keyword(s):  
Stochastic Process ◽  
Point Process ◽  
Intensity Function ◽  
Insurance Company ◽  
Stochastic Intensity ◽  
Image Position ◽  
Risk Processes

In this paper we are going to study some properties of a stochastic process, which has been proposed by Cramér (1968) as a model of the claims arising in an insurance company. This process has been studied by Cox in a different context. A few elementary results, concerning moments, are given by Cox and Lewis (1966). The present paper will be a survey of some results derived by the author (1970:1) and (1970:2). For detailed proofs we refer to these papers.Let λ(t) be a real-valued stochastic process, such that P{λ(t) < o} = o. We further assume that Eλ(t) = 1 and that Eλ2(t) < ∞ for every fixed value of t. We denote the covarianceThe process λ(t) will play the role of an intensity function. That means, that for every fixed realization of the process, the probability ofand that the number of events in disjoint intervals are independent.We now define a point process N(t), where N(t) is the number of events which have occurred in (o, t]). With this definition we getwhereThe integral is assumed to exist almost surely. This process will be called the N-process.

A limit theorem for point processes by applications

1978 ◽  
Vol 15 (04) ◽  
pp. 726-747
Author(s):  
Prem S. Puri
Keyword(s):  
Limit Theorem ◽  
Point Process ◽  
Random Vector ◽  
Arbitrary Function ◽  
Point Processes ◽  
Probability Generating Function ◽  
Mild Conditions ◽  
Dimensional Distribution ◽  
Image Position

Let 0 ≦ T 1 ≦ T 2 ≦ ·· · represent the epochs in time of occurrences of events of a point process N(t) with N(t) = sup{k : Tk ≦ t}, t ≧ 0. Besides certain mild conditions on the process N(t) (see Conditions (A1)– (A3) in the text) we assume that for every k ≧ 1, as t →∞, the vector (t – TN (t), t – TN (t)–1, · ··, t – TN (t)–k+1) converges in law to a k-dimensional distribution which coincides with that of a random vector ξ k = (ξ 1, · ··, ξ k ) necessarily satisfying P(0 ≦ ξ 1 ≦ ξ 2 ≦ ·· ·≦ ξ k) = 1. Let R(t) be an arbitrary function defined for t ≧ 0, satisfying 0 ≦ R(t) ≦ 1, ∀0 ≦ t &lt;∞, and certain mild conditions (see Conditions (B1)– (B4) in the text). Then among other results, it is shown that The paper also deals with conditions under which the limit (∗) will be positive. The results are applied to several point processes and to the situations where the role of R(t) is taken over by an appropriate transform such as a probability generating function, where conditions are given under which the limit (∗) itself will be a transform of an honest distribution. Finally the results are applied to the study of certain characteristics of the GI/G/∞ queue apparently not studied before.

A limit theorem for point processes by applications

1978 ◽  
Vol 15 (4) ◽  
pp. 726-747 ◽  
Author(s):  
Prem S. Puri
Keyword(s):  
Limit Theorem ◽  
Point Process ◽  
Random Vector ◽  
Arbitrary Function ◽  
Point Processes ◽  
Probability Generating Function ◽  
Mild Conditions ◽  
Dimensional Distribution ◽  
Image Position

Let 0 ≦ T1 ≦ T2 ≦ ·· · represent the epochs in time of occurrences of events of a point process N(t) with N(t) = sup{k : Tk ≦ t}, t ≧ 0. Besides certain mild conditions on the process N(t) (see Conditions (A1)– (A3) in the text) we assume that for every k ≧ 1, as t →∞, the vector (t – TN(t), t – TN(t)–1, · ··, t – TN(t)–k+1) converges in law to a k-dimensional distribution which coincides with that of a random vector ξ k = (ξ1, · ··, ξ k) necessarily satisfying P(0 ≦ ξ1 ≦ ξ2 ≦ ·· ·≦ ξk) = 1. Let R(t) be an arbitrary function defined for t ≧ 0, satisfying 0 ≦ R(t) ≦ 1, ∀0 ≦ t <∞, and certain mild conditions (see Conditions (B1)– (B4) in the text). Then among other results, it is shown that The paper also deals with conditions under which the limit (∗) will be positive. The results are applied to several point processes and to the situations where the role of R(t) is taken over by an appropriate transform such as a probability generating function, where conditions are given under which the limit (∗) itself will be a transform of an honest distribution. Finally the results are applied to the study of certain characteristics of the GI/G/∞ queue apparently not studied before.

The role of differential phase contrast in electron microscopy

1978 ◽  
Vol 36 (1) ◽  
pp. 176-177
Author(s):  
E.M. Waddell ◽  
J.N. Chapman ◽  
R.P. Ferrier
Keyword(s):  
Phase Contrast ◽  
Practical Method ◽  
Position Vector ◽  
Differential Phase ◽  
Pure Phase ◽  
Differential Phase Contrast ◽  
The Difference ◽  
Image Position ◽  
First Moment

Dekkers and de Lang (1977) have discussed a practical method of realising differential phase contrast in a STEM. The method involves taking the difference signal from two semi-circular detectors placed symmetrically about the optic axis and subtending the same angle (2α) at the specimen as that of the cone of illumination. Such a system, or an obvious generalisation of it, namely a quadrant detector, has the characteristic of responding to the gradient of the phase of the specimen transmittance. In this paper we shall compare the performance of this type of system with that of a first moment detector (Waddell et al.1977).For a first moment detector the response function R(k) is of the form R(k) = ck where c is a constant, k is a position vector in the detector plane and the vector nature of R(k)indicates that two signals are produced. This type of system would produce an image signal given bywhere the specimen transmittance is given by a (r) exp (iϕ (r), r is a position vector in object space, ro the position of the probe, ⊛ represents a convolution integral and it has been assumed that we have a coherent probe, with a complex disturbance of the form b(r-ro) exp (iζ (r-ro)). Thus the image signal for a pure phase object imaged in a STEM using a first moment detector is b2 ⊛ ▽ø. Note that this puts no restrictions on the magnitude of the variation of the phase function, but does assume an infinite detector.

scholarly journals PERHITUNGAN PREMI ASURANSI JOINT LIFE DENGAN MODEL VASICEK DAN CIR

2019 ◽  
Vol 8 (3) ◽  
pp. 246
Author(s):  
I MADE WAHYU WIGUNA ◽  
KETUT JAYANEGARA ◽  
I NYOMAN WIDANA
Keyword(s):  
Stochastic Process ◽  
Interest Rates ◽  
Life Insurance ◽  
Insurance Premium ◽  
Insurance Contract ◽  
Insurance Company ◽  
Vasicek Model ◽  
Cir Model ◽  
Premium Payment ◽  
Premium Price

Premium is a sum of money that must be paid by insurance participants to insurance company, based on  insurance contract. Premium payment are affected by interest rates. The interest rates change according to stochastic process. The purpose of this work is to calculate the price of joint life insurance premiums with Vasicek and CIR models. The price of a joint life insurance premium with Vasicek and CIR models, at the age of the insured 35 and 30 years has increased until the last year of the contract. The price of a joint life insurance premium with Vasicek model is more expensive than the premium price using CIR model.

Managing uncertainty in a general insurance company

1990 ◽  
Vol 117 (2) ◽  
pp. 173-277 ◽  
Author(s):  
C. D. Daykin ◽  
G. B. Hey
Keyword(s):  
Computer Model ◽  
Flow Model ◽  
Insurance Companies ◽  
Simulation Methods ◽  
Insurance Company ◽  
Practical Applications ◽  
General Insurance ◽  
Alternative Approaches ◽  
The Impact

AbstractA cash flow model is proposed as a way of analysing uncertainty in the future development of a general insurance company. The company is modelled alongside the market in aggregate so that the impact of changes in premium rates relative to the market can be assessed. An extensive computer model is developed along these lines, intended for use in practical applications by actuaries advising the management of genera1 insurance companies. Simulation methods are used to explore the consequences of uncertainty, particularly in regard to inflation and investments. Some comments are made on the role of actuaries in general insurance. Alternative approaches to describing the behaviour of an insurance firm in the market are considered.

Central Limit Theorems for Interchangeable Processes

1958 ◽  
Vol 10 ◽  
pp. 222-229 ◽  
Author(s):  
J. R. Blum ◽  
H. Chernoff ◽  
M. Rosenblatt ◽  
H. Teicher
Keyword(s):  
Stochastic Process ◽  
Probability Measure ◽  
Central Limit ◽  
Joint Distribution ◽  
Limit Theorems ◽  
Random Variables ◽  
Central Limit Theorems ◽  
Probability Measures ◽  
Image Position ◽  
Positive Integers

Let {Xn} (n = 1, 2 , …) be a stochastic process. The random variables comprising it or the process itself will be said to be interchangeable if, for any choice of distinct positive integers i 1, i 2, H 3 … , ik, the joint distribution of depends merely on k and is independent of the integers i 1, i 2, … , i k. It was shown by De Finetti (3) that the probability measure for any interchangeable process is a mixture of probability measures of processes each consisting of independent and identically distributed random variables.

On two integral equations of queueing theory

1967 ◽  
Vol 4 (2) ◽  
pp. 343-355 ◽  
Author(s):  
J. W. Cohen
Keyword(s):  
Stochastic Process ◽  
Integral Equations ◽  
Positive Constant ◽  
Mathematical Description ◽  
Queueing Theory ◽  
Image Position

In the present paper the solutions of two integral equations are derived. One of the integral equations dominates the mathematical description of the stochastic process {vn, n = 1,2, …}, recursively defined by K is a positive constant, τ1, τ2, …; Σ1, Σ2, …; are independent, non-negative variables, with τ1, τ2,…, identically distributed, similarly, the variables Σ1, Σ2, …, are identically distributed.

scholarly journals Analisis Kepuasan Pengguna Website Asuransi Untuk Peningkatan Pelayanan Asuransi Studi Kasus www.Cakrawalaproteksi.Com

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Syafitri Mona Sari ◽  
Firdaus Firdaus ◽  
A. Haidar Mirza
Keyword(s):  
Social Media ◽  
Customer Satisfaction ◽  
Insurance Industry ◽  
Expected Value ◽  
Insurance Company ◽  
Simple Analysis ◽  
Internet Users ◽  
The Difference

Currently, technology has developed quite rapidly and covers all aspects, including in the insurance industry. Almost every insurance company has a website or social media that can be accessed by all internet users as a means of promotion and transactions. PT. Asuransi Cakrawala Proteksi is an insurance company that also carries out promotions through websites and social media. This research will discuss the customer satisfaction of PT. Asuransi Cakrawala Protection with the role of social media. Customer satisfaction is determined by looking at the difference between the actual value received and the expected value using the website and social media Facebook. From calculating the level of customer satisfaction with ServQual dimensions and simple analysis, a strategy will be produced to maintain or increase customer satisfaction.

The Role of Communication in Business Decision Making

2018 ◽  
pp. 164-186
Author(s):  
Lavinia Nădrag ◽  
Alina Galbeaza Buzarna-Tihenea
Keyword(s):  
Decision Making ◽  
Development Process ◽  
Internal Communication ◽  
Communication Process ◽  
Insurance Company ◽  
Business Decision ◽  
Contemporary World ◽  
Challenges And Opportunities ◽  
New Challenges

The contemporary world bears the mark of a great development process that triggers new challenges and opportunities. In order to evolve, organizations and their members must constantly adapt to this ever-changing environment and, in this regard, communication and feedback play a major role. This chapter deals with the importance of communication, in general, and of internal communication and feedback, in particular, within organizations. The theoretical part of the chapter tackles several important issues related to communication and feedback, such as definitions, models and types of communication, the main barriers to effective communication, and feedback within organizations. The second part of the chapter is focused on a study analyzing the answers to a questionnaire administered to the employees of an insurance company, in order to assess its internal communication and to find correlations between the satisfaction degree resulted from the communication process and the way of viewing the organization's efficiency.

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