STATISTICAL ESTIMATION METHODS FOR EXTREME HYDROLOGICAL EVENTS

Author(s):  
P.H.A.J.M. van Gelder ◽  
W. WANG ◽  
J. K. VRIJLING
1962 ◽  
Vol 2 (1) ◽  
pp. 152-160 ◽  
Author(s):  
Norton E. Masterson

In his comprehensive paper entitled “A General Survey of Problems Involved in Motor Insurance”, Dr. Carl Philipson includes remarks with respect to mathematical reserves. The purpose of this paper is to discuss a method of statistical estimation of Third Party Motor Insurance claim reserves. These methods can also be used for Car Damage Insurance, since reserve determination for these rapid settlement property coverages is simpler than for Third Party lines.Dr. Philipson mentions the two main purposes of claim reservesbalance sheet loss reserves which shall be estimated on the safe side for financial reasons, and those reserves needed for risk statistics. In this paper I shall confine my subject to aggregate loss reserves for financial statements and internal management operating reports.In this paper, it will be convenient to refer to both European and U.S. terminology for Motor Car and Automobile Insurance.The comparable terms are:The accident year is the important fiscal period underlying not only the statistical estimation methods discussed in this paper but it is also the basic grouping of accidents in the official reserve tests required in the Annual Statements of U.S. companies for casualty and property lines. An accident year embraces the entire population of claims incurred with accident dates in a particular calendar year, whether reported to the company in that year or subsequently (i.e., incurred /not reported).


2016 ◽  
Vol 12 (1) ◽  
pp. 29-40 ◽  
Author(s):  
Manish Mittal ◽  
Donald L. Harrison ◽  
David M. Thompson ◽  
Michael J. Miller ◽  
Kevin C. Farmer ◽  
...  

1988 ◽  
Vol 52 (2) ◽  
pp. 323-329 ◽  
Author(s):  
T. B. Parkin ◽  
J. J. Meisinger ◽  
S. T. Chester ◽  
J. L. Starr ◽  
J. A. Robinson

2019 ◽  
pp. 57-62
Author(s):  
Aleksey Vladimirovich Tanyukhin

This article is related to obtaining methods for calculating the net-premium for franchise insurance certificate without taking into consideration life insurance. The theoretical foundations of such actuarial expectations are revealed, methods for calculating the mathematical expectation of a random value of insurer's loss from an event insured according to the franchise insurance certificate for gamma and logarithmically normal distributions of a random value of losses are presented, methods for statistical estimation of the mathematical expectation of a random value of the insurer's loss from an event insured in the presence of franchise insurance certificate are described in the article. The author comes to the conclusion that the obtained statistical estimation methods allow us to calculate net-premiums based on historical data without involving any known probabilistic distribution of a random value of loss from analytically specified event insured.


2019 ◽  
Vol 189 (4) ◽  
pp. 261-264 ◽  
Author(s):  
Matthew P Fox ◽  
Jessie K Edwards ◽  
Robert Platt ◽  
Laura B Balzer

Abstract Epidemiologic methods have advanced tremendously in the last several decades. As important as they are, even the most sophisticated approaches are unable to provide meaningful answers when the user lacks a clear study question. Yet, instructors have more and more resources on how to conduct studies and analyze data but few resources on how to ask clearly defined study questions that will guide those methods. Training programs have limited time for coursework, and if novel statistical estimation methods become the focus of instruction, programs that go this route may end up underemphasizing the process of asking good study questions, designing robust studies, considering potential biases in the collected data, and appropriately interpreting the results of the analysis. Given the demands for space in curricula, now is an appropriate time to reevaluate what we teach epidemiology doctoral students. We advocate that programs place a renewed focus on asking good study questions and following a comprehensive approach to study design and data analysis in which questions guide the choice of appropriate methods, helping us avoid methods for methods’ sake and highlighting when application of a new method can provide the opportunity to answer questions that were intractable with traditional approaches.


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