Sentences with phrase «value of the random variable»

Thus the most efficient estimate of the value of E -LCB- U (t)-RCB-, the trend rate, is the mean value of the random variable given previously as

In modern terminology, De Witt expressed the value of a life annuity as the expected value of a random variable.

Not exact matches

The amount of all possible sums related to the random variable is the expected value.

Expected Value — An expected value is associated with the areas of random variaValue — An expected value is associated with the areas of random variavalue is associated with the areas of random variables.

This might also involved was is the integral of what is called the probable density function as it applies to a random variable in terms of all its possible values.

Unlike the common practice with other mathematical variables, a random variable can not be assigned a value; a random variable does not describe the actual outcome of a particular experiment, but rather describes the possible, as - yet - undetermined outcomes in terms of real numbers.

At any such point, is a random variable with Still conditioning on, consider counterfactual outcomes as varies over, averaging over the conditional distribution of given: There is a structural function interpretation for: within a school with, we can obtain potential expected output for various assigned values of the teacher input, holding constant the distribution of classroom characteristics (at the conditional distribution of given).

It represents the distribution of a random variable in the form of a bell curve, with the exact shape defined by the expected value and the standard deviation.

The cost of retirement, also known as the stochastic (or random) present value of retirement, was the actual cost of paying for a given income in retirement when the unknown variables of longevity and asset returns were allowed to occur by chance.

General Introduction Two Main Goals Identifying Patterns in Time Series Data Systematic pattern and random noise Two general aspects of time series patterns Trend Analysis Analysis of Seasonality ARIMA (Box & Jenkins) and Autocorrelations General Introduction Two Common Processes ARIMA Methodology Identification Phase Parameter Estimation Evaluation of the Model Interrupted Time Series Exponential Smoothing General Introduction Simple Exponential Smoothing Choosing the Best Value for Parameter a (alpha) Indices of Lack of Fit (Error) Seasonal and Non-seasonal Models With or Without Trend Seasonal Decomposition (Census I) General Introduction Computations X-11 Census method II seasonal adjustment Seasonal Adjustment: Basic Ideas and Terms The Census II Method Results Tables Computed by the X-11 Method Specific Description of all Results Tables Computed by the X-11 Method Distributed Lags Analysis General Purpose General Model Almon Distributed Lag Single Spectrum (Fourier) Analysis Cross-spectrum Analysis General Introduction Basic Notation and Principles Results for Each Variable The Cross-periodogram, Cross-density, Quadrature - density, and Cross-amplitude Squared Coherency, Gain, and Phase Shift How the Example Data were Created Spectrum Analysis — Basic Notations and Principles Frequency and Period The General Structural Model A Simple Example Periodogram The Problem of Leakage Padding the Time Series Tapering Data Windows and Spectral Density Estimates Preparing the Data for Analysis Results when no Periodicity in the Series Exists Fast Fourier Transformations General Introduction Computation of FFT in Time Series

The net level premium reserve is found by taking the expected value of the loss random variable defined above.

To determine the actuarial present value of the benefit we need to calculate the expected value E (Z)-LCB- \ displaystyle \, E (Z)-RCB- of this random variable Z. Suppose the death benefit is payable at the end of year of death.

The NLP reserve at time t is the expected value of the loss random variable at time t given K (x) > t

Our objective is to find the value of the net level premium reserve at time t. First we define the loss random variable at time zero for this policy.

This time the random variable Y is the total present value random variable of an annuity of 1 per year, issued to a life aged x, paid continuously as long as the person is alive, and is given by:

We chose to retain the whole sample for the analyses and deal with the missing values for the sexual peer norm variables of the participants who did not complete the online questionnaire, because it has been shown that this yields more accurate results than listwise deletion, even when data are not missing completely at random (Schafer & Graham, 2002).