The Lognormal Distribution A random variable Y follows a lognormal distribution if its natural logarithm, ln Y, is normally distributed. The reverse is also true: If the natural logarithm of a random variable Y, ln Y, is normally distributed, then Y follows a lognormal distribution. Like the normal distribution, the lognormal distribution is completely described by two parameters. Unlike many other distributions,…
Details
A characteristic of Monte Carlo simulation is the generation of a very large number of random samples from a specified probability distribution or distributions to obtain the likelihood of a range of results. Another important use of Monte Carlo simulation in investments is as a tool for valuing complex securities for which no analytic pricing…
Details
Resampling, repeatedly draws samples from the original observed data sample for the statistical inference of population parameters. Boostrap, one of the most popular resampling methods, uses computer simulation for statistical inference without using an analytical formula such as a z-statistic or t-statistic. Both the bootstrap and the Monte Carlo simulation build on repetitive sampling. Bootstrapping…
Details
Probability sampling gives every member of the population an equal chance of being selected. Non-probability sampling depends on factors other than probability considerations, such as a sampler’s judgment or the convenience of access data. Consequently, there is a significant risk that non-probability sampling might generate a non-representative sample. Simple Random Sampling A sampling plan is…
Details
The Central Limit Theorem Central Limit Theorem. Given a population described by any probability distribution having mean µ and finite variance σ2, the sampling distribution of the sample mean X‾\bar{X} computed from random samples of size n from this population will be approximately normal with mean µ (the population mean) and variance σ2/n (the population variance divided by n) when the…
Details
where: SX‾S_{\bar{X}} = the estimate of the standard error of the sample mean, B = the number of resamples drawn from the original sample, θ^b\hat\theta_b = the mean of a resample, and θ‾\bar\theta = the mean across all the resample means.
Details
In hypothesis testing, we test to determine whether a sample statistic is likely to come from a population with the hypothesised value of the population parameter. The Process of Hypothesis Testing A hypothesis is a statement about one or more populations that we test using sample statistics. Stating the Hypotheses Null hypothesis (or null), designated…
Details
The sampling distribution of the mean, when the population standard deviation is unknown, is t-distributed, and when the population standard deviation is known, it is normally distributed, or z-distributed. Since the population standard deviation is unknown in almost all cases, we will focus on the use of a t-distributed test statistic. Test Concerning Differences between Means with Dependent…
Details
Hypotheses concerning the population correlation coefficient may be two- or one-sided, as we have seen in other tests. Let ρ represent the population correlation coefficient. The possible hypotheses are as follows: Two sided: H0: ρ = 0 versus Ha : ρ ≠ 0 One sided (right side): H0: ρ ≤ 0 versus Ha : ρ > 0 One sided (left side): H0: ρ ≥ 0 versus Ha : ρ < 0 Parametric Test of a Correlation…
Details