Consider $K$ processes, each generating a sequence of identical and independent random variables. The probability measures these processes have parameters that must be estimated. Specifically, they share parameter notation="LaTeX">$\theta$ common to all measures. Additionally, process notation="La...

This paper investigates the variance reduction techniques Antithetic Variates (AV) and Latin Hypercube Sampling (LHS) when used for sequential sampling in stochastic programming presents a comparative computational study. It shows conditions under which with AV LHS satisfy finite stopping guarantees are asymptotically valid, discussing detail. computationally compares their use both non-sequent...

We explain three random sampling techniques that are simple but widely applicable for various problems involving huge data sets. The first technique is an immediate application of large deviation bounds. The second and the third ones are sequential sampling or adaptive sampling techniques. We fix one simple problem and explain these techniques by demonstrating algorithms for this problem and di...

Sequential sample selection methods are distinguished from conventional methods in the manner in which random numbers are used to determine the sample. Conventionally, a sample of size n is selected from a sampling frame of N sampling units by selecting n random numbers and mapping then into n of the labels in the sampling frame. Sequential methods require each sampling unit in the sampling fra...

A sequential sampling algorithm or adaptive sampling algorithm is a sampling algorithm that obtains instances sequentially one by one and determines from these instances whether it has already seen enough number of instances for achieving a given task. In this paper, we present two typical sequential sampling algorithms. By using simple estimation problems for our example, we explain when and h...