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Decision theory analysis
Decision theory is divided into two categories; normative decision theory and optimal decision theory.
|Normative decision theory
||Optimal decision theory
|Normative decision theory analyzes outcomes of decisions
||Optimal decision theory investigates and analyzed why individuals make the decisions they do
|It determined the optimal decision based on an outcome
||It looks at the assumption people make in making decisions
|The theory is centered on the ideal decision-maker for that specific decision.
||This theory is centered on the decisions and assumptions around the decisions.
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Factorization theorem of Neyman
The multinomial distribution
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Also known as limiting or sequence distribution, asymptotic distribution is a hypothetical distribution used to identify a limiting distribution in a group or series of distributions. Fitting data to a model is not an easy task in real life, as there are so many limited sample sizes. Therefore, a researcher’s best bet is to use the information they have about the behavior of the data sample they are working with. The asymptotic distribution can be applied to this data instead of how a given variable is distributed. Asymptotic distributions are some of the most effective ways of identifying the correct sample sizes.
We cover all topics in asymptotic distribution theory including but not limited to;
2. Probability limit
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Randomization in statistics is when samples to be used in an experiment are chosen randomly. For instance, one may use a simple, random sampling method where the participants are picked from a population where every person has an equal probability of being selected. When randomization is used in an experiment, there is reduced bias. For example, accidental bias and selection bias, which are the two most common types of bias, are eliminated. It is also possible to perform various statistical tests on data when the chosen sample is randomized. Many types of randomization techniques are used today. The most common ones include simple random sampling, stratified random sampling, and permuted block randomization.
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Edgeworth expansion is a technique for predicting a probability distribution based on its cumulants. The data in the distribution is usually the same, but the arrangement of various variables is different, which causes the accuracy of the truncating cumulants to differ. The whole idea behind the use of the Edgeworth expansion is to identify the distribution’s characteristic function. Essentially, the density function of the probability is estimated based on the distribution’s characteristic function and analyzed by inverting the Fourier transform. When values in distribution converge, the expansion is said to be asymptotic because it becomes difficult to approximate the expansion error. If you are having an assignment in Edgeworth expansions and looking for an experienced mathematical statistics assignment solver to complete it, you are at the right place.
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