Monthly Archives: December 2016

Writing and Defence of Research Proposal

On 23rd Dec, I have required attending the PhD deference proposal. This is my first-time experience to do the assessment. As the examiner, I also google myself to find out what criteria that examiner wants to evaluate the proposal defend. Here are some good tips that I obtained from the internet on how to writing and also defend your PhD proposal
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Hints for Writing a PhD Proposal
By Angelos Keromytis (April 2010)

  • a description of the problem in enough detail to clearly state the thesis proposition (next item)
  • a proper, concise thesis proposition; this is not an abstract statement like “we’re going to investigate the insider problem”, but something along the lines of “our hypothesis is that the use of XYZ technology in environment Z under constraints Q can identify insider attackers with probability Z” — obviously, the fewer qualifiers the better, but you also need to be accurate; since this is a thesis proposal, we will cut you some slack — but it’s in your best interest to think hard about this, since it is the anchor point of your whole thesis (and the next few years’ worth of work for you)
  • a description of the related work, how it does not solve the problem, and how your hypothesis has not been tested before
  • preliminary results (if any) that indicate that you have reason to believe that the hypothesis holds
  • additional experiments that you will run to prove that the hypothesis holds
  • what you’ll need to build to run said experiments (and what you’ve already built)
  • what happens if you can’t run some of these experiments, or if they give you “bad” results — what’s your failover?
  • what are the expected contributions, keeping in mind that each major contribution must demonstrate novelty, non-triviality, and usefulness (so, “first”, “best”, “only” are good adjectives here)
  • how long you expect all this to take

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Example of Bootstrapping

In a statistical analysis, bootstrapping is a popular technique, especially useful when the sample size is small. It is involved with resampling and the technique is assume nothing about the distribution of our data. It is noted that a small sample (<40), we can assuming a normal or a t-distributions.

Bootstrapping can be run in many statistical software in the market. For me, I like to use SPSS, since the software has integrating bootstrapping in each analysis. Many codes that implementing bootstrapping can be found in Matlab and R-language.

The following example is how this technique works that been obtained from this source:

Example Sample

We begin with a statistical sample from a population that we know nothing about. Our goal will be a 90% confidence interval about the mean of the sample. Although other statistical techniques used to determine confidence intervals assume that we know the mean or standard deviation of our population, bootstrapping does not require anything other than the sample.

For purposes of our example, we will assume that the sample is 1, 2, 4, 4, 10.

Example – Bootstrap Sample

We now resample with replacement from our sample to form what are known as bootstrap samples. Each bootstrap sample will have a size of five, just like our original sample. Since we randomly selecting and then are replacing each value, the bootstrap samples may be different from the original sample and from each other.

For examples that we would run into in the real world we would do this resampling hundreds if not thousands of times. In what follows below, we will see an example of 20 bootstrap samples:

2, 1, 10, 4, 2
 4, 10, 10, 2, 4
 1, 4, 1, 4, 4
 4, 1, 1, 4, 10
 4, 4, 1, 4, 2
 4, 10, 10, 10, 4
 2, 4, 4, 2, 1
 2, 4, 1, 10, 4
 1, 10, 2, 10, 10
 4, 1, 10, 1, 10
 4, 4, 4, 4, 1
 1, 2, 4, 4, 2
 4, 4, 10, 10, 2
 4, 2, 1, 4, 4
 4, 4, 4, 4, 4
 4, 2, 4, 1, 1
 4, 4, 4, 2, 4
 10, 4, 1, 4, 4
 4, 2, 1, 1, 2
 10, 2, 2, 1, 1

Example – Mean

Since we are using bootstrapping to calculate a confidence interval about the population mean, we now calculate the means of each of our bootstrap samples. These means, arranged in ascending order are: 2, 2.4, 2.6, 2.6, 2.8, 3, 3, 3.2, 3.4, 3.6, 3.8, 4, 4, 4.2, 4.6, 5.2, 6, 6, 6.6, 7.6.

Example – Confidence Interval

We now obtain from our list of bootstrap sample means a confidence interval. Since we want a 90% confidence interval, we use the 95th and 5th percentiles as the endpoints of the intervals. The reason for this is that we split 100% - 90% = 10% in half so that we will have the middle 90% of all of the bootstrap sample means.

For our example above we have a confidence interval of 2.4 to 6.6.