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Cavalerius

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  1. It is interesting because given the description that I cited of the various fields, I would definitely have thought that operations research rather than statistics would be the more obvious fit for my own research interests: My background has been heavier in pure math than in computer science or applied math, and my primary interest--although I am trying to remain somewhat flexible on this point--is in topics that lie at the intersection of statistics and finance. Thus, I looked at the programs to which I'd been accepted and the opportunities that each provided for me to do research in statistics that would also be relevant to finance in particular, and it seemed that while operations research and areas like stochastic calculus are mathematically rich and pertinent to mathematical finance, more promise was held (for both academic inquiry and for industrial applications) by some of the newer methods and tools being devised in statistics and machine learning. Moreover, given my predisposition to finance, I looked at how closely aligned each school's statistics program was with its business school (through shared classes, dual appointments, presence of faculty on dissertation committees, etc.) as well as the quality of the business school. After accounting for the strength of research fit, I also made by decision based on the location of the school, the funding package and related responsibilities, and the structure of the program (e.g., the number of required classes, size of the program, and accessibility of professors). Anyway, I am not sure whether any of this information is helpful in your case, but right or wrong, that was my thought process. (It was without a doubt one of the tougher decisions that I have had to make in my academic career thus far. I am sure given the quality of the programs in question that you've worked very hard to have these opportunities, and you want to ensure that you are making the best decision to both reap the benefits of your work to this point and set you up for future success, so it is certainly a difficult decision to make.)
  2. Although I did not apply to any of the schools that you mention (or to Cornell either, for that matter), I also had to choose between operations research and statistics and found the following description on Cornell's statistics PhD website to be helpful: Choosing a Field of Study There are many graduate fields of study at Cornell University. The best choice of graduate field in which to pursue a degree depends on your major interests. Statistics is a subject that lies at the interface of theory, applications, and computing. Statisticians must therefore possess a broad spectrum of skills, including expertise in statistical theory, study design, data analysis, probability, computing, and mathematics. Statisticians must also be expert communicators, with the ability to formulate complex research questions in appropriate statistical terms, explain statistical concepts and methods to their collaborators, and assist them in properly communicating their results. If the study of statistics is your major interest then you should seriously consider applying to the Field of Statistics. There are also several related fields that may fit even better with your interests and career goals. For example, if you are mainly interested in mathematics and computation as they relate to modeling genetics and other biological processes (e.g, protein structure and function, computational neuroscience, biomechanics, population genetics, high throughput genetic scanning), you might consider the Field of Computational Biology. You may wish to consider applying to the Field of Electrical and Computer Engineering if you are interested in the applications of probability and statistics to signal processing, data compression, information theory, and image processing. Those with a background in the social sciences might wish to consider the Field of Industrial and Labor Relations with a major or minor in the subject of Economic and Social Statistics. Strong interest and training in mathematics or probability might lead you to choose the Field of Mathematics. Lastly, if you have a strong mathematics background and an interest in general problem-solving techniques (e.g., optimization and simulation) or applied stochastic processes (e.g., mathematical finance, queuing theory, traffic theory, and inventory theory) you should consider the Field of Operations Research.
  3. Thanks for the astute observation and encouragement, @Bayesian1701, and for the additional information and comments, @Stat PhD Now Postdoc. I guess now that I have basically all the information that I need to make my decision, I just wanted to make sure that my reasoning made sense. You have all been very helpful through the whole process, and thanks again for all the insights!
  4. Thanks for the clarification, @Stat PhD Now Postdoc. It just seemed that there are fewer academic and industry job prospects, as well as less interest in general among the students with whom I have met, when it comes to applied probability and stochastic processes, and some of the developments in other areas of statistics seem more promising, and I want to keep some options open since I have not committed to doing research in a particular area and had only picked schools initially that fit the type of research and projects that I had done up to this point, which were more in the applied probability vein. I am, however, mainly interested in developing statistical methodology with a focus on applications to finance where possible. (That is why UT Austin was particularly intriguing to me since the university excels in finance, and a few of the professors in the statistics department have dual appointments in the finance department or collaborate therewith to develop methodology for financial data analysis in particular.)
  5. I had posted this question as a follow-up to a previous post that I made about Texas PhD programs specifically, but it likely deserved its own post. I have now concluded my visits to all the schools to which I was admitted, and I have narrowed down my choices to UT Austin, NCSU, UNC-STOR, and TAMU. Assuming that the funding is relatively equal after accounting for the cost of living, that the surrounding environment itself is relatively inconsequential to one’s quality of life, and that one works with a top professor within the given department, would choosing any one of these programs over the others provide better opportunities for the future, especially as regards the obtainment of academic jobs after graduation? Some of my thoughts are as follows: I really like the small size and research focus of UT Austin, but given its newness, it is hard to tell what kind of program it will ultimately turn out to be, whereas NCSU and TAMU, though larger--with NCSU being considerably larger--are already established as top statistics departments and UNC-STOR's focus on applied probability and theoretical statistics appeals to my mathematical inclinations--yet in talking with certain professors, I got the sense that these theoretical subjects are not as favored as others these days within the statistics community and of course, as compared with the others, UNC-STOR does not really offer the opportunity to do research in Bayesian statistics, at least not in the department proper. Any further insights into what might distinguish these programs from one another are greatly appreciated.
  6. True, sorry, I did not see that you had never taken a probability class. I like Resnick's book, but it's probably too mathematical and not as helpful without some prior background in probability. At Duke, it seems that Resnick's book is used in the first semester for their probability requirement, and that is the reason that I picked it up. UT Austin doesn't formally require probability theory, but otherwise their program is similar to Duke's, so I am choosing to use my time before starting my PhD studies in the fall to learn some more probability on my own.
  7. I don't know whether it's the best, but I recently picked up Resnick's A Probability Path and find its presentation to be very lucid. Here is a link to its table of contents: https://d-nb.info/955671957/04. If you are looking for a more mathematical book that deals with abstract measure theory, this book might not be what you need, however, since it is geared toward graduate students in statistics, applied probability, biology, operations research, mathematical finance and engineering, rather than in pure mathematics.
  8. Cavalerius

    Texas Stats PhD Programs

    Thanks, everyone, for the opinions and insights. It will be difficult to make a decision, so if I could add just one final question here, I would appreciate any further feedback. Right now, I have narrowed down my choices to UT Austin, NCSU, UNC-STOR, and TAMU. Assuming that the funding is relatively equal given the cost of living, would choosing any one of these programs over the others provide better opportunities for the future, especially for academic jobs? I really like the small size and research focus of UT Austin, but given its newness, it is hard to tell what kind of program it will ultimately turn out to be, whereas NCSU and TAMU, though larger--with NCSU being considerably larger--are already established as top statistics departments and UNC-STOR's focus on applied probability and theoretical statistics appeals to my mathematical sensibilities--yet perhaps these subjects are not as favored as others these days. (I know that the original topic was strictly about Texas programs, but I do not want to overlook other reputable programs. Nonetheless, all things being equal, I would still prefer to live in Texas!)
  9. From what I gathered, the biostat and stat departments at UNC are now relatively distinct, although it seems that in the past, on the basis of certain comments from professors, the two were more collaborative. The UNC STOR department also appears to have closer connections with Duke than with NCSU. Perhaps this situation is due to Duke being a bit closer geographically and easily reachable by bus, to NCSU being so large that it doesn't really need to associate with anyone else, and to Duke and UNC STOR having somewhat complementary programs (with Duke focusing on Bayesian and computational methods and UNC STOR focusing on probability and stochastic processes).
  10. Cavalerius

    Duke v Michigan v NC State

    From my impressions so far, comparing NC State and UNC-STOR, the initial sequence is more rigorous at UNC-STOR than at NC State, but NC State seems more flexible, allowing students to take the qualifying exam before starting, so that perhaps for a well-prepared student, there is no real difference in rigor between the two. @Stat PhD Now Postdoc Do you really see much of a difference between UNC-STOR and NC State when it comes to rankings and prestige? NC State's placement data do not appear readily available, but UNC-STOR seems to have some good placements of late, and the difference in ranking seems minimal, at least as far as the USNWR is concerned, with UNC-STOR moving up in position over the past few iterations as well. Also, @StatNerd100, if you don't mind sharing, did you apply for a fellowship at NC State, or did they just award you one?
  11. Cavalerius

    UT Austin vs. Duke

    @SkyHighway Were you accepted into the PhD program in SDS or in McCombs at UT Austin?
  12. Cavalerius

    Fall 2019 Statistics Applicant Thread

    @bigdata Okay, thanks for the info. I will probably have to go later in the month. I think it would be beneficial to visit at the same time as other admitted students, but that is hard to arrange if there is no official visit day. Anyway, enjoy your visit!
  13. Cavalerius

    Fall 2019 Statistics Applicant Thread

    To anyone admitted to Texas A&M, are you planning a campus visit? As far as I know, there is no official visit planned at this point, but it would likely be beneficial to be able to visit at the same time as other potential classmates.
  14. Cavalerius

    Texas Stats PhD Programs

    Thanks for the response, Bayesian1701. I also read your post about your visits to both A&M and Austin, which helped give me some insight into the differences between the programs as well. I suppose since your interests were in Bayesian statistics, you didn't really look at Rice, which doesn't seem involved in Bayesian research. I think that I just need to spend more time considering the programs and how they fit with my research interests and overall objectives. Still, making such a big decision does not come easy, especially since it is easy to be concerned about what one's missing by not choosing certain programs.
  15. Cavalerius

    Texas Stats PhD Programs

    Thanks, Stat PhD Now Postdoc, for the feedback. Going back to the post that I created earlier on this forum about Bayesian vs. frequentist statistics, I am still deciding whether I would want to commit to a Bayesian perspective, but I have been reading Hoff's A First Course in Bayesian Statistical Methods, which the syllabi of the first-year UT Austin PhD statistics courses indicated as a good primer on the subject. The approach is very intriguing and does offer some interesting perspectives on statistics. The PhD statistics program at UT Austin is also interesting because according to the documents available on the website, about half of the core faculty are 50% appointments between mainly the business school and the new statistics department, an arrangement that is actually one of the desirable aspects of the program to me, and of course, I have heard that Austin is a great place to live: indeed, it was selected as US News' best place to live this past year.
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