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Poisson

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  1. I'm a current PhD student at Cornell, happy to answer any questions. I wouldn't base any decisions off of US News rankings; they have fairly subjective methodology and Cornell is penalized for being a small department. Statistics doesn't necessarily have the same pedigree bias that exists in other fields (in academia at least), so what really matters when seeking a job is the quality of your research and your relationship with your adviser.
  2. I mainly looked through the relevant wikipedia articles (Cholesky, QR, Singular Value Decomposition, Principal Components, etc), and used this book http://www.amazon.com/Numerical-Analysis-Statisticians-Statistics-Computing/dp/1441959440/ref=sr_1_1?ie=UTF8&qid=1325446448&sr=8-1 as an additional reference (concentrating on the numerical linear algebra chapters).
  3. If you have the time, I would recommend taking both classes. In most graduate programs, you will cover some, but probably not all material from both courses (e.g. linear models will probably cover Cholesky/QR decompositions methods to solve the OLS equations) so having the background will be very useful. I took stochastic processes before starting grad school, and I found it very useful as I ran into Markov Chains and Poisson Processes in several classes, and it definitely helped to have the background. I wish I could have taken numerical linear algebra, but it was relatively easy to self-study (probably easier than self studying stochastic processes). If you have the time, I would also highly recommend taking measure theory if you plan on entering a more theoretical program.
  4. Most PhD programs will state that their goal is to train academics, but realistically there are not enough tenure-track positions for every graduate, and a field like statistics has a lot of private/public sector options. That said, I wouldn't explicitly state on your statement of purpose that you don't want to work in academia, as it will not help your case, and could possibly hurt your chances. I don't think that research experience is as important as the sciences. It is difficult to do statistical research as an undergrad, because it generally requires a fairly sophisticate knowledge of statistical software/theory/applications, which are skills that one does not typically acquire until after they have already started a graduate program. Recommendations are fairly important. If you cannot find three recommenders from the Master's program, try to find some from undergrad if they will write enthusiastic letters. Work experience might be a slight positive, but not substantial. Probably not at the very top programs, but it couldn't hurt to apply if you think it would be a good fit and have the funds. Since you have good grades from a top university with a solid math background, I think you would have a good chance at the programs in the next tier like CMU, Duke, Washington, Cornell, UNC, and NC-State.
  5. Yeah, for econ it is standard practice only to mention that you want to go the academic route. Statistics as a discipline seems better geared towards industry employment especially many programs require some sort of internship or statistical consulting course. Right now I have my statement of purpose consisting of about 1/4 my undergraduate background, 1/2 why I feel an econ PhD is a bad fit for me and a stat PhD would be a good fit and 1/4 my potential areas of focus within statistics/future goals. I know that a lot of schools don't really seem to put much weight into them, but it seems like in a case like mine where I am switching disciplines, it might play more of a factor. I went to 2 for econ one had about 20-30 other prospective students, one had like 6. They were similar to what you described except I did not sit in on any classes or meet many graduate students. Conveniently, there were no first year students at either one and basically both schools seemed to harp on the fact that they were fast developing and looking to move up in the rankings and hire more faculty members rather than their actual student placement or quality of life. One other question. I know stat programs typically have much smaller entering classes, but when Duke says that their PhD program "admits between 6-10 students per year" should that be interpreted as they aim for an entering class of 6-10 students or they actually only admit 6-10 students? Because if it is the former, then I feel like I really need to apply to a lot of places. Even Harvard or MIT economics admit like 30-40 students a year in hopes of an entering class of like 25. My program probably admits about 70-80 out of 500-600 applicants for an entering class of 30-40. Is the admit rate in stat programs really that low, or are these figures that websites post not factoring in yield? If Duke only makes 6-10 offers, are they aiming for an entering class of like 2 or 3 people? I can't imagine them having anywhere near 100% yield. I had a few questions about an internal transfer, so I met with the grad secretary and set up a meeting with the DGS. They made it sound like transferring into the Master's program would be relatively simple because I am already admitted into the external graduate school, so admission is solely at the department's discretion. Direct entry into their PhD program does not seem possible right now, but being able to finish the MS in 2-3 semesters and then applying for entry to the PhD program, either at the institution I am at or somewhere else seems like a tenable option. That is good to hear. It would be nice to have at least some degree of consistency and a single person writing your prelim questions. That is kind of what I figured. I have just found C to be a major pain since it has no built-in functions (not even something to find the maximum in an array). I will just hold off on all of this stuff for awhile and concentrate on R since it seems like it will be required for my fall classwork. The program at my school seems to emphasize that measure theory is a strict requirement for entry into the PhD program since the PhD level Mathematical Statistics is measure-theory based. I also think that the conditional acceptance is very rare. but I do know that if you haven't finished your undergrad degree, your admission offer can be rescinded if you decide to drop out or flunk a class in the second semester or something else along those lines. If I understand correctly, the typical PhD Stat program follow a structure similar to: Year 1: Master's Level Probability\Math-Stat (2 semesters) Generalized Linear Models\Anova\Regression (2 semesters), Computational Statistics (1 semester), Measure Theory (1 semester), elective. End of year 1, take Master's prelim (not always required). Year 2: Measure-based probability\math stat (2 semesters), Higher Level Estimation\Computation Class (2 semesters), electives End of year 2: PhD prelim Years 3,4,5: Field Classes, Seminars, independent research I am under the impression that some people can enter directly into the second year if they already hold a Master's degree of have substantial undergraduate coursework, but most Americans probably start at year 1. But it seems like a lot of the elective classes don't necessarily require measure theory/an extensive mathematical statistics background, so you can start taking some of them right away. Do most people tend to finish in 4-5 years? I imagine that an advisor would be less reluctant to hold a student back to polish his dissertation if he/she was not aiming for the academic job market. The graduate secretary at the Stat department made it sound like I should have at least one person in my current department write me a letter of reference to make sure that I am not leaving on bad terms or anything like that. Otherwise, I think that admissions committees could really misinterpret why I am leaving my current program. I explained my situation to one professor who does mostly econometrically and statistically oriented research and whose class I took was primarily statistics based and he offered to write me an excellent letter of recommendation. I also spoke with the econometrician and one of my stat professors who both agreed to serve as references. At this point I have one econometrician (who also publishes in statistics journals), one economist who is a highly active researcher in some interdisciplinary areas like biostatistics and time series analysis ( and who is ranked in the top 200 in IDEAS for publication counts), and my undergraduate advisor who is a probabilist and taught 2 of my stat courses. I think this is a good list, but I could also ask the professor who taught my Master's sequence in Mathematical Statistics. I haven't talked to him in a while and I think he is currently on leave, so I will probably only go that route if it is absolutely necessary. I am not sure what he would write beyond the fact that I got A's in his courses since I did not really talk to him very much outside of class. The econ professor also advised me to apply to public policy PhD programs, since I like the political analysis oriented research of Andrew Gelman et al. I know that Carnegie Mellon has a joint PhD program in statistics and public policy, but I have not heard of any other places like this. Do you know of any other programs like this, or should I just shoot for programs that are strong in Bayesian methods, since it seems like that methodology has the most direct applicability to public policy/demographic related statistical questions? Columbia seems out of the question since they are one of those programs that "highly recommends" the math subject exam.
  6. I am primarily interested in non-academic employment as well. Should I mention on my statement of purpose that I am interested in going into industry or do they mostly want to train academics? I am guessing that this probably varies by department. If I have multiple offers my plan is to visit on a day different than the official visit day because I went on 2 official visits for econ and I felt like they hid some of the less appealing elements of the program and the environment created was a bit artificial. I didn't meet any first year students and only a small selection of grad students. I think I will try to visit the professors which share my research interests, the director of graduate studies, and first and second year students. Also, I plan on applying to the statistics department of the school I am currently attending, do you think it might be worth it to send an email and try to arrange a meeting with the DGS during this semester to get some advice/learn more about the program, or is it standard practice not to contact professors unless you are accepted into the program? That sounds fair. Who usually teaches the classes-younger APs or older, established professors? In econ, 2 profs typically split the semester for theory classes, focusing on their specialty. It exposes you to a wide body of research, but it is difficult to keep up. I have experienced some minor speed improvements by converting R code to Python, which, along with the Numpy package, is pretty efficient for an interpreted language and the syntax is really simple. Is Python used at all within statistics? It seems like FORTRAN might be worth learning. Is that something that you pick up on your own or is it incorporated in the coursework? It is just an introductory stochastic processes class that follows the first 5 chapters in Sidney Resnick's Adventures in Stochastic Processes. I think so, but I am not sure how much weight this will carry. I know within econ of some people who have received conditional acceptances contingent on completion of an absolute non-negotiable requirement to comprehend graduate theory (requiring passing something like intermediate micro, multivariable calculus, linear algebra, etc. in the spring semester) It doesn't seem like stochastic processes is one of these types of classes, but measure theory might be. One other question: Most programs don't explicitly state this, but how many letters of reference should I get from undergrad versus grad? Right now, I am planning on obtaining one from graduate school-an economist who taught one of my theory courses and does a lot of interdisciplinary work (uses explicitly statistic rather than econometric methodology) and 2 stat profs who taught my theory sequences in undergrad (one has a PhD in math and is a probabilist-not sure if that will make a difference. I received A's in all of their classes and I feel like I had a decent rapport with them, so I think the letters should be decent (one of them wrote me letters for econ) but I haven't had much contact with my undergraduate professors over the past year so it might be a bit awkward asking for 10-12 letters and I am hundreds of miles away from my undergraduate campus. I could also get a letter of reference from the econometrician I did research for as an undergrad. My role involved somewhat intricate wage estimations, but it was nothing especially groundbreaking. Nonetheless, it seems like it might be useful to have someone that can speak to my ability to use statistical software. I still keep in contact with him and he is very well known within econometrics and used his connections to help me get into a good econ program (I think he also has a background in physics). The main issue with economists writing my letters of reference is that I need to justify to them that I would be better suited in a Statistics PhD program and I also have no idea how much influence letters from non-statisticians will have on admissions committees. If I go with my current plan, I will have 1 math and 2 econ PhDs writing my letters. Would it be better if I asked at least 1 Stat PhD for a letter?
  7. That sounds fine. I just want to get exposure to everything since I really wasn't able to learn much about the current methodological debates within statistics while studying economics. A lot of economists use variants of Bayesian methods; it seems much less controversial within econ than statistics, but we don't do much in non-parametrics (aside from bootstrapping). I really like that approach of splitting up the exams. I feel like it is ridiculous within econ that we do not have computational/applied portions of exams despite the fact that a majority of our research is in that area. I think that they are really worried about us cheating. Those questions look difficult, but straightforward and fair. Do they provide you with solutions to old exams to practice from? Econ's prelim questions are designed to trick us, screw with our intuition, not reflective of what we learned over the year and are inherently confusing. They are used to weed people out of the program. Do they do this in stats, what are pass rates generally like? (most programs don't publish them) Also, how would you consider your general quality of life as a Stat PhD student? Are your classmates very competitive, do professors treat you with respect, are grad students generally happy, are attrition rates high, etc? Another thing I have noticed is that statistics programs have many more American students than econ programs, which is a stark contrast to econ, where between 70-80% of most programs and 80-90 percent of people who actually finish the degree consist of foreigners (mostly Chinese). I believe that this discrepancy is caused in part by the fact that an undergraduate econ degree is not adequate preparation for graduate work, whereas foreign programs are much more rigorous. If you don't mind sharing, what was your background like before you entered your respective program If you did your undergrad (math/stats degree) in the US, Do you feel that your coursework prepared you well for graduate study or was there a pretty big culture shock? The book required for my time series class is Time Series Analysis and Its Applications With R Examples so hopefully that should provide me with a lot of practice. I also used Data Analysis and Graphics in R as an undergraduate. Do you think that this will provide me with enough preparation? The main limitation I have found in R is that it handles loops INCREDIBLY slowly, even for an interpreted language. I am guessing that these are the cases that you mentioned of statisticians using FORTRAN or C. That is the impression that I got. Stochastic processes is also offered in the spring, so I might just hold off on that and take those two classes cocurrently in the spring, since I will have no obligations to the econ dept at that time. Is this a good idea, Will stochastic processes carry enough weight with adcoms to justify taking it in the fall? Thanks again for all of your advice!
  8. That looks like a good list. UChicago's website lists that they "highly recommend" the GRE math subject test and that they rarely admit students without the test so I don't think I will bother with them, since they also have like a $100 application fee. I went through about the first half of Casella-Berger in my econometrics class, but the selection of topics was somewhat capricious and aimed towards applications to economics. (Basically solely hypothesis testing/maximum likelihood estimation). I went through almost everything in Hogg/Craig except for the "optional chapters." I did not notice a huge difference between the rigor of the two books except that Hogg-Craig was a bit more readable and less compact than C/B. I am guessing the difference is in the later chapters. I don't so much mind going through the material again as long as it is a more holistic approach devoting partial coverage towards non-parametrics and Bayesian methods. I would much prefer to show/derive the various distributions than perform hypothesis test after hypothesis test. Do they typically allow the use of calculators on PhD level exams/will the questions be designed so that the calculation is relatively straightforward? I occasionally had trouble solving double/triple integrals by hand and under time pressure and I also had to memorize all of the distributions and mgfs. So what is considered knowing R "really well"? I am sure that I will learn a lot of its Time Series capabilities in the class I am taking in the fall, but at the moment, I am able to read data into R, run regressions, perform diagnostics, and basic simulations. I can also write basic functions and if I need to use a specific function from a package, I can generally figure it out by looking at its instruction manual. Are there any other things I should know before entering a program? Yeah, I definitely planning on taking it. There is a year long measure theoretic probability class is taught in the fall and requires only the Measure Theory/Integration course as a pre-requisite. It uses the textbook:Richard Durrett: Probability: Theory and Examples. 3rd edition. Is this what I will likely take in a PhD program? Are you referring to the fall course? The spring course explicitly lists in its description that it is geared towards those pursuing higher education in statistics. The spring course also mentioned one of the topics it would cover as: It seems like there is a disconnect between the fall and spring course, and I think that I could handle the spring course without having taken the fall class since they are not part of the same sequence. The main issue is that if I take the fall class, I will have to drop one of my required econ classes, because there is a scheduling conflict,
  9. Thanks for your input again! I am aware of Iowa State's location; I have visited Ames. One of the factors in choosing my current program was location, and I am coming to realize that this was not incredibly wise because I have been so busy with coursework that I have not had a chance to really experience much of the town. So I am relatively ambivalent regarding location. However, I don't think I could live in College Station, Texas, so I don't think I will apply to Texas A&M. I was mainly considering the Canadian schools as potentially funded Master's programs which could serve as a stepping stone to PhD programs because they looked be true "doctoral stream" master's programs since they do not allow direct entry into their PhD program without a Master's degrees. Toronto looks like a great program as well. SUNY Stony Brook seemed interesting because they are a sort of interdisciplinary program between Applied Math, Quant Finance, and Statistics and it felt like a good "safety." I have found that a lot of schools without much "lay prestige" have great faculty members. Moreover, a lot of top schools have really small and specialized statistics programs, which make them ill-suited towards me. So organizing schools by relative fit, I will have: Reach: Wisconsin, Michigan, Washington, Minnesota, Cornell Target: North Carolina State, Iowa State, Purdue, Toronto, Ohio State Safety: SUNY Stony Brook (one more) Does this sound about right? That's exactly how I feel about my Econ Master's that I am expecting to receive. Additionally, I have been through courses with both Casella/Berger and Hogg and Craig and I have no interest in rehashing either. When I start a PhD program, will my first year coursework be mostly measure based probability or are the aforementioned two books going to be references? I have some knowledge of the basic functionality of R (it was the basis of Data Analysis class), but I am trying to learn it in a Unix environment at the moment to allow for things like batch submissions. I am currently working on rewriting a few of my computationally inefficient programs by calling C subroutines from R (basically outsourcing the for loops). Is there a low-level language that serves as an academic standard for running computationally intensive programs? Most economists use FORTRAN, which I am not willing to learn because the syntax is a mess. So a school like NCSU has several statisticians specializing in time series, including D.A. Dickey (of Dickey-Fuller fame). They seem to be on the older side, but some of them are still actively publishing. Should I aim for programs with active faculty on the younger side? I get a bit worried about non-tenured professors because they tend to move around a lot (at least within economics). I will add Cornell to my list, and possibly UChicago, but I don't have much hope of getting in there. I know John Cochrane in GSB does a lot with Time-Series analysis applications in Macroeconomics/Finance, but I have no idea if he works with the statistics program. So I looked at my schedule of classes, and there is a class called Analysis II which uses Rudin as a textbook and covers things like Convergence, Metric Spaces, Contractions, and differential calculus. I never took a class like this, but I self studied several of the topics as they were used in my macroeconomics sequence. This doesn't seem especially relevant for statistics, but it is a pre-requisite for the class entitled "Introduction to Measure and Integration, which uses Royden, Real Analysis. Ash, Real Analysis and Probability as the primary textbooks which is offered in the spring. Topics covered in the spring semester are: the fall covers:
  10. Thanks for the input Yeah, I am kind of confused about where to apply because I am used to evaluating schools based on econ programs which does not necessarily correlate to having a good statistics program (for one thing, there are a lot fewer stat programs than econ!) Which programs would you recommend I remove? I posted this over at talkstats and received Iowa State as a recommendation so along with your three that brings the total number of prospective programs to 14. I was planning on applying to 10-12 programs, do you think this is an appropriate amount, or should I go higher? During my last admission cycle, I applied to 16 econ PhD programs and was accepted to 5. Do you think it would be worthwhile to apply to any Master's programs? One thing very different from econ programs is that many stat programs have terminal master's degrees which seem to have roughly the same coursework as the doctorate and seem like they could be used as a stepping stone to the PhD program. I am still not settled on research interests, I went into a bit more detail on this on the other forum, I will just repost what I wrote below: To summarize: my main interest is in time series analysis, but I would like to learn more about biostatistics and computational statistics in general. I am not really interested in a pure biostats program because I don't want to pigeonhole myself into a specific subfield without being exposed to the wider body of research. I still have a few econ classes to fulfill my requirement for the Master's degree, so I don't think that I will have time to take that class in the fall, so I was planning on taking it in the spring. Is it one of the courses that primarily serves as a signal to adcoms re: mathematically ability or is it useful for first year coursework? Is it more valuable than stochastic processes?
  11. I completed the first year of a top 20 economics PhD program, and I really am unsure if I want to continue. My program is very large, so there is not very much in the way of faculty support, I have found that I am not that interested in economic research. In undergrad, I studied statistics and economics and I enjoyed my statistics coursework much more than economics, I went into economics because I was interested in public policy-that interest has dissipated. I will post my profile below and would appreciate recommendations as to coursework for the fall and places to apply. Type of Undergrad: Decent Private University Undergrad GPA: 3.95 GRE: 560v 790Q 5.5 AWA Grad GPA: 3.1 (they rarely give out A's in my program) Undergrad Courses: Standard Statistics Degree (Calc I-IV, Linear Algebra, Regression, Data Analysis, Advanced Calculus, Analysis, Math Stats I-III (at the level of Hogg and Craig). Standard Undergrad courses. A's in all Math/Stat courses Grad Courses: Standard Year long sequences in Micro, Macro, and Econometrics. Research Experience: 1 years as RA on an applied econometrics project in undergrad. 1 year as RA on an empirical project as grad school. Letters of reference: I plan on getting 2 from undergrad stats professors, 1 from graduate school (I think this is acceptable by most schools). Research Interests: Applied/Computational Statistics specifically biostatistics and high frequency finance. I plan on applying to: SUNY Stony Brook, Wisconsin, Illinois, Michigan State, Michigan, Toronto, University of Western Ontario, Purdue, North Carolina State and Washington. Questions: 1) Should I add/remove any schools from this list? 2) Should I take any courses in the fall to shore up my profile? (Thinking of Time Series Analysis and Stochastic Processes) 3) Will adcoms look past my subpar graduate GPA? I am hoping to improve it in the following semester, since grading is a bit more lenient in field courses than the core. Thanks in advance
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