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LostLamb

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  1. Thanks very much for sharing your insights! Having read all the responses here (and after consulting friends who majored in pure math), I think I will be taking nonparametrics for a grade and will only be auditing functional analysis out of interest. Really appreciate everyone's views on this issue!
  2. Got it! The only reason I'm sparing some thought for functional analysis is because it's related to the applied area my honors thesis is in. I think at my stage it's not necessary to actually use the results from functional analysis. But since I have to pick a class anyway I'm just toying with the idea of getting more background.
  3. Yeah one reason why I was looking at functional analysis is that quite a lot of people have mentioned its relevance to things like kernels, Bayesian nonparametrics and functional data analysis. For now I'm browsing through kreyszig's Introductory functional analysis with applications to see if I would be somewhat interested in the material before committing an entire semester to it. Thanks again for everyone who replied! Wasn't really expecting this much input!
  4. Thanks for all the advice! As for proofs, it's not that I hate it, but it's not like I love it either. But I certainly think it's important to be able to read and digest the proofs though. Otherwise I don't think one can get a good grasp of the literature in whatever subfield one is working in. I'm really more into applied research (but not too applied to the point where you are just using tools developed by others to work solely on empirical data). Will look more into biostats programs!
  5. Thanks so much for your input! If you don't mind, I have another quick question: Out of the three classes, which one do you think is the most useful (ignoring the difficulty of the class) for statistics research (especially in the more applied areas)? The main issue i have with pure-ish math classes (like functional analysis) is that they tend to be a little too detached from statistics and oftentimes i find it quite hard to maintain sufficient motivation to power through the deluge of theorems, lemmas and proofs (e.g. while studying metric spaces, most of the time i was like "who the heck cares if a set is compact!". Of course, now I see why people care after studying some advanced probability and optimisation theory). This motivation issue is probably made worse by fact that my main background is in psychology and I ventured into statistics by accident. So it would be great to get some insight into how each topic is relevant to actual statistics/methodology research!
  6. Hi all, I was hoping to get some advice on which course I should take in the coming semester. I only need one more class to complete my undergrad and I am currently faced with three choices: 1) Functional Analysis 2) Stochastic Processes II (i.e. continuous-time Markov chains, martingales) 3) Nonparametric Statistics To provide some background, here's the list of math/stats classes (with grades) I've completed Math Linear Algebra (A), Calculus I,II (B+) -- Used pass/fail option here, Calculus III (i.e. Multivariable) (B+), Probability (A), Mathematical Stats (A-), Real Analysis I (B+), Real Analysis II (A-), Real Analysis III (Metric Spaces) (B+), Stochastic Processes I (A-), Ordinary Differential Equations (A-), Advanced (Measure-theoretic) Probaility (grades not released yet but probably A- and above) Statistics Intro to Computing (B+) -- Used pass/fail option here, Regression Analysis (A+), Simulation (A+), Categorical Data Analysis (A+), Data Mining (A), Computer-Intensive Statistics (A+), Linear Models (A-), Bayesian Statistics (A), Multivariate Statistics (A), Structural Equation Modeling (A), Psychometrics (A) As you can probably tell, my math grades are less than stellar, especially in analysis. Considering how real analysis is so important, I was wondering if getting a good grade in functional analysis would kind of make up for it. However, the main drawback of taking functional analysis is that the professor teaching it next semester is known for making math modules a lot more difficult than they usually are. So taking functional analysis is kind of like a gamble: if I do well then great, but if not I may end up getting a really ugly grade. On the other hand, I know for a fact that it would be easier to score a decent grade for the other 2 classes. Any thoughts?
  7. Thank you all for your responses! The information provided has been immensely helpful. Really hope that I can be part of this amazing quant psych community one day!
  8. Thank you both for providing such detailed responses! They were really helpful! I have a question about the interview process: What are the things faculty look out for when interviewing potential candidates, especially when it comes to international students? I know (or am I mistaken?) that for domestic students they tend to invite potential candidates for on-site visits, but how does it work for international applicants who can't simply hop on a plane to the States due to financial constraints? Moreover, are there any pitfalls one should be aware of during an interview? I'm probably sounding like an idiot for asking something so obvious but I would really like to avoid committing a cultural faux pas in something as important as a grad-interview. @MissData Is it possible to share more about your grad experience at OSU? The program at OSU is really interesting to me since there appears to be a decent mix of quant and math psychologists (I'm interested in both) within the quant area. Do the different PIs / labs interact much with each other, or is each group mostly doing its own thing in isolation? What about the peer group within the quant area? And what is it like to live in Columbus as a grad student (as compared to something like cities on the coasts)? Once again a big thank you to everyone who contributed to this info-sharing!
  9. Dear all, Thank you for hosting this AMA session! I happened to chance upon the field of quant psych only in the past year and I find this part of psychology to be really interesting! I'm also hoping to apply to a quant psych program in the near future! I would greatly appreciate it if someone could answer some of queries below: 1. What is the level of mathematical rigour one should expect in quant psych? I've browsed through several articles in quant journals (SEM, MBR, Psychometrika) and the level of mathematics involved seems to vary wildly from article to article. Should I be expecting epsilon-delta proofs and asymptotics? Or is it more computational (e.g. extensive simulations) in nature? I'm pretty comfortable with simulation-based studies but I can't quite imagine spending 4-5 years solely on deriving asymptotic properties (which seems kind of divorced from psychology). 2. What is considered a decently strong math background? I'm currently doing a double in psych and stats but I kind of messed up a few math modules (B+ for calc, multivariable calc, real analysis I & III). Will that be a problem? To put things into perspective, I've scored decent grades (at least A-) in other math modules (ODE, Linear Algebra, Real Analysis II, Probability etc.) and all the stat modules. Also there's no grade inflation where I'm at (some school in Asia) but I guess most American professors won't be aware of that?. Is it even worth it (from an admissions point of view) to take more advanced (pure-ish?) math classes (like linear algebra II, functional analysis) to make up for those deficiencies? 3. One thing that really confused me is the apparent distinction between mathematical psychology and quantitative psychology. How exactly are they different? Or are there some overlaps? 4. Will previous experience in substantive research actually help when applying to quant psych programs? If yes, how much does it matter? I've spent quite a bit of time working on social decision-making before transitioning towards quant and I'm just wondering if its even relevant at this point. 5. Does the prestige of the PhD program matter when it comes to job prospects for faculty positions later on? I know that the specific advisor you get is probably more important but still... Also, how do you actually evaluate the "prestige" (whatever that means) of a quant psych program since there are so few of them? Its a little confusing since the conventional league tables for psych don't really apply to the quant subfield. 6. I know that some programs allow (or demand?) that their students take up a masters from the stats/biostats department. Is that usually the case or is it program-specific? Sorry for the long list of questions! Really appreciate being able to get some insight into quant psych through this platform!
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