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I'm sure this has been asked on this site before so I suggest you search a bit more thoroughly. Here are some helpful questions that are related: Probability theory books for self-study Introduction to statistics for mathematicians Standard reference for classical mathematical statistics? Introduction to applied probability for pure mathematicians? ...

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I'm a fan of these YouTube videos by Nando de Freitas: http://www.youtube.com/user/ProfNandoDF/videos

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The Udacity course Introduction to Hadoop and MapReduce might be a good place to start. Addition in response to question edit: Maybe the textbook Mining of Massive Datasets would be useful.

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Probability and statistics are essential. Some keywords are hypothesis test, multivariate normal distribution, Bayesian inference (joint probability, conditional probability), mean, variance, covariance, Kullback-Leibler divergence, ... Basic linear algebra is essential for machine learning. Topics that you could learn are Eigen decomposition and singular ...

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As far as brushing very very basic math skills, i'm using these books: Elements of Mathematics for Economics and Finance. Mavron, Vassilis C., Phillips, Timothy N This books covers essential math skills (addition substraction), to partial differentiation, integration, matrix and determinants, and a small chapter on optimization, and also differential ...

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I don't know if mathy enough (but it should have the right references to get you started): Gelman, A. (2005). Analysis of Variance: Why It Is More Important than Ever. The Annals of Statistics, 33(1), 1–31. doi:10.2307/3448650 Should be available from Andrew Gelman's website.

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Try: Linear Models by Shayle Searle

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I second the Wikipedia recommendation. Standard warnings about Wikipedia apply, but I find it much more readable for self-teaching than any book. Usually I look up whatever probability distribution I'm concerned with at the moment. Everything I learned about power laws I learned from reading Wikipedia, then following up the academic references given there on ...

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I usually refer to NIST Engineering Statistics Handbook for the common ones (http://www.itl.nist.gov/div898/handbook/eda/section3/eda366.htm) and they recommend the following two books for detailed treatment of the subject: Johnson, Kotz, and Balakrishnan, (1994), Continuous Univariate Distributions, Volumes I and II, 2nd. Ed., John Wiley and Sons. Evans, ...

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First build models. (1) Decide on the statistical package you're interested in using. (2) Choose a standard practical textbook using this statistical package. (Stata: http://www.amazon.com/Introduction-Time-Series-using-Stata/dp/1597181323/ref=sr_1_1?ie=UTF8&qid=1386268375&sr=8-1&keywords=stata+time+series) Then deepen your understanding ...

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What, in my opinion, you need to do is first study difference equations, then basic statistics and after that you can approach the elementary time series models, e.g. ARMA, GARCH and so on. Math and Stats are a prerequisites I am afraid. A good math book for econ students is "Fundamental Methods of Mathematical Economics" by Chiang, Wainright. I am sure ...

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If you are fitting a multivariate regression tree (MRT), then you need method = "mrt" - i.e. if you are using a matrix for the response, you can't use method = "class". If you just have a vector response, then if this is a factor and you use method = "class", then mvpart is doing nothing different to rpart i.e. the usual thing for a classification tree. If ...

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The fact that you are using 4 out of 14 parameters implied that you used significance testing to select the variables. This is invalid. There are a number of other problems: Your total sample size is far too small for data splitting to be a reliable method You are seeking arbitrary cutoffs You are not using a proper accuracy score such as deviance, ...

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Lots could be said here. gung had the best most concise answer. But to add... (1) If the cutoff I use is 50%, a max of 25% of the defects get detected is this how you want to define the quality of your model? You can detect all the defects if you change your cutoff. (2) split sample is suboptimal. (3) prediction is hard (4) classically the first approach is ...

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One really simple introductory statistics book is Andy Fields "Discovering Statistics using R" - also available for SPSS. It contains a lot of nice examples and is even fun to read. Less precise, though compared to other books, but with very little mathematical formulations and lots of text. I found it easy for a basic start, and am still using it from time ...

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Regarding mixed-effects models, in my opinion, the best applied book is: Fitzmaurice, G.M., Laird, N.M., & Ware J.H. (2011). Applied Longitudinal Analysis. Wiley. For more on fitting them with different software, West, B.T., Welch, K.B., & Galecki, A.T. (2006). Linear Mixed Models: A Practical Guide Using Statistical Software. Chapman ...

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Yes. The first chapter talks mostly about support vector machines and hyperplane classifiers(among other things). So, yes, they have discussed a frequentist approach. They do discuss bayesian methods in chapter 16 and give a good explanation for the connection between regularized loss minimization and bayesian methods.

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A recent paper called "a biterm topic model for short text" (WWW13) has made some progress on this topic, and here is its code

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The Elements of Statistical Learning. Free online. http://statweb.stanford.edu/~tibs/ElemStatLearn/

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I would recommend P.J. Green and B.W. Silverman - "Nonparametric regression and generalised linear models - a roughness penalty approach", CHapman & Hall/CRC Monographs on statistics and applied probability, volume 58.

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genetic algorithms were used to lower the prime gap to 4680 in the recent Zhang twin primes proof breakthrough & associated polymath project. the bound has been lowered by other methods but it shows some potential for machine-learning approaches in this or related areas. they can be used to devise/optimize effective "combs" or basically sieves for ...

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I guess I would like to read or at least browse in that too, but only a polymath or a committee could write it, and the polymath isn't evident and committee books often don't work well. Also, many of the general books on statistics that tend to pop up from (e.g.) searches on Amazon just leave out most of the interesting technical details and/or are written ...

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In a paired t-test, each observation is of the form: $Z = X - Y$. That means that the variance of each observation is $$Var(Z) = Var(X-Y) = Var(X) + Var(Y) -2Cov(X,Y)$$ Now, suppose that X,Y and are positively related (for instance before and after scores on a test). The paired t-test will then reduce the $Var(Z)$ by the $-2Cov(X,Y)$ term. Thus in the ...

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Fortuitous timing, as Bayesian Data Analysis, 3rd ed was just released. It's a good general-purpose text, with an emphasis on hierarchical methods, a section on advanced computation (that is, Markov chain Monte Carlo), and an appendix on Gelman's Bayesian inference tool, rstan. The text focuses on statistics rather than programming, though, so perhaps ...

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Peter D. Huff. A First Course in Bayesian Statistical Methods. Springer (2010) Also Andrew Gelman et. al. Bayesian Data Analysis (3rd ed.). CRC (2013) The Gelman book isn't constrained to R but also uses Stan, a probabilistic programming language similar to BUGS or JAGS. I believe earlier editions of the book used BUGS instead of Stan, which is ...

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I am simply copy-pasting the answers I got from Alexandre Passos on Metaoptimize. It would really help if someone here can add more to it. Any binary classifier can be used for multiclass with the 1-vs-all reduction, or the all-vs-all reduction. This list seems to cover most of the common multiclass algorithms. Logistic regression and SVMs are ...

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I recommend that you be explicit about all elements of the plot. Explain how the boxplot indicates the median (mean?), quartiles (quantiles?), and extreme values (distant quantiles?)... assuming that's what you're plotting. I suggest you be explicit here not only for clarity, but also because the general boxplot template can be used to display different ...

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