#55 in Science & math books
Use arrows to jump to the previous/next product

Reddit mentions of Statistical Inference

Sentiment score: 13
Reddit mentions: 29

We found 29 Reddit mentions of Statistical Inference. Here are the top ones.

Statistical Inference
Buying options
View on Amazon.com
or
    Features:
  • Used Book in Good Condition
Specs:
Height9.25 Inches
Length6.5 Inches
Number of items1
Weight2.2928075248 Pounds
Width1.25 Inches

idea-bulb Interested in what Redditors like? Check out our Shuffle feature

Shuffle: random products popular on Reddit

Found 29 comments on Statistical Inference:

u/statmama · 9 pointsr/statistics

Seconding /u/khanable_ -- most of statistical theory is built on matrix algebra, especially regression. Entry-level textbooks usually use simulations to explain concepts because it's really the only way to get around assuming your audience knows linear algebra.

My Ph.D. program uses Casella and Berger as the main text for all intro classes. It's incredibly thorough, beginning with probability and providing rigorous proofs throughout, but you would need to be comfortable with linear algebra and at least the basic principles of real analysis. That said, this is THE book that I refer to whenever I have a question about statistical theory-- it's always on my desk.

u/COOLSerdash · 9 pointsr/statistics
u/[deleted] · 6 pointsr/statistics

If you have your three calculus courses out of the way, might I suggest a hearty round with Casella and Berger

u/ffualo · 5 pointsr/askscience

For mathematical statistics: Statistical Inference.

Bioinformatics and Statistics: Statistical Methods in Bioinformatics.

R: R in a Nutshell.

Edit: The Elements of Statistical Learning (free PDF!!)

ESL is a great book, but it can get very difficult very quickly. You'll need a solid background in linear algebra to understand it. I find it delightfully more statistical than most machine learning books. And the effort in terms of examples and graphics is unparalleled.

u/CrazyStatistician · 4 pointsr/statistics

Casella and Berger is a fairly standard text for first-year graduate Math Stats courses. It's not the most detailed or exhaustive text on the topic, but it covers the main points and is fairly accessible.

u/RAPhisher · 4 pointsr/statistics

In addition to linear regression, do you need a reference for future use/other topics? Casella/Berger is a good one.

For linear regression, I really enjoyed A Modern Approach to Regression with R.

u/placemirror · 4 pointsr/statistics

Try the two:

https://www.amazon.com/Introduction-Mathematical-Statistics-Robert-Hogg/dp/0321795431

https://www.amazon.com/Statistical-Inference-George-Casella/dp/0534243126

introduction to mathematical statistics by craig and statistical inference by george casella.

u/CodeNameSly · 3 pointsr/statistics

Casella and Berger is one of the go-to references. It is at the advanced undergraduate/first year graduate student level. It's more classical statistics than data science, though.

Good statistical texts for data science are Introduction to Statistical Learning and the more advanced Elements of Statistical Learning. Both of these have free pdfs available.

u/trijazzguy · 3 pointsr/statistics

See Casella and Berger chapter 2, theorem 2.1.5

u/animalcrossing · 3 pointsr/cscareerquestions

You received A's in your math classes at a major public university, so I think you're in pretty good shape. That being said, have you done proof-based math? That may help tremendously in giving intuition because with proofs, you are giving rigor to all the logic/theorems/ formulas, etc that you've seen in your previous math classes.

Statistics will become very important in machine learning. So, a proof-based statistics book, that has been frequently recommended by /r/math and /r/statistics is Statistical Inference by Casella & Berger: https://www.amazon.com/Statistical-Inference-George-Casella/dp/0534243126

I've never read it myself, but skimming through some of the beginning chapters, it seems pretty solid. That being said, you should have an intro to proof-course if you haven't had that. A good book for starting proofs is How to Prove It: https://www.amazon.com/How-Prove-Structured-Approach-2nd/dp/0521675995

u/BayesianPirate · 3 pointsr/AskStatistics

Beginner Resources: These are fantastic places to start for true beginners.

Introduction to Probability is an oldie but a goodie. This is a basic book about probability that is suited for the absolute beginner. Its written in an older style of english, but other than that it is a great place to start.

Bayes Rule is a really simple, really basic book that shows only the most basic ideas of bayesian stats. If you are completely unfamiliar with stats but have a basic understanding of probability, this book is pretty good.

A Modern Approach to Regression with R is a great first resource for someone who understands a little about probability but wants to learn more about the details of data analysis.

​

Advanced resources: These are comprehensive, quality, and what I used for a stats MS.

Statistical Inference by Casella and Berger (2nd ed) is a classic text on maximum likelihood, probability, sufficiency, large sample properties, etc. Its what I used for all of my graduate probability and inference classes. Its not really beginner friendly and sometimes goes into too much detail, but its a really high quality resource.

Bayesian Data Analysis (3rd ed) is a really nice resource/reference for bayesian analysis. It isn't a "cuddle up by a fire" type of book since it is really detailed, but almost any topic in bayesian analysis will be there. Although its not needed, a good grasp on topics in the first book will greatly enhance the reading experience.

u/PandaHuggers · 3 pointsr/AskStatistics

This is a classic. I took a grad level course with this textbook and every problem is nasty. But yes, it is really a classic.

Also, I just begun Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman and Jennifer Hill. Love his interpretation of linear regression. Linear regression might sound like basics, but it lays the foundation work for everything else and from time to time I feel compelled to review it. This book gave me a new way to look at a familiar topic.

If you are familiar with any statistical programming language/packages, I would highly suggest you implement the learnings from any books you have.

u/jmcq · 3 pointsr/statistics

I was an Actuary (so I took the Financial Engineering exams) before I went back to get my PhD in Statistics. If you're familiar with:

  • Real Analysis (limits, convergence, continuity etc)
  • Basic Probability (Random variables, discrete vs. continuous, expectation, variance)
  • Multivariate Calculus

    You should be fine in a PhD stats program. It's easy enough to learn the statistics but harder to learn the math (specifically you're going to want strong analysis and calculus skills).

    Check out Statistical Inference - Casella & Berger it's a pretty standard 1st year theory text in Statistics, flip through the book and see how challenging the material looks to you. If it seems reasonable (don't expect to know it -- this is stuff you're going to learn!) then you ought to be fine.
u/complexsystems · 3 pointsr/econometrics

The important part of this question is what do you know? By saying you're looking to learn "a little more about econometrics," does that imply you've already taken an undergraduate economics course? I'll take this as a given if you've found /r/econometrics. So this is a bit of a look into what a first year PhD section of econometrics looks like.

My 1st year PhD track has used
-Casella & Berger for probability theory, understanding data generating processes, basic MLE, etc.

-Greene and Hayashi for Cross Sectional analysis.

-Enders and Hamilton for Time Series analysis.

These offer a more mathematical treatment of topics taught in say, Stock & Watson, or Woodridge's Introductory Econometrics. C&B will focus more on probability theory without bogging you down in measure theory, which will give you a working knowledge of probability theory required for 99% of applied problems. Hayashi or Greene will mostly cover what you see in an undergraduate class (especially Greene, which is a go to reference). Hayashi focuses a bit more on general method of moments, but I find its exposition better than Greene. And I honestly haven't looked at Enders or Hamilton yet, but they will cover forecasting, auto-regressive moving average problems, and how to solve them with econometrics.

It might also be useful to download and practice with either R, a statistical programming language, or Python with the numpy library. Python is a very general programming language that's easy to work with, and numpy turns it into a powerful mathematical and statistical work horse similar to Matlab.

u/mrdevlar · 2 pointsr/statistics

Berger and Casella's Statistical Inference is what you need if you want a mathematical approach.

u/Econonerd · 2 pointsr/GradSchool

This book has a fairly good introduction to probability theory if you don't need it to be measure theoretic. Statistical Inference

u/flight_club · 2 pointsr/math

What is your background?

http://www.amazon.com/Statistical-Inference-George-Casella/dp/0534243126
Is a fairly standard first year grad textbook with I quite enjoy. Gives you a mathematical statistics foundation.

http://www.amazon.com/All-Statistics-Concise-Statistical-Inference/dp/1441923225/ref=sr_1_1?ie=UTF8&s=books&qid=1278495200&sr=1-1
I've heard recommended as an approachable overview.

http://www.amazon.com/Modern-Applied-Statistics-W-Venables/dp/1441930086/ref=sr_1_1?ie=UTF8&s=books&qid=1278495315&sr=1-1
Is a standard 'advanced' applied statistics textbook.

http://www.amazon.com/Weighing-Odds-Course-Probability-Statistics/dp/052100618X
Is non-standard but as a mathematician turned probabilist turned statistician I really enjoyed it.

http://www.amazon.com/Statistical-Models-Practice-David-Freedman/dp/0521743850/ref=pd_sim_b_1
Is a book which covers classical statistical models. There's an emphasis on checking model assumptions and seeing what happens when they fail.

u/efrique · 2 pointsr/statistics

Maybe Casella and Berger? If you can get hold of a copy, take a look. If that's more theoretical than you were looking for to start we might be able to suggest something else.

u/sovietcableguy · 2 pointsr/learnmath

I learned from Wackerly which is decent, though I think Devore's presentation is better, but not as deep. Both have plenty of exercises to work with.

Casella and Berger is the modern classic, which is pretty much standard in most graduate stats programs, and I've heard good things about Stat Labs, which uses hands-on projects to illuminate the topics.

u/determinot · 1 pointr/math

Since you're an applied math PhD, maybe the following are good. They are not applied though.

This is the book for first year statistics grad students at OSU.
http://www.amazon.com/Statistical-Inference-George-Casella/dp/0534243126/ref=sr_1_1?ie=UTF8&qid=1368662972&sr=8-1&keywords=casella+berger

But, I like Hogg/Craig much more.
http://www.amazon.com/Introduction-Mathematical-Statistics-7th-Edition/dp/0321795431/ref=pd_sim_b_2

I believe each can be found in international editions, and for download on the interwebs.

u/lrnz13 · 1 pointr/statistics

I’m finishing up my stats degree this summer. For math, I took 5 courses: single variable calculus , multi variable calculus, and linear algebra.

My stat courses are divided into three blocks.

First block, intro to probability, mathematical stats, and linear models.

Second block, computational stats with R, computation & optimization with R, and Monte Carlo Methods.

Third block, intro to regression analysis, design and analysis of experiments, and regression and data mining.

And two electives of my choice: survey sampling & statistical models in finance.

Here’s a book for intro to probability. There’s also lectures available on YouTube: search MIT intro to probability.

For a first course in calculus search on YouTube: UCLA Math 31A. You should also search for Berkeley’s calculus lectures; the professor is so good. Here’s the calc book I used.

For linear algebra, search MIT linear algebra. Here’s the book.


The probability book I listed covers two courses in probability. You’ll also want to check out this book.

If you want to go deeper into stats, for example, measure theory, you’re going to have to take real analysis & a more advanced course on linear algebra.

u/shaggorama · 1 pointr/statistics

Start here:

  • http://en.wikipedia.org/wiki/Likelihood_function
  • http://en.wikipedia.org/wiki/Bayes_factor
  • http://en.wikipedia.org/wiki/Loss_function#Decision_rules
  • http://en.wikipedia.org/wiki/Risk_function

    If you have access to a decent academic library, I'd recommend reading skimming the chapters on hypothesis testing in Casella & Berger or Hogg, Mckean & Craig. I believe HMC also discusses risk and loss functions in the context of bayesian statistics, although I'm not sure of C&B does. Unfortunately I don't have any good recommendations for you specific to bayesian hypothesis testing, my bayesian experienced only lightly touched on hypothesis testing.

    Very, very generally, the idea is that you want to determine how likely each hypothesis is given your data. By taking a ratio of these probabilities, you can determine proportionally which how likely each hypothesis is relative to the other one, given the data. If you set up your ratio (here called a bayes factor) as p(H0|X)/P(H1|X), then a BF close to or larger than '1' indicates you can't reject the null hypothesis, since it is more likely than the alternative. As the BF approaches 0, your confidence in a choice to reject the null hypothesis increases, since this indicates that the denominator is large and therefore the alternative hypothesis is more likely given your data.

    The question then becomes how do you establish the cutoff value: exactly how small does the BF need to be to definitively reject the null hypothesis? This is where the risk functions come in. The risk functions help you to decide on a decision function, which is really just a point estimator. You will use this result to determine the cutoff value for rejecting the null hypothesis and compare this value to your calculated BF, which here serves as the test statistic.
u/whyilaugh · 1 pointr/math

We use Casella and Berger. It glosses over the measure theory somewhat but it appropriately develops the concept of "a probability". If you haven't had much background in proper math stats, then this is a good place to start (even if you've done the more applied courses).

u/El-Dopa · 1 pointr/statistics

If you are looking for something very calculus-based, this is the book I am familiar with that is most grounded in that. Though, you will need some serious probability knowledge, as well.

If you are looking for something somewhat less theoretical but still mathematical, I have to suggest my favorite. Statistics by William L. Hays is great. Look at the top couple of reviews on Amazon; they characterize it well. (And yes, the price is heavy for both books.... I think that is the cost of admission for such things. However, considering the comparable cost of much more vapid texts, it might be worth springing for it.)

u/gabbriel · 1 pointr/math

Maybe "too applied", depending on your fields, but there's always Casella and Berger, especially if you're in Economics.

u/mathwanker · 1 pointr/math

For probability I'd recommend Introduction to Probability Theory by Hoel, Port & Stone. It has the best explanations of any probability book I've seen, great examples, and answers to most of the problems are in the back (making it well-suited for self-study). I think it's still the best introductory book on the subject, despite its age. Amazon has used copies for cheap.

For statistics, you have to be more precise as to what you mean by an "average undergraduate statistics" course. There's a difference between the typical "elementary statistics" course and the typical "mathematical statistics" course. The former requires no calculus, but goes into more detail about various statistical procedures and tests for practical uses, while the latter requires calculus and deals more with theory than practice. Learning both wouldn't be a bad idea. For elementary stats there are lots of badly written books, but there is one jewel: Statistics by Freedman, Pisani & Purves. For mathematical statistics, Introduction to Mathematical Statistics by Hogg & Craig is decent, though a bit dry. I don't think that Statistical Inference by Casella & Berger is really any better. Those are the two most-used textbooks on the subject.

u/Sarcuss · 1 pointr/statistics

Hrmh, given your background I guess I would go with a suggestion of Wasserman for Statistical Inference or Casella and Berger which isn't really applied. If those are too much for you (which I doubt with your background), there is also Wackerly's Mathematical Statistics with Applications :)

u/cherise605 · 1 pointr/AskStatistics

Since you are still in college, why not take a statistics class? Perhaps it can count as an elective for your major. You might also want to consider a statistics minor if you really enjoy it. If these are not options, then how about asking the professor if you can sit in on the lectures?

It sounds like you will be able to grasp programming in R, may I suggest trying out SAS? This book by Ron Cody is a good introduction to statistics with SAS programming examples. It does not emphasize theory though. For theory, I would recommend Casella & Berger, many consider this book to be a foundation for statisticians and is usually taught at a grad level.

Good luck!

u/DS11012017 · 1 pointr/AskStatistics

I will second this. I used this book for my year of undergrad foundations of probably and stats.
I also really like Casella and Berger's 'Statistical Inference.'

https://www.amazon.com/Statistical-Inference-George-Casella/dp/0534243126https://www.amazon.com/Statistical-Inference-George-Casella/dp/0534243126