Category

bigdata

Exposure to Stan has changed my defaults: a non-haiku

By | ai, bigdata, machinelearning | No Comments

Now when I look at my old R code, it looks really weird because there are no semicolons
Each line of code just looks incomplete
As if I were writing my sentences like this
Whassup with that, huh
Also can I please no longer do <-
I much prefer =
Please

The post Exposure to Stan has changed my defaults: a non-haiku appeared first on Statistical Modeling, Causal Inference, and Social Science.


Source link

When Size Matters: Weighted Effect Coding

By | ai, bigdata, machinelearning | No Comments

Categorical variables in regression models are often included by dummy variables. In R, this is done with factor variables with treatment coding. Typically, the difference and significance of each category are tested against a preselected reference category. We present a useful alternative.

If all categories have (roughly) the same number of observations, you can also test all categories against the grand mean using effect (ANOVA) coding. In observational studies, however, the number of observations per category typically varies. Our new paper shows how categories of a factor variable can be tested against the sample mean. Although the paper has been online for some time now (and this post is an update to an earlier post some time age), we are happy to announce that our paper has now officially been published a the International Journal of Public Health.

To apply the procedures introduced in these papers, called weighted effect coding, procedures are made available for R, SPSS, and Stata. For R, we created the ‘wec’ package which can be installed by typing:

install.packages(“wec”)

References

Grotenhuis, M., Ben Pelzer, Eisinga, R., Nieuwenhuis, R., Schmidt-Catran, A., & Konig, R. (2017). When size matters: advantages of weighted effect coding in observational studies. International Journal of Public Health, (62), 163–167. http://doi.org/10.1007/s00038-016-0901-1

Sweeney R, Ulveling EF (1972) A transformation for simplifying the interpretation of coefficients of binary variables in regression analysis. Am Stat 26:30–32




Source link

Black Knight: Mortgage Delinquencies Declined in January

By | ai, bigdata, machinelearning | No Comments


From Black Knight: Black Knight Financial Services’ First Look at January Mortgage Data: Impact of Rising Rates Felt as Prepayments Decline by 30 Percent in January

• Prepayment speeds (historically a good indicator of refinance activity) declined by 30 percent in January to the lowest level since February 2016

• Delinquencies improved by 3.9 percent from December and were down 17 percent from January 2016

• Foreclosure starts rose 18 percent for the month; January’s 70,400 starts were the most since March 2016

• 2.6 million borrowers are behind on mortgage payments, the lowest number since August 2006, immediately following the pre-crisis national peak in home prices

According to Black Knight’s First Look report for January, the percent of loans delinquent decreased 3.9% in January compared to December, and declined 16.6% year-over-year.

The percent of loans in the foreclosure process declined 0.5% in January and were down 27.6% over the last year.

Black Knight reported the U.S. mortgage delinquency rate (loans 30 or more days past due, but not in foreclosure) was 4.25% in January, down from 4.42% in December.

The percent of loans in the foreclosure process declined in January to 0.94%.

The number of delinquent properties, but not in foreclosure, is down 413,000 properties year-over-year, and the number of properties in the foreclosure process is down 178,000 properties year-over-year.

Black Knight: Percent Loans Delinquent and in Foreclosure Process
Jan
2017
Dec
2016
Jan
2016
Jan
2015
Delinquent 4.25% 4.42% 5.09% 5.48%
In Foreclosure 0.94% 0.95% 1.30% 1.76%
Number of properties:
Number of properties that are delinquent, but not in foreclosure: 2,162,000 2,248,000 2,575,000 2,764,000
Number of properties in foreclosure pre-sale inventory: 481,000 483,000 659,000 885,000
Total Properties 2,643,000 2,731,000 3,234,000 3,649,000




Source link

He wants to know what book to read to learn statistics

By | ai, bigdata, machinelearning | No Comments

(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

Tim Gilmour writes:

I’m an early 40s guy in Los Angeles, and I’m sort of sending myself back to school, specifically in statistics — not taking classes, just working through things on my own. Though I haven’t really used math much since undergrad, a number of my personal interests (primarily epistemology) would be much better served by a good knowledge of statistics.

I was wondering if you could recommend a solid, undergrad level intro to statistics book? While I’ve seen tons of options on the net, I don’t really have the experiential basis to choose among them effectively.

My reply: Rather than reading an intro stat book, I suggest you read a book in some area of interest to you that uses statistics. For example, Bob Carpenter is always recommending Jim Albert’s book on baseball. But if you’re interested in epidemiology, then maybe best to read a book on that subject. Sander Greenland wrote an epidemiology textbook; I haven’t read it all the way through, but Sander knows what he’s talking about, so it could be a good place to start.

If you had to read one statistics book right now, I’d suggest my book with Jennifer Hill. It’s not quite an intro book but we pretty much start from scratch.

Readers might have other suggestions.

The post He wants to know what book to read to learn statistics appeared first on Statistical Modeling, Causal Inference, and Social Science.

Please comment on the article here: Statistical Modeling, Causal Inference, and Social Science

The post He wants to know what book to read to learn statistics appeared first on All About Statistics.




Source link