'Large data' refers to situations where the number of observations (data points) is so large that it necessitates changes in the way the data analyst thinks about or conducts the analysis. (Not to be confused with 'high dimensionality'.)

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24 views

How to apply an R prediction model to very big data from SQL database in parallel.

I dont need to load the entire dataset into memory. In fact I only need 1 row at a time to apply a trained model, get the predicted response and put that response somewhere, possibly back into another ...
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0answers
32 views

Analyzing 30 million rows of data [closed]

I am trying to build a predictive model on 30 million rows of customer data to predict which product type they will buy. I've looked at and tried out the ff package ...
3
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1answer
28 views

Is an F-test for equality of variance appropriate for a very large dataset?

I have a dataset with about 500,000 subjects and I am trying to establish whether the variance is equal. I first performed an F-test but then I realised the data is slightly skewed with kurtosis. So ...
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3answers
108 views

Why do irrelevant regressors become statistically significant in large samples?

I am trying to better understand statistical significance, effect sizes and the like. I have a perception (perhaps its wrong) that even irrelevant regressors often become statistically significant in ...
2
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0answers
14 views

Propensity score matching with large data

I have a large healthcare claims database with 1.6 million subjects and I'm interested in doing a cohort study with propensity score matching. I have produced my propensity score with a logistic ...
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0answers
26 views

Enterprise Use of SAS & SPSS and Impact of Open Source Platforms (R & Python) [duplicate]

I'm curious to hear from people that use SAS & SPSS why a lot of the Fortune 100/500 enterprises still use SAS & SPSS despite the high cost of the license. Also would love to understand what ...
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1answer
51 views

Classification problem-Big Data and simple decision rules: logit regression, LDA, random forest, cond. trees, or something else?

This is a big data question from someone who is more accustomed to small data. I would like to develop some classification "rules of thumb," that is, some simple decision rules or a decision tree ...
3
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2answers
49 views

Collinearity in multivariate regression with huge amounts of data

Take the following example. I wish to predict physical performance as a function of height and weight. I already know weight negatively affects performance. Height also negatively affects performance, ...
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1answer
58 views

Large data variable selection

I'm looking for some methods of variable selection on large datasets.The number of variables are around 30-40, but the number of observations is quite large (around 36000000) Any methods which I ...
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0answers
19 views

Diference between glm and bigglm estimates

How does bigglm function in biglm package work for logistic regression? I thought that it is not possible to calculate LR on chunks of data and then merge results. Will glm and bigglm yield ...
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1answer
35 views

Compare two distributions of large sizes and unequal variances where one distribution is heavily skewed

My data is from cells that are treated under two different conditions and then their response to the condition is measured by one output variable. The cell populations in the two conditions are quite ...
3
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1answer
36 views

Is there any rule of thumb to delete a variable in a large data set?

I'm working with a large set as a project for the business analytic course with $10^5$ observations and 170+ variables, some of which come with a missing value proportion of larger than 20%, even more ...
3
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1answer
105 views

sample size too large? [duplicate]

I always thought larger sample sizes were better. Then I read something somewhere about how when sample sizes are larger, it's easier to find significant p-values when they're not really there (i.e., ...
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0answers
16 views

Joint significance testing w/ large samples

long time listener, first time caller. Unfortunately I can't show code as the computer the analysis is done on has rather tight security. I need to test for joint significance in a logit model that ...
3
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1answer
94 views

Robust estimates of the covariance matrix in the big data space

I am trying to compute the robust estimates of the covariance matrix (and also the mean) in the big data space. I am aware of FastMVE and FastMCD (Minimum Covariance Determinant and Minimum Volume ...
2
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0answers
62 views

How to fit OLS with many categorical levels, on more than one category

This question is not meant to be a software question, but I will illustrate the issue using R a bit. My Understanding of the Simple Case If I have a simple linear model with a categorical variable ...
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0answers
42 views

Quantifying the predictive ability of a model developed from a huge data set? (variation of bootstrapping?)

I have a statistical model with around 20 predictor variables, built on 90% of a dataset consisting of over 600k observations. The original developer held out 10% of the original dataset for the ...
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1answer
91 views

Wherefore Big Data?

It's been many years since I've done any statistics (or any serious math), but I do remember that the sampling error decreases more slowly for larger sample sizes (like n^-1/2, at least for some ...
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15 views

Difficulties of making inferences on large and high dimensional data

I've seen a couple of seemingly unrelated notes about working with large volumes of data and it struck me that I couldn't find much content on problems specific to statistical analysis of Big Data. ...
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0answers
98 views

Identifying multivariate outliers in a large sample with missing data, using SPSS

I'm a psychology PhD student doing analysis on a relatively large set of data, obtained via online surveys. The purpose of the study is largely to determine normative data for a population of adults, ...
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37 views

appropriate sample size to fit non-normal distribution parameters

I have a population that, in theory, I could determine exactly. I am not doing so, however, because on my poor home computer, it would be too computationally intensive. Each calculation takes 45-90 ...
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1answer
41 views

Big Data Regression Coefficient Estimation

I am working on a very large data set (n = 6.5 million) and I am trying to come up with a simple linear regression between two variables. I am working in R and using a monte carlo style simulation to ...
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1answer
69 views

Can binning a continuous predictor or DV variable improve large data sets fit?

I read that averaging and binning a continuous predictor variable is in general a bad idea because it's always better to fit the continuous relationship through splines, poly and all of that. Sure, I ...
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1answer
54 views

Is it possible to fit a logistic regression model to a dataset with categorical predictive variables with very high number of levels each?

I want to fit a model to a very large dataset, with a standard binary response variable and with 3 categorical predictor variables with 3000, 15 and 2 levels. Is there any inherent problem in this ...
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18 views

Linear-time approximation to kernel SVM?

Scaling kernel support vector machines to large datasets is a very challenging problem. For linear SVMs, PEGASOS is able to learn efficiently online, so training time scales linearly with the size of ...
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1answer
163 views

What are some interesting examples of wrong or crazy inferences being drawn from Big Data?

I'm interested in well-known examples of Big Data misinterpreted, poorly analysed, or wrongly employed to unscientific and incorrect ends. Would appreciate any examples or observations. Thanks a lot!
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2answers
104 views

Does the dataset size influence a machine learning algorithm?

So, imagine having access to sufficient data (millions of datapoints for training and testing) of sufficient quality. Please ignore concept drift for now and assume the data static and does not change ...
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76 views

Permutation for large sample sizes (and z-test)

I was trying to do a permutation test on a large amount of temperature observations in R. Approximately 1700 temperature observations (g1) from one place and 2 ...
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20 views

Big Data Use Cases

I am going to study about big data use cases in the real world. I want the stuff to be preferable in book format(not article or blog posts or website articles) and high level, industry based and ...
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2answers
68 views

How do you draw a random sample of unique IDs in a large dataset?

Rookie here -- I have a a large data set with about 75,000 observations, and 2000 unique IDs. Therefore, each unique ID has about 37 observations. I'm trying to draw a random sample of unique IDs, say ...
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59 views

Calculation of an overall top X over several groups

I'm not a statistics expert, so I'm hoping someone here can lend me a hand. I've got a bunch of key-value pairs associated with a specific time range. Something like this: ...
2
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1answer
117 views

Comparing large categorical data sets with low or zero counts

I'm dealing with a biological feature which can be classified into $2^{20}$ categories. I also have two pretty large data sets of 1 and 3 million entries. Actually, only around 30 thousand categories ...
4
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2answers
105 views

How to reduce the dimension of $10^8$ vectors

I have $10^8$ vectors in $1000$ dimensions each. I would like to drastically reduce their dimensions. However PCA seems computationally infeasible. Are there near linear time methods to do ...
1
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1answer
80 views

Best metric for evaluation of mixture-of-Gaussian clusters on big-data

I have made a new algorithm that is specifically crafted for clustering very large datasets. In order to document it as a research paper, I have to choose one or two internal (no-label) cluster ...
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14 views

Sybil detection metric

I have a set of user data and I want to build some kind of metric to evaluate the probability of the user being a sybil (a "fake" account). But I have a very limited set of users who are sybils with ...
2
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1answer
25 views

Need reasons or references on small p-values with large data sets

What are reasons or references for a statement such as "When you have a lot of data the statistical problem you run into is that even a tiny difference will be statistically significant." I see this ...
0
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1answer
83 views

Random forest ML algorithm suitable for use on cluster based HPC?

I have developed a script using pythons scipy package to analyse a rather large model that I wish to solve, the model contains over 12gb of data, including over 500 parameters. Now running small ...
1
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1answer
60 views

Testing for heteroscedasticity with many observations

I used the Breusch-Pagan test for heteroscedasticity, but I have many observations ($\approx 500,\! 000$) and the Breusch-Pagan test uses $nR^2$ as a test statistic where $n$ is the number of ...
0
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1answer
37 views

Literature for Cross Validation on Sparse Data?

I've read a lot about Cross Validation to estimate prediction error, specifically for selecting the number of components in a PCA model (I'm not doing SVD/PCA, but it's very similar), but I can't find ...
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0answers
97 views

regression algorithms which work with sparse categorical predictors

I am working with a very sparse problem with a large number of categories per feature and I am currently looking for existing machine learning regression algorithm implementations which can either ...
0
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1answer
40 views

Memory consumption in NLP tasks [closed]

I am trying to apply a simple Naive Bayes or SVM (libSVM) algorithms to a large data set, which I've constructed as an .arff file. The number of features in my set is ~180k and there are ~6k ...
0
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1answer
125 views

Can I subsample a large dataset at every MCMC iteration?

I have a large dataset from which I want to perform a bayesian probit regression using Gibbs sampling 1. Since the dataset has one milion rows, and variables from a truncated normal must be sampled ...
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0answers
32 views

Window models in stream data processing

Reading about data stream clustering I met the next terms: landmark window model, sliding window model, damped window. As to sliding window it's clear - oldest data escape the scope, the new data ...
2
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2answers
251 views

Fast missing data imputation in R for big data that is more sophisticated than simply imputing the means?

I need a package for missing data imputation in R. But since I am dealing with big data, the number of missing data entries can also be high. The packages which impute using mean or median are of ...
1
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1answer
222 views

Comparing two distributions

I have more than five hundred thousands samples of a continuous variable measured in two groups: a treatment and a control one. I would like to decide whether the measurements follow the same ...
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1answer
146 views

Clustering large movie dataset using k-medoids?

I have to cluster a movie dataset of 10000 movies. A movie has attributes like Genres, Actors, Directors, Year. Earlier I thought that we can use a simple clustering algorithm like k-medoids and the ...
2
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0answers
112 views

Is Big Data the end of Statistics? [closed]

Statistics emerged as a discipline whose goal was to allow inferences about inaccessible populations from data obtained from its samples. Does modern access to huge amounts of data depreciate the ...
4
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1answer
99 views

Dealing with Big Data and Lots of Variables

What is a good technique to use on data that has many categorical variables with many possible values? For example, let's say you are trying to determine what kind of people are more likely to ...
0
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2answers
186 views

Streaming k-means

I want to perform something like streaming/online/out-of-core kmeans clustering on large data. Here is simple idea: Break all data into N chunks. Read from disk 1st chunk and calculate centroids ...
3
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0answers
79 views

Big Data? Have we solved Small Data yet? [closed]

There has been a lot of attention on Big Data recently, where the problems are often more logistical (how to deal with large volumes of data) rather than statistical. At the other end of the spectrum ...