Creating samples from a well-specified population using a probabilistic method and/or producing random numbers from a specified distribution.
4
votes
2answers
64 views
Understanding bootstrap method for confidence interval of correlation coefficients
Please correct me where I'm wrong:
My understanding of bootstrapping is that it is a way to estimate the distribution of some statistic (mean, standard error, Pearson's correlation coeff, etc), ...
0
votes
2answers
33 views
Finding the distribution when the observations are dependent
How do we find information about the distribution of a variable in presence of dependency among our observations? This dependency is coming from measuring the variable on the same group of subjects ...
2
votes
2answers
70 views
Estimating values of a sequence from observed differences
I have a sequence of random variables $S_1, S_2 \dots S_N$ that is guaranteed to satisfy
$$S_1 + S_2 + \cdots + S_N = 0$$
I can't observe any of these random variables directly, however I can ...
0
votes
1answer
24 views
Sampling from a database
I am conducting a research study about breastfeedding. I receive a whole database of subjects which are suitable to my research. I have had to call each individual to ask for their approval to ...
0
votes
1answer
48 views
How to add confidence intervals to describe the variability in monthly samples of data?
The data analyzed here is a sample of individuals collected on a monthly basis. What would be the best way to compute "confidence intervals" for the monthly sample means, in order to indicate the ...
1
vote
1answer
52 views
Ordinary Least Squares method: why are my regression results insignificant?
I have a problem in my thesis results of OLS regression being insignificant.
I have 3 sectors and each sector has 130 observations.
Is this sample size is sufficient or not ?
Can anyone suggest ...
2
votes
0answers
52 views
How does predictive model for the Eurovision Song Contest work?
I've encountered interesting prediction of Eurovision Song Contest http://mewo2.com/nerdery/2013/05/12/eurovision-2013-first-predictions/ it based on some kind of Bayesian model I assume but I don't ...
4
votes
0answers
36 views
Estimating repeat shoppers from an incomplete sampling
I'm trying to estimate how many people visited the farmers market once, twice, thrice, etc. in a given time period, using sampled data. We have interview data from approximately 50% of visitors as ...
0
votes
0answers
16 views
Getting the degree of overlap between each pair of classes
In a dataset containing many classes, is there any way to get some values which indicate how much the data-points of two different classes $y_i$ and $y_j$ are overlapped ?
0
votes
0answers
22 views
Random Sampling [closed]
A college statistics class has 14 women and 11 men. If the professor selects a random sample of n= 4 students, what is the probability that 2 men and 2 women will be selected? What is the probability ...
2
votes
1answer
23 views
How to determine the sample size of a Latin Hypercube sampling?
I am designing an experiment with 5 variables. Some of the variables have 2 and others have 3 levels. The method that I am going to use is the Latin Hypercube, but I do not not what the sample size ...
1
vote
0answers
12 views
How to handle samples with many replicates for population estimation,when sample size is small?
I have a sample with very small sample size ($n<10$) and I would like to estimate the population mean. However, some of the samples are replicates (for instance, sampled at the same location).
...
3
votes
1answer
61 views
Compute sum of vectors drawn from multivariate normal, subject to a linear constraint
I want to compute $S = \sum_{i=1}^n x_i$ where $w^t x_i>-1, \; \forall i$ and $x_i \tilde{} \mathcal{N}(\mu, \Sigma)$ for known $w$, $\mu$ and $\Sigma$.
I know $S$ can be approximated by sampling ...
1
vote
0answers
9 views
How to create subscale scores for 4 subscales of the REI using SPSS? [migrated]
I need to create subscale scores for 4 subscales of the REI: REI_Appear; REI_Hlth; REI_Mood; REI_Enjoy. The items comprising each subscale are as follows:
Appearance (9 items): 1, 5, 9, 13, 16, 17, ...
1
vote
1answer
17 views
Weighted sampling from objects with costs
I have a set of models which I have fit using nonlinear regression to some data, and hence for each has an associated squared error, or cost. I want to sample an models in a manner which favours ...
0
votes
0answers
35 views
R Quadratic programming and constrained optimization problem [closed]
I want find a vector p which lies in the positive null space of a matrix S and as well minimize the value of a least squares function with respect to p. Constraints are thus that p>0 and S*p=0
Here is ...
0
votes
0answers
22 views
Might be a bit basic, but I need some help (samples)
If I was interested in observing a variable during different and sometimes very specific periods of the day (like the varying shift pattern, for person X over time)…
Am I correct in my thinking below? ...
2
votes
1answer
69 views
The sample size applied to a non-normal distribution
I have a single variable that represents my population values (sample of data):
...
2
votes
1answer
89 views
Most efficient way of sampling a product of distributions $k \cdot f(x) \cdot g(x)$, given that PDFs $f(x)$ and $g(x)$ can easily sampled?
Is there an efficient approach tailored to sampling from a PDF $k \cdot f(x) \cdot g(x)$ (where $k$ is a normalizing constant) that would perform better than naive Metropolis, slice sampling, etc.? ...
7
votes
1answer
164 views
Given that one can sample $X \sim f(x)$, is there an easy way to sample $Y \sim k \cdot f(g(y))$ (such as $k \cdot f(e^y)$)?
Say I'm able to sample an RV $X$ from a PDF $f(x)$, can I exploit this to efficiently sample another RV $Y \sim k \cdot f(g(y))$ (where $k$ is a normalizing constant)?
I'm interested in something ...
0
votes
1answer
55 views
Determining butterfly weight distribution
I'm a researcher studying a specific butterfly species. I've conducted an experiment where I measure the weight of this species at different locations on the earth. The data is cateogorical and ...
0
votes
1answer
55 views
How do I sample multiple normally distributed items and maintain a fixed sum?
Suppose jars are being filled with Red, Green and Blue balls in an approximately fixed ratio. The total number of balls that fits in a jar is fixed (say N=1000). I know the mean and standard deviation ...
2
votes
1answer
50 views
What are the implications of sampling to attain a given distribution in one variable?
Some while ago I attended a presentation of a case-control study for which enrolment was still in progress.
The lecturer, a PhD student with basic statistical education, mentioned that while in the ...
3
votes
3answers
141 views
Difference between Randomization test and Permutation test
In the literature the terms Randomization and Permutation are used interchangeably. With many authors stating "Permutation (aka randomization) tests", or vice versa.
At best I believe the difference ...
1
vote
0answers
40 views
Price elasticity of specialized goods
What type of "declared-intention" survey would be adequate to measure the price elasticity (among the general public) of a product, such as a car battery, for which the following applies:
The only ...
7
votes
4answers
217 views
How can I sample from a distribution with incomputable CDF?
Semi-computer science simulation related problem here.
I have a distribution where
P(x) = $\frac{(e^b-1) e^{b (n-x)}}{e^{b n+b}-1}$
for some constants b and n, and x is an integer such that $0\leq ...
0
votes
0answers
13 views
Discrete Fourier Transform and uneven sampling
In this blog article
an example is given of one can use the DFT to detect frequencies much higher than the sample rate.
In the comments sections I asked how it was done, since DFT normally requires ...
0
votes
0answers
12 views
How would you approach weighting of open response lists in a statistical manner?
How would you deal with lists of varying lengths? For example, if you asked for an ordered list of your favourite movies without any constraints how would you value the weight of someone who submits ...
3
votes
2answers
154 views
Cluster Big Data in R and Is Sampling Relevant?
I'm new to data science and have a problem finding clusters in a data set with 200,000 rows and 50 columns in R.
Since the data have both numeric and nominal variables, methods like K-means which ...
0
votes
0answers
28 views
Sample changing distribution
I have the following process:
I have N buckets in front of me, $M$ of which are filled with water (the other ones are empty).
I pick one of them (consider uniform distribution) and empty it (so ...
0
votes
0answers
112 views
Why does the mean of the bootstrapped distribution not equal the original summary stat?
I have n samples and their average. There's some correlation so I used a moving block bootstrap to get an empirical distribution of the mean. The mean of this empirical bootstrapped distribution seems ...
0
votes
0answers
32 views
Can I improve data accuracy by taking many smaller samples? [closed]
Please excuse my lack of stats knowledge - it's been a while since I took Stats101.
Currently at my workplace we take a single 250g sample from a bin of product to analyse product quality. This ...
1
vote
0answers
9 views
Using known correlations to select a sample that maximizes model prediction
I want to model the outcome of a binary dependent variable D over a large universe [many records, 100s of variables].
Given a fixed sample size, I want to maximize the accuracy of the model over the ...
1
vote
1answer
62 views
Sampling from conditional distribution in general case
I'm dealing with Gibbs Sampling now.
Let's consider the example:
I know the distribution of X|Y and the distribution
of Y. They are some known - Binomial or Beta or other
but particular. Thus I have ...
1
vote
1answer
75 views
What is behind JAGS (Just Another Gibbs Sampler)?
I have been using JAGS but I am not quite sure how it actually simulates it values. I need to know in a general sense what's going on in the background.
Thanks for the help
0
votes
0answers
16 views
Probability calculation for a ranked set sampling design
This question is an extension of my previous question that was very nicely answered by whuber. But now some new restrictions have been imposed and I am trying to figure out the form of the ...
3
votes
2answers
267 views
Is a sample covariance matrix always symmetric and positive definite?
When computing the covariance matrix of a sample, is one then guaranteed to get a symmetric and positive-definite matrix?
Currently my problem has a sample of 4600 observation vectors and 24 ...
0
votes
1answer
49 views
What is the Cauchy meta distribution?
I overhead a professor speak about the Cauchy meta-distribution, but I am unable to find anything about it on the web. My question is what is the Cauchy meta distribution and what is the theory behind ...
0
votes
1answer
57 views
Sampling from the conditional distribution assuming sampling from the joint
I am struggling with this question, which I thought it should be easy: suppose we have a method of sampling from the joint distribution of a collection of (discrete ordinal) random variables. We do ...
1
vote
1answer
50 views
Sampling small dataset from large dataset with reference to a given variable
Here is a statistics question which I have been thinking about while working with some of my data. I have a large dataset named "bigbird" (say about a billion rows) and I want to randomly sample a ...
0
votes
1answer
107 views
R: Density estimation vs Histogram Estimation
To draw random samples from a custom distribution, I recall reading that KDE's are better than histograms. (See hadley's comment here.)
When I experimented in R, I am finding that the KDE method ...
2
votes
4answers
95 views
Strangely imbalanced dataset
I'm new to Machine Learning and this forum. I have a beginner's doubt regarding imbalanced dataset. Here it goes:
I have a binary classification task, where I'm more interested in accurately ...
1
vote
0answers
31 views
Sampling to maximize model accuracy
Suppose you have a relatively small random sample and have a corresponding model
$\ Y$ ~ $\operatorname{Bernoulli}(p_i) $
$\ \operatorname{logit}( \hat{p_i} )=\hat{\beta}*X$ and now want to draw a ...
0
votes
0answers
7 views
hypergeometric sampling [duplicate]
Hypergeometric Sampling involves lot=n, sample=s, defectives in lot=d, defectives found in sample=c. And the question is what is the Probability p of getting exactly 5 defectives from a lot of 50000 ...
0
votes
0answers
36 views
How to pick a sample from a multiple group, stratified population for repeat surveys
I provide support to about 500 internal customers on a computer system. I want to start measuring my 'customers' satisfaction on some kind of routine schedule. However my customer base can be ...
0
votes
0answers
37 views
Comment on this method of sampling from any distribution?
Say I have a distribution, either described by a probability density function that is integrable and continuous, or by a set of discrete probabilities over a finite set of symbols.
I want to ...
0
votes
0answers
19 views
Dealing with variance within a closed set
A naive question perhaps, but one I need an answer to.
I am developing the functional requirements of a machine that needs to accept and process a number of incoming items daily. Any model for ...
1
vote
1answer
97 views
Generation of a random vector on an affine hyperplane
I would like to design a proposal of the form:
$$
p(t=(t_i)|\hat{t}=(\hat{t}_i))
$$
where $t$ (and $\hat{t}$) lies in an affine hyperplane $T \subset R^n$:
$$
t \in T \Leftrightarrow
\sum_i t_i=1
...
0
votes
0answers
214 views
2 samples, t test with multiple dependent variables SPSS
I am attempting to compare two samples in SPSS. I have taken a sample of 350 from group 1 and 350 from group 2. Now, I want to compare these two groups that are identified by a filter variable ...
1
vote
1answer
62 views
clustered-stratified random sampling
i've got questions about clustered-stratified random sampling.
let's say I want to do a research in narcotics.
the population that I will be working with is a combination of academics, students, and ...
