Given a set of bivariate data (x, y), to impute a value of y corresponding to some value of x at which there is no measurement of y is called interpolation, if the value of x is within the range of the measured values of x.

learn more… | top users | synonyms

0
votes
0answers
4 views

Observed versus Synthetic data

I am looking for studies that compare different spatial interpolation methods for observed data. However I am looking for studies that have also compared observed with generated synthetic data. For ...
1
vote
0answers
20 views

Interpolation of time series data

I am working on using cubic spline interpolation in time series data. I used Galdolfo and Prachowney algorithms. Now how do I obtain estimates of models of cubic spline?
0
votes
0answers
17 views

Minimum points required for fitting curve

If I need to build yield curve, n what is the minimum number of data points necessary for any interpolation method, e.g. say cubic spline or Nelson–Siegel, etc. I ma not sure if I can raise this ...
0
votes
1answer
28 views

How to take into consideration gaps in time series?

I've been analysing what is the probability of that measurement going up or down during a week (e.g. 4 times out of 7, I have 60% chances of my measurement going up) everyday for the last 100 days, ...
1
vote
0answers
23 views

Limitations of using interpolated data

I have a data set that is composed of point locations in a landscape, lets call this dataset X. Some of the points in data set X need to be grouped together because they "function" together as a ...
0
votes
0answers
22 views

Missing data interpolation

I have two time series at different time intervals. So, for 60 seconds, I have 1000 values for X and 50 for Y. All measurements are evenly spaced over the interval. I wish to "fill in" the blanks ...
3
votes
1answer
131 views

Find out if there is a way to create BMI weight category variable

I am working on a project and have to find a way to break my data into percentiles. The variables that I already have are gender, age in months and BMI. If I am able to calculate the percentile that ...
1
vote
0answers
42 views

How to select a radial basis function?

Currently I am investigating interpolation of 3D data with radial basis functions (RBF) and I am wondering that there are quite a few families of such (see table1 here). However, I cannot find any ...
1
vote
1answer
67 views

Explanation of cubic spline interpolation

Can someone explain to me what a cubic spline is, and how we could use it to interpolate a function? I have searched on the internet but I would like a simple explanation.
3
votes
1answer
101 views

Kriging without covariance?

I am trying to krige monthly snowfall totals using data from weather stations and elevation. When I use a linear variogram model (set using a GUI and appears to be a good fit), the resulting layer ...
1
vote
0answers
51 views

Interpolation problem [closed]

We have the following solar meteorology parameters through satellite data with resolution of 100 km$^2$ (10 km x 10 km). Insolation on horizontal surface Diffuse radiation on horizontal ...
1
vote
1answer
55 views

How to algorithmically determine the best order of fit?

I am doing a least squares polynomial interpolation for 10,000 data sets that look mostly like one period of a sine curve, but whose values are not evenly spaced in the time domain, and can sometimes ...
1
vote
0answers
38 views

Dimension independent regression/interpolation methods?

Hopefully this question is not too simple or too general. I am working on a problem right now in which I am given different sets of data. Each data set consists of some number of samples (sampled at ...
0
votes
0answers
56 views

Appropriate algorithms for interpolating 2D plane

I have a dataset describing a signal on a 2D plane. The data can be spaced at arbitrary increments. If it was an ordered grid, I understand bicubic interpolation would be a good choice. In the ...
4
votes
0answers
232 views

Uncertainty propagation in linear interpolation

How do I calculate the uncertainties in linearly interpolated values from a given tabulated function? I am just coming back into the fold after a bit of a hiatus, and am having trouble ...
2
votes
1answer
40 views

Interpolation of spectra: uneven sampling to even sampling

I have a spectrum. Specifically, my data is relative intensity $[I_{\tilde{\nu}}]$ versus wavenumbers $[\tilde{\nu}]$. The wavenumbers are equally sampled so that ${d\tilde{\nu}} = c$, where $c$ is ...
13
votes
1answer
1k views

How do I find values not given in (/interpolate in) statistical tables?

Often people use programs to obtain p-values, but sometimes - for whatever reason - it may be necessary to obtain a critical value from a set of tables. Given a statistical table with a limited ...
6
votes
1answer
345 views

Fourier/trigonometric interpolation

Background In a paper from Epstein (1991): On obtaining daily climatological values from monthly means, the formulation and an algorithm for calculating Fourier interpolation for periodical and ...
7
votes
1answer
137 views

Stationarity - assumptions and examination

I am examining rodent captures on six permanent rodent trapping grids measuring 150 x 150 meters and consisting of 121 trap stations evenly spaced 15 meters apart. There are six such trapping grids ...
9
votes
4answers
271 views

Interpolation of influenza data that conserves weekly mean

Edit I have found a paper describing exactly the procedure I need. The only difference is that the paper interpolates monthly mean data to daily, while preserving the monthly means. I have trouble to ...
0
votes
0answers
56 views

Performing linear regression analysis on two data series with different sample spacing

I have a record of one climate variable with a data point every year, and another one which has sample spacing that varies between 1.3 and 75.2 years. I even have a few ages in that series for which I ...
0
votes
2answers
132 views

2d interpolation method for coarsely sampled image

I'm looking for a general method for 2d interpolation of a coarsely sampled image. I'll use an example, taken from the scipy.interpolate (Python) page. Say, I have this image, but instead of ...
1
vote
1answer
101 views

Backfilling ARIMA data with exogenous variable

I have time series data for a set of cities that goes back for about 10 years. I also have the data at the state level for almost 30 years. There was an event that occurred about 20 years ago, that is ...
0
votes
0answers
86 views

Should we always do interpolation polynomially or fill the gaps with the average value?

I have a series which takes values as 1,2 and 3. It also has some ...
1
vote
0answers
50 views

Confusion related to kriging

I was going through the wiki article related to kriging http://en.wikipedia.org/wiki/Kriging. However, I couldn't follow some derivations. In the first figure for simple kriging, how come the ...
3
votes
2answers
526 views

Kriging on log transformed rainfall data

I am beginner in R. I had found in the literature that prior to performing kriging on the data, the distribution has to be investigated to check if it is Gaussian. So, in order to check if the data ...
5
votes
1answer
150 views

Spatial interpolation models: deterministic vs statistical

I am applying diferent methods to interpolate continuous spatial surfaces (kriging, splines, glm,etc). Most of the studies that have enough detail for me to follow usually focus on one specific ...
4
votes
1answer
672 views

Non-algebric curve-fitting along weighted pointcloud (if possible using python)

I have a list of weighted 2D points taken from symmetry analysis of a human back surface. I am supposed to find the "midline" representing the most likely path describing vertebrae location (actually, ...
1
vote
0answers
196 views

Sparse matrix representation of a spline interpolation

I use spline interpolation within a statistical model, and the transpose of the operator turns up in the gradient of the log-likelihood. Let me set up some notation first. If $x_1 \ldots x_n$ are a ...
4
votes
0answers
121 views

Spatial interpolation of vectors in vector fields

Statistical modeling is new to me and I would appreciate some thoughts on my project. I am trying to model the spatial (and possibly temporal as well) relationships within vectors in vector fields. ...
3
votes
1answer
208 views

How to display concentration data in space?

I have a data frame of chemical concentrations that are measurements taken from 12 locations all in the same vicinity. I have manually assigned x and y coordinates to each location (on a 0 to 20 ...
1
vote
1answer
66 views

Sampling an interpolated model with MCMC

Is it safe to run a MCMC by interpolating in tabulated data of a model? For background, I have output of a model that involves a set of coupled non-linear differential equations. Calculating models ...
2
votes
1answer
144 views

Density estimation with scaled sinc-like kernels

Given data points $x_i$ in $\mathbb{R}^d$ with function values $f_i$, one can estimate the function at a given $x$ by $\ \ \ \ \text{f}_{est}( x ) = \frac {\sum { w_i f_i }} {\sum { w_i }}$ with $w_i ...
2
votes
1answer
216 views

Interpolation of missing values using results produced by arima

I would like to know if anyone knows how to apply the arima results to calculate missing values in the observation period. I am looking for something similar to ...
1
vote
0answers
178 views

Correlate bivariate Brownian bridges

Given two independently constructed Brownian bridges (from their marginal means and variances), is there a way to correlate the sample paths?
1
vote
4answers
1k views

How is interpolation related to the concept of regression?

Explain briefly What is meant by interpolation.How is it related to the concept of regression? interpolation is art of reading between the lines of a table and in elementary mathematics the term ...
1
vote
0answers
106 views

Derivation of equations in kriging

I have some confusion regarding some derivations in the equation of kriging of wiki article http://en.wikipedia.org/wiki/Kriging. It says that kriging error is given by $$ ...
7
votes
1answer
233 views

Using ARMA when data is missing

I am using ARMA over a dataset with missing samples. How do I treat them? Would you suggest to make linear/nonlinear interpolation or just keep them out and consider two samples with missing data in ...
6
votes
2answers
291 views

Confusion regarding kriging

I was reading this wikipedia article related to kriging . I didn't understand the part when it says that Kriging computes the best linear unbiased estimator, $\hat Z (x_0)$, of $Z(x_0)$ such that ...
3
votes
2answers
139 views

Coefficient estimated with a binary predictor $\in \{0,1\}$, but making predictions with values between $0$ and $1$ - is this OK?

Let's say I have a variable $x_d$ that, in the estimation data, is a simple indicator ($x_d \in \left\{0,1\right\}$). I estimate a coefficient for it, $\beta_d$, along with several other coefficients ...
1
vote
1answer
244 views

Ordinary kriging stationary case

I am trying to understand ordinary kriging. Say I have 3 elevation measurements: Z1, Z2, and Z3 taken at X positions: X1, X2 and X3. I am also assuming some semivariogram: g(h) and that the process ...
3
votes
1answer
184 views

Interpolation in multivariate time series

I have a problem in multivariate time series. The data consist of three time series related to foreign trade. Although my client is still doing research and attempting to find monthly data for all ...
1
vote
2answers
224 views

Adjusting data for missing observations

I have an unbalanced panel data set of 40 cities and 20 years. It is unbalanced because the data are not collected for certain cities for every year. The data are then balanced after these 20 years. ...
0
votes
0answers
322 views

Similarities between different size matrices, rescaling problem

Given a series of matrices {$M_i$($m_i\times n_i$),i=1...k,$m_j,n_j \in$random} if we rescale (resize) all matrices into a ...
0
votes
1answer
402 views

Correlated brownian interpolation

I would like to generate conditional correlated random variables. I have a correlation matrix between normal variables, and these variables are modeled through SDEs. What are the algorithms to ...
2
votes
0answers
50 views

Consistent ranked list for ROC interpolation

For classifiers with binary outputs, their performance is summarized by a true positive rate and false positive rate. To interpolate the performance between two classifiers $A$ and $B$ with their ...
1
vote
0answers
112 views

Co-efficient of correlation weighted method for spatial interpolation

Teegavarapu and Chandramouli (2005) has mentioned Coefficient of correlation method for spatial interpolation of moisture data that calculates the coefficients between a point and its neighbors and ...
0
votes
1answer
86 views

Interpolating between models in ROC space

Suppose I have two models $A$ an $B$ that predict class labels. If these give binary predictions, these will appear as pairs of (false positive rate, true positive rate) in the ROC space. We should be ...
1
vote
0answers
95 views

How to determine the number of nearby samples for spatial estimation?

In many applications e.g, in mining engineering when we need to generate a map of dispersion of an element (e.g., copper) over the field of study, to depict depletion and concentration regions we have ...
8
votes
1answer
460 views

What is the statistical justification of interpolation?

Suppose that we have two points (the following figure: black circles) and we want to find a value for a third point between them (cross). Indeed we are going to estimate it based on our experimental ...