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
1answer
21 views

Should an interpolation coincide the original function on the given data points?

Suppose having a model $f(x)=y$ where $f$ is unkown. Moreover, suppose you have some data points for this model i.e. $(x_1,y_1), (x_2,y_2), \dots , (x_n,y_n)$. If one can find an approximate of $f $ ...
0
votes
0answers
4 views

fractional desing and interpolation

Factorial design is an experimental design which help in determining the effects of the factors on the response. I would wonder whether there is a relation between factorial design (used for some ...
0
votes
0answers
19 views

Difference between imputation and interpolation?

When dealing with data sets that have missing values, imputation replaces missing values with substituted values while interpolation replaces missing values with calculated values within some range. ...
0
votes
0answers
32 views

Pearson correlation with missing values

I am trying to correlate dendrochronological data with climate data. The first one is acquired directly from trees, the second one from various stations from around the world. According to the formula ...
0
votes
0answers
6 views

Instances where certain Interpolation Methods are suitable

When comparing interpolation methods such as Chebyshef Nodes, Splines, Kriging - are there instances (instances with fewer measurements, smaller space between measurements) where certain techniques ...
0
votes
0answers
13 views

Using known and complete data to predict data part of which is know and the rest is unknown

It is a general problem. I have a training set(size > 1000). Each data point has 1000 features. What I want to do is to use these training data to complete a data point which has 600 known features ...
0
votes
0answers
9 views

Basis for fitting a complete monotone

I have set of measured data: x=[x1,x2,....,xn] y=[y1,y2,....,yn] It is a multi component data. However I need to fit for only one component which is known to ...
2
votes
0answers
28 views

Interpolation methods: splines, kriging, IDW (Inverse Distance Weighting)

In general, do any one of these methods tend to out perform the others? I have been reading a paper in which it mentions that kriging tends to out perform splines but splines would never outperform ...
2
votes
0answers
25 views

Interpolating missing time-series data

I have time-series for creatinine levels in patients, which has missing samples, due to patients' irregular visits to doctors. The figure below represents the time-series for a patient. Task: I ...
1
vote
0answers
37 views

Strictly increasing interpolation / spline

I have points in the x-y-plane that are strictly increasing most of the time. The problem is that there are cases with one or two outliers (Knots where an out-of-the-box spline would be decreasing). ...
1
vote
1answer
45 views

can random forest project/interpolate based on new values of X?

Sometimes I want a model to predict what would happen when presented with values of predictor variables that it has not seen before. For example, say, I have predictor variables (X) that go from 1 ...
0
votes
0answers
25 views

Is there a recursive version of Kriging or Inverse Distance spatial interpolation?

Classic use-case of Kriging: you have a 2d space, you have $n$ observations, each of them representing an exploratory dig. It has a $x$ and $y$ coordinate, and a $V$ representing the value discovered ...
0
votes
1answer
35 views

How to interpolate for the normal distribution (or any other distribution)? [duplicate]

I have that X is normally distributed with mean 4 and variance 9 and I need to calculate the probability: Pr(X>9). So far, I have Pr(X>9)=Pr(Z>5/3)=1-Pr(Z<5/3). Now, the value for Pr(Z<5/3) ...
0
votes
1answer
20 views

Weighted Minkowski RBF kernel

The radial basis function (RBF) kernel is given by $$K_{\text{RBF}}(\mathbf{x}, \mathbf{y})=\exp[-\gamma\|\mathbf{x}-\mathbf{y}\|^2_2]$$ where $\|\mathbf{x}-\mathbf{y}\|^2_2$ is the squared ...
2
votes
0answers
51 views

Help understanding a kriging variation for bare earth extraction

Problem: The authors of a paper (http://www.isprs.org/proceedings/XXXIV/part3/papers/paper106.pdf) develop a bare earth extraction algorithm for LiDAR that is based on kriging. What I don't ...
0
votes
0answers
40 views

“R” computing derivative of multivariate spline

I am trying to compute the derivative of a multivariate spline, in fact bi-variate I use a b-spline univariate to create a basis, for the first x1 and second variable x2, then I use the tensor product ...
0
votes
0answers
15 views

Inversely proportional version of a nearest-neighbour results vector - how?

Short version: Given an input vector D of n values, what are the different methods that one can use to return a vector W such that each value in W is in inverse proportion to the magnitudes of the ...
2
votes
0answers
36 views

Interpolating population data

I'm currently doing an internship where I have to calculate incidence ratios for townships for a period of 11 years. I don't have access to the population data for all years and I would like to do ...
1
vote
0answers
89 views

What is the correct way to interpolate error?

If I have a 2-D data, say $y = f(x)$, with error in the dependent variable, $\delta y$ in this case, and I want to interpolate this data set to a coarser independent variable grid, $x$, what is the ...
0
votes
0answers
35 views

gaussian process - missing data

One approach to deal with missing data is be to define a joint gaussian distribution / Gaussian process, and then define the (conditional) distribution of the unknown values on the known values. (e.g. ...
1
vote
1answer
352 views

Cubic splines for interpolation through four points in R

I am attempting to write R code for cubic splines to connect points on a graph. Specifically, I am attempting to reproduce Figure 3.3 of Wood (2006) ...
1
vote
0answers
112 views

How can we use genetic algorithm for curve fitting?

I want to use genetic algorithm in order to fit a curve to some data, or in other words, to estimate some equation that describes the relationship. Suppose that I select the equation to be a ...
0
votes
1answer
41 views

Interpolation model to estimate missing analytics

We have about 7 months of partially (30%) missing web analytics, that is apparently missing at random across all segmentations. We need to estimate the missing data to correctly compare current and ...
4
votes
1answer
207 views

How Does Kriging Interpolation work?

I am working on a problem in which I need to use Kriging to predict the value of some variables based on some surrounding variables. I want to implement its code by myself. So, I've went through too ...
0
votes
0answers
23 views

Interpreting margins of dummy variables [duplicate]

I have estimated an ordinal probit model in Stata. The dependent variable is walkability. The main independent variables are on a Likert scale (1=agree, 2=partially agree, 3=disagree). The other ...
1
vote
2answers
76 views

Which interpolation technique should I use?

I have an annual data set, but I have a few missing values in the series. I do not know which interpolation technique should I use to fill the missing values. ...
6
votes
2answers
66 views

Extrapolation of 2d movement

I have a problem with missing data in my dataset. My dataset is timeseries which contains x,y coordinates. I'd like to extrapolate missing values and use the assumption that I know speed and direction ...
0
votes
0answers
33 views

What issues may I face when interpolating my dependent variable in an OLS regression?

I'm doing my undergrad dissertation on what host-country factors impact FDI inflows - FDI inflows to the UK is my dependent variable. All of the independent variables I have managed to find at a ...
0
votes
0answers
24 views

creating polynomials using sets of data to represent a correlation

I need some guidance in creating a polynomial function that represents sets of data and its correlation, if that makes any sense. I know there Lagrange interpolation, least squares etc. I don't know ...
3
votes
0answers
25 views

Partition of function into pieces for interpolation needs

I've got some experimental data obtained from my mate's research. There are two sets of (x,y) points for each curve. He asked me to interpolate function values between these points, so for each curve ...
0
votes
0answers
81 views

Trend line for “discontinuous” data (missing data points)

How do I draw a trend line for data with missing points? There should be a measurement for each day, but sometimes the user forgets to take it. Here’s some data and my current approach: The data ...
0
votes
1answer
738 views

Normalize time series with different lengths with linear interpolation in R

I have a large set of time series (100k, each 3 observations), their lengths varies about 10% on average. Each of them cover the time interval of the same lengths but varies due to rate of sampling, ...
0
votes
0answers
17 views

Interpolating singular values

So I have the singular values associated with a data matrix and I would like to interpolate them and then find the maximum curvature of the interpolation in order to decide how many singular values to ...
0
votes
0answers
22 views

How to investigate higher frequency oscillation from lower frequency observations?

I have a set of data, e.g. chilled water flow rate, from a sensor reading, which record the value every half an hour. However, it yields irregular high-low pattern which bothers me quite much. Until ...
1
vote
0answers
68 views

Sampling subset to span entire range of full set (*not* to be representative), in order to construct some sort of lookup table

I have a large number of $N$ (20770) measurements. I need to perfom a calculation on all of them, but this is computationally too expensive. Therefore, I am looking for a way to select a subset of $p$ ...
1
vote
0answers
70 views

How to combine probabilities, weighting by strength of evidence?

Let's say that I'm trying to classify an item as a member of either class c1, class c2, class c3, etc. out of a large number of classes. In my training set, feature A appears only once; it happens to ...
1
vote
0answers
37 views

Is smooth.spline appropriate for presenting the trend of chemical concentration in a catchment [closed]

In my case,the Electrical Conductivity (EC) values are measured daily in some pints of catchment from 2007 to 2014, and around 20% data are missed, due to without probe or flux. To presenting the ...
0
votes
2answers
265 views

How to interpolate a variable with frequency of 5 years to annual data?

I have two time-series variables: each has 14 points with an interval of 5 years. The precise years are: ...
0
votes
0answers
106 views

What is the difference between Bézier splines and Loess curves?

I'm a bit naive on this topic, and wanted to understand the difference in the mechanics of Bézier splines and Loess curves as curve-fitting methods.
5
votes
1answer
117 views

In inverse theory, how do I transform the averaging kernel matrix to a new grid?

Rodgers and Connor (2003) describe how measurements by remote sounders can be properly compared, taking into account differences in averaging kernels and error covariances. They make the assumption ...
1
vote
2answers
140 views

Interpolating time series

what are best ways to interpolate time series? I have three data points(1980, 1990 and 2001) and I need to interpolate them. Using R na.approx doesn't seem to be what I need since the data I need to ...
1
vote
0answers
25 views

Predicting the missing data out of three values in each of the two vectors [duplicate]

I have 2 vectors of rural and urban populations of the same country. (years from 1975 to 2020) with only three values (1980, 1990 and 2001 years) in each. And I need to predict the missing data. My ...
4
votes
1answer
349 views

How to interpolate (spline, LOESS, etc.) with a mix of critical and non-critical points

The data we are interpolating is monotonically increasing (for example, a car's odometer reading). We have two types of points we'd like the solution to interpolate through. The solution surface must ...
5
votes
1answer
389 views

What is the difference between (universal) kriging and spatial autoregressive models?

As part of a course on missing observations in social/survey statistics I am trying to explore existing methods of predicting either point pattern or polygon data. I got quite confused by all the ...
0
votes
1answer
49 views

Multivariate interpolation: Getting Started

I have a large set of data points received by experimentation and each point has n ,or let's say 8 in this case, independent variables and one output/dependent variable. (x1, x2, x3, ...., xn) = y ...
2
votes
0answers
212 views

SUTSE DLM on daily mean water & air temperature TS

I have two time series: (1) daily mean water temperature from 1988 to 2014 and (2) daily mean air temperature from 1968 to 2010. The water temperature time series has missing data, occurring on ...
2
votes
0answers
261 views

Interpolating time series wind data using correlation

Is there a way to use correlation to interpolate missing data? I know the wind speed in 6 locations every hour. This shows the correlation between the separate locations. Hourly Data I know the wind ...
3
votes
1answer
92 views

How can I estimate the tail of a distribution with a truncated distribution?

The broadband speed data I'm working with have all values over 30Mbps placed into a >30 category. The distribution is thus truncated. This leads to the final column in the histogram below being a ...
1
vote
0answers
172 views

How to enforce periodic boundary conditions when performing regression with sci-kit learn?

I have signals that resemble sine waves (or, more accurately, sums of sines). The data are normalized in the time domain so that there is only one cycle and all of the points lie between 0 and 1. An ...
1
vote
1answer
263 views

Denormalizing Data

I am applying Polynomial Regression to my data, however the parameters theta were always =0, i noticed that my y data or output is too large in the order of 100000 so i normalized y, i got very good ...