The process of censoring yields data w/ only partial information. The most common example of censoring is *right censoring* in survival analysis, where the time until the event occurred is only known to be longer than some duration because the event had not occurred when the study ended.

learn more… | top users | synonyms

1
vote
0answers
22 views

Heteroscedastic censored regression

I am dealing with a heteroscedastic censored dataset. I tried to use the survival analysis package in R to estimate a linear model for it. So before doing that, I conducted a simulation study, where I ...
0
votes
0answers
14 views

Some details in Cragg's Limited Dependent Variable article

1) Is there anybody who can explain me the precise meaning of the indices and hence, dimensions of the involved matrices of Cragg's equation (18) in his Limited Dependent Variables article from 1971? ...
0
votes
1answer
16 views

Cutoffs to consider for survival tree

In an tree based algorithm a criterion is measured at certain cutoffs for the variable. This cutoffs are the candidate split points for that variable. How does one come up with candidate split points ...
1
vote
1answer
27 views

Left-censoring , $Y_i=\text{max}(T_i,U_i)$

In the book , Statistical Models and Methods for Lifetime Data , in Left-censoring , it is written that Can only observe $Y_i=\text{max}(T_i,U_i)$ . Where , ...
1
vote
0answers
23 views

Censored logistic regression

I have the following problem: We have data with a 0-1 outcome which can occur precisely once. It can occur at any time within a certain time period (say 3 years). For this data set, for some ...
1
vote
0answers
33 views

Question on Tobit Regression

Does Tobit regression assume that the dependent variable is continuous above the lower bound? I am trying to model mortality (ie. "dead" or "living" but not the time) based on a set of independent ...
1
vote
1answer
68 views

Method for fitting censored data in R

I have 320 data points - each has a redshift and a turnover-frequency, and I want to fit a correlation between them (a linear fit). However, 120 of the turnover-frequency values are upper limits. As ...
1
vote
1answer
30 views

How do you compare two “survival times” when there is no censoring per se?

I've gone through the 70+ questions when using "survival no censoring" as my search criteria, but I can't seem to find an answer to this very simple situation. I have patients' length of stay in a ...
2
votes
1answer
58 views

Python Astronomy Censored Data in Lifelines

I am trying to find a correlation between a given data set containing redshifts and turnover frequencies (I have a list of 320 galaxies, and the redshift and turnover frequency (a turnover frequency ...
2
votes
1answer
48 views

Survival estimation when death/censoring is probabilistic

I am trying to estimate survival function, but in case where each event is censoring with probability $p_i$. (That is, I am never sure if the event is right-censoring or death, but I can estimate the ...
1
vote
0answers
23 views

The most general definition of the Likelihood function for continuous data (including truncation and censoring)

How would you rigorously define the likelihood function for censored/truncated observations? Even in most lifetime/reliability literature (where these types of observations are frequently encountered) ...
12
votes
2answers
1k views

What is the difference between censoring and truncation?

In the book Statistical Models and Methods for Lifetime Data , it is written : Censoring: When an observation is incomplete due to some random cause. Truncation: When the incomplete nature of ...
2
votes
2answers
134 views

Right censoring and Left censoring

Wikipedia gives the following definitions: Right censoring: a data point is above a certain value but it is unknown by how much. Left censoring: a data point is below a certain value but it is ...
0
votes
0answers
13 views

Imposing follow-up time cutoffs on censored data for cox regression

When fitting cox regression models, is it valid to impose a cutoff for censored samples to have been followed for at least a given stretch of time for inclusion in the analysis?
1
vote
1answer
47 views

What model fit / predictive accuracy measure can be used to cross validate a Cox PH model with censored data?

How would you go about validating a Cox PH model with censored data? I am trying to run a Cox PH model on a dataset with observations that failed, and observations that are censored. Normally, I use ...
0
votes
0answers
14 views

Correlation of scores, when most scores are the highest

Let's say we have 10,000 observations of two kinds of scores for each item, i.e. each observation has two scores reported. The scores are from 0 to 100. Within each kind of score 9,900 observations ...
5
votes
2answers
163 views

How should I model a continuous dependent variable in the $[0, \infty]$ range?

I have a dependent variable that can range from 0 to infinity, with 0s actually being correct observations. I understand censoring and Tobit models only apply when the actual value of $Y$ is partially ...
0
votes
0answers
10 views

Examples of random censoring where it is unknown if there was censoring or an event

I am wondering if there are any (standard) examples of random censoring where it is unknown if there was censoring or an event occurred. To be specific say the time to event is the random variable ...
2
votes
1answer
189 views

Determining sample size for proportional hazard

I am in the design phase for a longitudinal study examining the effect of a predictor (neighborhood risk score) on time to an outcome (re-arrest), as well as whether or not the variation explained by ...
0
votes
0answers
10 views

Multinomial Heckman Models

I'm interested in modeling left censored data with a tobit model using R. Mine is a two step, as I want to generally predict probablity, and then quantity, so I'm planning on using a heckman. The ...
0
votes
1answer
43 views

Parametric distribution for time to event data - where event is 'uncertain'

Is there a canonical approach to deal with the modeling of time to event ($A$) where $P(A) \ne 1$. For instance, assume the marriage rate is 50%. The study is a set of times (ages) until marriage ...
1
vote
1answer
42 views

Interpreting tobit coefficients of 0

I'm using a tobit model for a left censored dataset in R, and including both continuous and categorical predictor variables. I've converted the factors to indicator coding for each level. I was ...
2
votes
0answers
17 views

estimate the pdf from mutually censored (competing) observations

Imagine we have three independent random variables, distributed according to pdfs $a(t), b(t), c(t)$, with cdfs $A(t), B(t), C(t)$. In my case the distributions are lognormal. However, our ...
3
votes
0answers
19 views

Inferences about a distribution given running maximum values

Here is a question inspired by this question from StackOverflow. Suppose you have observations of a variable which is measured once a minute, but the values are only recorded if they are greater than ...
1
vote
1answer
39 views

Person-time still contributed after censorship? (Stata)

I feel like I might be going a little crazy here, so I'd appreciate some advice. I have a multiple-record-per-subject dataset that goes something like this: id | day | failure ----+------+-------- ...
2
votes
0answers
39 views

Clarify terminology regarding truncated and censored distributions [duplicate]

I'm looking for clarification on the definition of truncated distributions and on terminology for censored distributions and truncated distributions. I recently had a [dialog on SO][1] regarding [a ...
2
votes
1answer
78 views

What is “Targeted Maximum Likelihood Expectation”?

I'm trying to understand some papers by Mark van der Laan. He's a theoretical statistician at Berkeley working on problems overlap significantly with machine learning. One problem for me (besides ...
2
votes
1answer
230 views

ML estimate of exponential distribution (with censored data)

In Survival Analysis, you assume the survival time of a r.v. $X_i$ to be exponentially distributed. Considering now that I have $x_1,\dots,x_n$ "outcomes" of i.i.d r.v.'s $X_i$. Only some proportion ...
1
vote
1answer
18 views

Max fraction of censored values for Cox regression?

I'm not sure whether there's an exact answer to this, but I'm wondering in the simplest case of doing Cox Regression with censoring on one variable, if we have N measurement values, what number (k) of ...
0
votes
0answers
83 views

fitting a model for time series data

Folks, I am working on time series traffic data where the waiting times are indexed over time, with 288 observations for 24 hour time period (interval of 5 minutes). I am trying to cleanse the data, ...
2
votes
0answers
47 views

Evaluating survival models in the presence of covariate-dependent censoring

I have a censored survival analysis problem with the following characteristics: Failure times are discretized The censorship distribution depends on certain covariates I don't have a ...
0
votes
0answers
23 views

Incomplete inhibition curve fitting with Graphpad Prism6

One of my data set shows incomplete inhibition (for high concentrations points, inhibition response goes up to 45%-50% (of control). I am fitting my data using log(inhibitor) vs response -Variable ...
0
votes
0answers
101 views

Censored data from a truncated distribution (Stan)

I'm trying to write a survival model of fossil species durations. In this case, the minimal possible duration for a species is 1. Also, the general idea in paleontology is that we are only observing a ...
0
votes
0answers
24 views

Expectation maximisation for right-censored iid data from Normal

This is the data (which are length of ropes), $\textrm{Data}=\{99, 70, o ,89, 88, o, 88,70, o ,o\}$, where $o$ are censored data with value above $100$. Assume that data are from $\textrm{iid} \sim ...
4
votes
1answer
367 views

Tobit versus OLS

There is a dependent variable which is measured in £ and can take the form of £0-£100,000. It is effectively the value of the payment made. If it takes the form of £0 it means a payment was not made ...
9
votes
5answers
395 views

What exactly are censored data?

I have read different descriptions of censored data: A) As explained in this thread, unquantified data below or above a certain threshold is censored. Unquantified means data is above or below a ...
0
votes
0answers
49 views

Run Tobit with missing values (from linear prediction) or change them to zeros - STATA “practical” question

I want to run a Tobit linear regression in order to especify a labor-supply curve (linear-linear). As dependent variables I have personal characteristics, enviromental ones, and wages (predicted using ...
0
votes
0answers
56 views

Error using “cengaussian” family with MCMCglmm

I'm trying to run a model using a cengaussian family distribution with the function MCMCglmm. The model is: ...
1
vote
0answers
57 views

Using standard machine learning tools on left-censored data

I'm developing a forecasting application whose purpose is to allow an importer to forecast demand for its products from its customer network of distributors. Sales figures are a pretty good proxy for ...
2
votes
2answers
451 views

Mean value of truncated normal distribution

I have a bunch of data where each observation represents an error $\in [0,1]$ (computed error between a variable and it's ground truth). Extra info: These are the results of the difference between a ...
1
vote
0answers
80 views

Why are these MLE estimates biased?

I estimate the parameters of survival data with censoring which is simulated from Weibull distribution. The mean time to event was set to 10 by choosing the combinations of shape and scale ...
0
votes
0answers
47 views

Any reliability conclusion from many duplicate data points?

I'm trying to draw reliability conclusion about a material under a tensile test. All of my data max out the sensor. The spec is 8 lbf and I'm maxing out a 50 lbf load cell. "Get a bigger sensor" ...
1
vote
1answer
187 views

LASSO or other regularized regression with censored (missing) data

Here is my problem. I am looking at various time series curves. Let's call them total spend aggregated over all customers on various products versus time. At any given time, I want to predict the ...
0
votes
2answers
363 views

High Censoring Rate in Survival Analysis; Much higher survival time among censored patients

I am trying to understand censoring in survival analysis and wondering about how to tell when standard use of censoring breaks down. In one case, the number of censored patients is fairly high (low ...
5
votes
2answers
134 views

How to cope with missing data in logistic regression?

I'm investigating optimal bidding in auctions, and am using logistic regression to predict the probability of winning an auction given a set of explanatory variables (e.g. the price I bid, number of ...
1
vote
0answers
32 views

which regression model: nested, censored data

I have the following data structures and would like to know your opinion which regression models are most suitable (and available in R) model 1: - 20,000 cases - nested in 500 spatial units (only ...
2
votes
0answers
266 views

Random Effects Tobit Model

I'm studying the effect of various criminal case and court district characteristics on sentence lengths. I was planning on running xttobit in Stata because I have individual defendants/cases within ...
2
votes
0answers
52 views

Censored data when censoring time is uneven

I have a data set relating to credit defaults. The data set contains around 100 predictors (some categorical and some numeric). I am interested in predicting the time to default, if the person ever ...
3
votes
1answer
59 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 ...
8
votes
3answers
220 views

If $Z_i =\min \{k_i, X_i\}$, $X_i \sim U[a_i, b_i]$, what is the distribution of $\sum_iZ_i$?

Assume the following set up: Let $Z_i = \min\{k_i, X_i\}, i=1,...,n$. Also $X_i \sim U[a_i, b_i], \; a_i, b_i >0$. Moreover $k_i = ca_i + (1-c)b_i,\;\; 0<c<1$ i.e. $k_i$ is a convex ...