What to assume if exact lower limit data values are not available I have some data - about 100 values, maximum value 100.
However, values below 10 are just written as <10, exact values for these are not available. 
How can I impute these data points. What value should I assume these data points to have? Should I take 5 (midway between 0 and 10)? If we assume normal distribution for whole data, average of these points would be around 7 (considering tail of normal distribution curve).
Thanks for your insight.
Edit: re discussion in comments:

For a perfect triangle with base between 0 and x, mean seems to be 0.633 times x (on checking data on spreadsheet), though I do not have mathematical proof.
 A: For some simple approaches with censored data, see USEPA, 2000, Guidance for Data Quality Assessment: Practical Methods for Data Analysis,EPA/600/R-96/084. Section 4.7. Note that this document was last revised 20 years ago.
They recommend that if less than 15% of the data is censored †, that a value of half the censored limit can be used ‡.  For larger percentages, they recommend trimmed means or categorizing data and using a test of proportions.  Among other approaches.
Also, I think it's worthwhile to think about what kind of data you have and what went into the censoring process.  This may inform the approach you take.
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† Here, they're considering environmental data, which I presume assumes the data are non-negative, left-censored, and the censoring is done at the detection limit of the chemical/physical analysis.
‡ It worries me some that your limit of censoring (10) isn't that many orders of magnitude away from the maximum observed value (100).  Often, say with water quality data, you might have, say, a detection limit of 0.1 and a maximum of 10.  This at least gives you a couple orders of magnitude between the detection limit and the maximum of the data.  Because of this, it will often make no practical difference if the actual observed value was 0, 0.05, or 0.1.  For your data, I wonder if the difference between 0, 5, and 10 would make a difference in interpreting data.
