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JMP Formula Error While Creating Bootstrap‑Simulated Column

I am working with a dataset that contains many missing values and a small number of observations, which makes modelling difficult. To address this, I would like to use bootstrap resampling. One column in the dataset contains the values shown in the attached Excel file, and I want to replace this column with simulated data consisting of 10,000 observations (Column Name: Sim_Y1_Liability). I used the following two formulas to generate the simulated values, but JMP returns an error when I enter them. Could anyone advise what might be causing the error and how to correct it?

JMP Formula 1:

Pick Random(

0,

2239500,

929880,

2192100,

2806020,

0,

3387600,

109080,

1057860,

441840,

880950,

1753830,

0,

968070,

1990950,

295410,

1083420,

0,

235770,

616500,

0,

279120,

0,

591090,

35100

)

 

JMP Formula 2:

 

Random Sample(

{0, 2239500, 929880, 2192100, 2806020, 0, 3387600, 109080, 1057860, 441840,

880950, 1753830, 0, 968070, 1990950, 295410, 1083420, 0, 235770, 616500,

0, 279120, 0, 591090, 35100}

)[1]

 

 

 

Data Blending and CleanupRe: JMP Formula Error While Creating Bootstrap‑Simulated Column

Neither of those functions exist in JMP. You can find the available functions from Random in Scripting Index or JSL Syntax Reference JSL Syntax Reference > JSL Functions, Operators, and Messages > Random Functions . You can most likely combine your list of values with Random Integer to get one of those randomly Random Integer (jsl.jmp.com) 

Re: JMP Formula Error While Creating Bootstrap‑Simulated Column

Thank you for the comments. Yes, I can generate random numbers using the technique you suggested. However, I am specifically interested in creating a bootstrap‑simulated column with 10,000 observations for the dataset. I know I can do this by selecting the distribution, clicking Mean, and then applying bootstrap. Is there an alternative method available?

Re: JMP Formula Error While Creating Bootstrap‑Simulated Column

Try this JSL and see if it gives you what you need:

Names Default To Here( 1 );
dt = data table("Sample Data File_Sheet1");

dt << Distribution(
	Continuous Distribution(
		Column( :CPF_Payout_USD ),
		Process Capability( 0 ),
		Fit Normal
	)
);
obj = current report();
obj["Fitted Normal Distribution",Number Col Box( 1 )] << 
Bootstrap(
	10000,
	Fractional Weights( 1 ),
	Split Selected Column( 1 )
);

 

Re: JMP Formula Error While Creating Bootstrap‑Simulated Column

Hi @Madhu_Acharyya,

Welcome in the Community !
Completely different answer (and questions !) from my side :

• You can have a reliable model with a small number of observations. The quality and information contained in your dataset is more important than the number/quantity of data.
• Using boostrapping to "artificially" expand the size of your dataset won't help you fit a model more easily. It will only repeat and enforce patterns already present in the data and give you a (false) impression of confidence in the model, with inflated model/performance metrics and "artificially reduced" confidence intervals.
• Based on the files you provided, it's not clear what you intend to do. I would have naively guessed that you want to do a forecasting of CPF response based on the year (so Time Series Analysis and/or Time Series Forecast) but your second file about simulations show very different columns. Can you clarify ?

If you intend to do Time Series analysis, I would try easy options first, like simple moving average with 3 or 5 points for the window width, as this simple model type still helps to understand and forecast the trend in your data:

More advanced method and models, like ARIMA / Seasoned ARIMA / ... are also available, but require more assumptions and a better definition of the seasonality and other model parameters.

Hope this answer may help in the discussion,