What’s the difference between clinical document architecture (CDA) and Continuity of Care Document (CCD)? Having trouble understand what’s the difference and which one would be best to be used.
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I am aware that consulting a statistician is not free and it is something I cannot afford, so I am trying my shot here. So for the problem at hand, I've already finished data gathering for my research and am now calculating the results. However, I am stuck on what should I use for my statistical treatment of data.
For background, I am using ISO 25010 to test my software quality and user acceptance. The questionnaire consists of a number of questions for each cluster (functionality, reliability, usability, efficiency, maintainability, and portability). I've also used Likert Scale: Agreement Type. The hypothesis of my research says "There is no significant difference in the user acceptance results in terms of [clusters]". As of now, I've used Descriptive Statistics, mean(for each question), average mean(ave. of mean for each cluster, and mode), for calculating the results.
I feel that the result I currently have might be lacking when the final defense came. As far as I know, using a combination of statistical methods is okay to give a more strong foundation for your result.
Based on the background of my research, what other statistical methods should I use?
I am thinking of sample standard deviation, but I don't know if I should compute it by questions or by cluster.
Sorry, statistics is not really my forte.
Thank you in advance for your answers
I am currently working on a project where a multi criteria decision making algorithm is needed in order to evaluate several alternatives for a given goal. After long research, I decided to use the AHP method for my case study. The problem is that the alternatives taken into account for the given goal contain incomplete data.
For example, I am interested in buying a house and I have three alternatives to consider. One criterion for comparing them is the size of the house. Let’s assume that I know the sizes of some of the rooms of these houses, but I do not have information about the actual sizes of the entire houses.
My questions are:
Can we apply AHP (or any MCDM method) when we are dealing with
incomplete data?
What are the consequences?
And, how can we minimize the presence of missing data in MCDM?
I would really appreciate some advice or help! Thanks!
If you still looking for answers, please let me answer your questions.
Before the detail explain, I coludn't answer with a technical approach in programming language.
First, we can use uncertinal data for MCDM, AHP with statical method.
As reducing lost of data, you can use deep learning concepts like entropy.
The result of it will be get reliability by accuracy of probabilistic approach.
The example that you talked, you could find the data of entire extent in other houses has same extent of criteria. Accuracy will depend on number of criteria, reliability of inference.
To get the perfect answer in your problem, you might need to know optimization, linear algebra, calculus, statistics above intermediate level
I'm student in management major, and I would help you as I can. I hope you get what you want
I am seeking a method to allow me to analyse/search for patterns in asset price movements using 5 variables that move and change with price (from historical data).
I'd like to be able to assign a probability to a forecasted price move when for example, var1 and var2 do this and var3..5 do this, then price should do this with x amount of certainty.
Q1: Could someone point me in the right direction as to what framework / technique can help me achieve this?
Q2: Would this be a multivariate continuous random series analysis?
Q3: A Hidden Markov modelling?
Q4: Or perhaps is it a data-mining problem?
I'm looking for what rather then how.
One may opt to use Machine-Learning tools to build a learner to either
both classify of what kind the said "asset price movement" will beand serve also statistical probability measures for such a Classifier prediction
both regress a real target value, to which the asset price will moveandserve also statistical probability measures for such a Regressor prediction
A1: ( while StackOverflow strongly discourages users to ask about an opinion about a tool or a particular framework ) there would be not much damages or extra time to be spent, if one performs academia papers research and there would be quite a remarkable list of repeatedly used tools, used for ML in the context of academic R&D. For a reason, there would not be a surprise to meet scikit-learn ML-classes a lot, some other papers may work with R-based quantitative finance / statistical libraries. The tools, however, with all due respect, are not the core to answer all the doubts and inital confusion present in a mix of your questions. The subject confusion is.
A2: No, it would not. Well, unless you beat all the advanced quantitative research and happen to prove that the Market exhibits a random behaviour ( which it is not and for which it would be waste of time to re-cite remarkable research published about why it is not indeed a random process ).
A3: Do not try to jump on any wagon just because of it's attractive Tag or "contemporary popularity" in marketing minded texts. With all due respect, understanding HMM is outside of your sight while you now appear to move just to the nearest horizons to first understand what to look for.
A4: This is a nice proof of a missed target. Your question shows in this particular point better than in others, how small amount of own research efforts were put into covering the problem-domain and acquiring at least some elementary knowledge before typing the last two questions.
StackOverflow encourages users to ask high quality questions, so do not hesitate to re-edit your post to add some polishing efforts to this subject.
If in a need for an inspiration, try to review a nice and a powerful approach for a fast Machine Learning process, where both Classification and Regression tasks obtain also probability estimates for each predicted target value.
To have some idea about highly performant ML-predictors, these typically operate on much more than a set of 5 variables ( called in the ML-domain "features" ) . ( Think rather about some large hundreds to small thousands features, typically heavily non-linear transformations from the original TimeSeries' data ).
There you go, if indeed willing to master ML for algorithmic trading.
May like to read about a state-of-art research in this direction:
[1] Mondrian Forests: Efficient Online Random Forests
>>> arXiv:1406.2673v2 [stat.ML] 16 Feb 2015
[2] Mondrian Forests for Large-Scale Regression when Uncertainty Matters
>>> arXiv:1506.03805v4 [stat.ML] 27 May 2016 >>>
May also enjoy other posts on subject: >>> StackOverflow Algorithmic-Trading >>>
In the context of a large-scale data mining benchmarking study, I am comparing 15 algorithms over 9 data sets, leading to an overall 135 algorithm/dataset combinations. The study is done using WEKA.
My last analysis is concerned with the influence of feature selection. I am aware, that there is no such thing as the perfect feature selection algorithm but the optimal choice rather depends on both algorithm to be deployed and the data set to which it will be applied.
Although the problem is to large to find the optimal feature selection algorithm for each combination, I am looking for ones that are considered to show a good performance in general, 'allrounder' so to say.
So far I have found recommendation for CFS (Correlation-based feature selection), ReliefF and Consistency-based subset evaluation (Hall / Holmes 2002) as a generally good choice as well as the note from a survey, that methods as simple as Rankers (e.g. Correlation coefficient) proved quiet effective (Guyon / Ellissef 2003).
Is there a good benchmark study some other research indicating which methods to use or which ones to use in practice?
From a Text Classification point of view, there is one article by Yang etal. comparing different feature selection algorithms (chi square, document frequency and Information Gain).
Although it is focus on text (i.e., the document frequency won't apply to you at all) the others might, depending on the nature of your features (i.e., binary or not, always present, ...)
I hope this helps.
I'm trying to use topic modeling with Mallet but have a question.
How do I know when do I need to rebuild the model? For instance I have this amount of documents I crawled from the web, using topic modeling provided by Mallet I might be able to create the models and infer documents with it. But overtime, with new data that I crawled, new subjects may appear. In that case, how do I know whether I should rebuild the model from start till current?
I was thinking of doing so for documents I crawled each month. Can someone please advise?
So, is topic modeling more suitable for text under a fixed amount of topics (the input parameter k, no. of topics). If not, how do I really determine what number to use?
The answers to your questions depend in large part on the kind of data you're working with and the size of the corpus.
Regarding frequency, I'm afraid you'll just have to estimate how often your data changes in a meaningful way and remodel at that rate. You could start with a week and see if the new data lead to a significantly different model. If not, try two weeks and so on.
The number of topics you select is determined by what you're looking for in the model. The higher the number, the more fine-grained the results. If you want a broad overview of what's in your corpus, you could select say 10 topics. For a closer look, you could use 200 or some other suitably high number.
I hope that helps.