Data Model - Science topic

Thanks for your help

You can use these codes below

generate time = td(30 jan 2012)+_n-1

br time

format time %td

br time

tsset time

before using these codes copy the data set, excluding the time/date column and past them in stata

If you have any problem call me on +2348060962048

Yes

Hello. I also need this model. If you find it, please send it to me

Hakan is right but to make sure it is not "spurious correlation" I would check the regression model for different time period (years) when, for example, economic situation of seniors was different. But it depends how long your times series are to make this kind of validation.

IYH Dear Igor Niezgodzki

IMHO the issue with the inconsistent p-values generated by durbinWatsonTest() may be that by default, durbinWatsonTest() generates a non-zero number of bootstrap replicates to obtain more precise p-values. When run repeatedly, the resulting p-values will differ slightly due to the random sampling involved in the bootstrapping procedure.

To address this, set the number of bootstrap replicates to zero using the reps=0 argument inside durbinWatsonTest(). Doing so will yield deterministic p-values, and they should match the p-values produced by dwtest().

Thanks for sharing. Wishing you every success in your work.

That link is not working.

Hi Luis

To convert precipitation from g/m**2/s , you have to multiply by 3.6 to get precipitation in mm/hour. Since 1g=0.001kg, we can match units of kg/m**2/s to mm/hr. Now if you have the time of accumulation of precipitation in hr, you can easily match the two units (kg/m2/sec to meter).

See "On the continuity and distribution of water substance in atmospheric circulations" by E. Kessler, Atmospheric Research 38 (1995) 109-145.

Dear Sindia Trujillo ,

It is the interface.

Regards,

Shafagat

For small N and large T panel data, you can use the Fixed Effects (FE) model or the First Difference (FD) model. The FE model is preferred when there is no serial correlation in the idiosyncratic errors. However, if there is serial correlation in the idiosyncratic errors, then the FD model may be more efficient.

There are also other advanced panel data models that can be used for small N and large T data such as the Generalized Method of Moments (GMM) and the Maximum Likelihood (ML) estimator. However, these models require more assumptions and may not be appropriate for small N and large T data.

1/ multipurt y x1 x2 , lags(0)

2/ for the first diff if needed u have to diff vars before using multipurt

However, u have N>T and i think running URT is not necessary.

Best

why are you thinking on weighing instead of an indicator variable for censored data (observations you know they have attrition)?

I would not know how to weight observations that only report their perception just once or twice relative to others more responsive.... If you weight by the number of times they answered you are assuming that dropping out if relatively random I believe.

Applying a censored model you are using estimators that take into account that participants who drop out the sample are not similar to the ones that remain

There are several ongoing and emerging research areas in AI for healthcare systems. Some of the newest and next research focuses are:

Explainable AI (XAI) in Healthcare: Explainable AI aims to provide a clear understanding of how AI algorithms work and why they make certain decisions. In healthcare, XAI can help physicians and clinicians understand the reasoning behind AI-powered recommendations or diagnoses, and increase the trust and adoption of AI in clinical practice.

Federated Learning for Healthcare: Federated learning is a machine learning technique that enables multiple parties to collaboratively train a model without sharing their raw data. In healthcare, federated learning can enable the development of AI models on decentralized and diverse datasets while maintaining patient privacy and data security.

AI-assisted Medical Imaging Diagnosis: Medical imaging diagnosis is a time-consuming and error-prone process, and AI can assist radiologists and clinicians in accurately identifying and analyzing medical images. AI-powered medical imaging diagnosis can also help to reduce healthcare costs and improve patient outcomes.

AI-powered Drug Discovery and Development: AI can accelerate the drug discovery and development process by predicting drug efficacy, identifying potential side effects, and optimizing drug dosages. AI-powered drug discovery can also help to identify new therapeutic targets and treatments for diseases.

Personalized Medicine with AI: Personalized medicine aims to provide tailored healthcare interventions based on individual patient characteristics such as genetics, lifestyle, and environment. AI can help to identify personalized treatment options and predict treatment outcomes for patients, improving patient outcomes and reducing healthcare costs.

Overall, the application of AI in healthcare is still in its infancy, and there is a vast scope for research and innovation in this field.

Well, adding more independent variables has its penalty which is mainly the R-square adjusted.

Also, being as parsimonious as possible is quite necessary for model building. You could run a pre-analysis to confirm the significance of the independent variables you want to add. However, it is advisable to make the whole variables not more than 6.

Wintoki et al. (2012) adapt the procedure for testing the relevance of the instruments in 2SLS settings, that is to say they examined the F-Statistics for the joint significance of the instruments on the instrumented variable after the first stage, which is the reduced form equation where we regress the instrumented variable on all exogenous variables (x) + some some excluded instruments (z).

They carried out two separate procedures, one for the difference equation and for the level equation as they use system GMM estimator. In case you are using differenced GMM then you can follow the procedure for the differenced equation. For each procedure you use the instruments for the corresponding equation, for example, when you do the test for the level equation, use the instruments that were used in the level equation.

For more details you can consult their paper:

Wintoki, M. B., Linck, J. S., & Netter, J. M. (2012). Endogeneity and the dynamics of internal corporate governance. Journal of financial economics, 105(3), 581-606.

only take the difference of that variables which are non stationary.

Data about customers are sensitive and confidential and I doubt if any bank will like to release data about customers' loans. In my country (Nigeria) there may not be possibility of getting online information to capture this data because of fraudsters. I therefore have no idea of how clearance and permission can be given by Micro finance bank to get customers data about loans for research..

Hai John, Emmanouil and all:

Thank you having wonderful applicable discussions. My experience with highly reputed peer-reviewed first journals with high impact factor are that they are quite groovy and selective to known names.

Another interesting experience with recent many peer-reviewed papers will be that there are progressive upcoming journals where highly knowledgeable reviewers have migrated moving from top tier journals so-called the must citable journals. Scientific community will take a while to realize progressive journals have ongoing really useful papers or original articles, not exclusively with top-tier journals.

What John and Emmanouil have stated about the peer-review process, agenda is developing to establish novel publication platform requiring initial presentation, then RG type preprint with subsequent publication of paper or article, that are further peer-reviewed to ensure that more scientists have participated debates and discussions of addressed topics. Noteworthy rubrics may involve high quality of original research versus mere quantity or number of papers. This shift of publication paradigm will help to cure inconsistencies between theoretical and experimental outputs. Logically meaningful knowledge achieved thus will make sense of what physics really is, especially with respect to nature!!

We will have interesting debates discussions time progressing!!

Thank you with engaging communications.

Sincerely,

Rajan Iyer

ENGINEERINGINC INTERNATIONAL OPERATIONAL TEKNET EARTH GLOBAL

following this link

Akhilesh Nautiyal

1. Use the downhill simplex method to minimize a function.

2. Use the BFGS algorithm to minimize a function.

3. Use the nonlinear conjugate gradient technique to minimize a function.

4. Using the Newton-CG approach, minimize the function f.

5. Use a modified Powell's approach to minimize a function.

Brown Chitekwere,

May I ask if you find your answer to share it here, please?

In addition, when I ran the model, I received the error "rbind error: "names do not match previous names." Do you have any idea about it?

I appreciate your help.

Kind regards,

Mona

Jochen Wilhelm I agree very much with your statement that "you should better do more research on the meaning of the variable you are actually analyzing." I think that this is generally desirable for many studies. I also agree that there is a tendency in the social sciences to overemphasize standardized coefficients and to not even report unstandardized coefficients. That is very unfortunate in my opinion, as I believe both are important and have their place.

That being said, there are fields (mine included: psychology) where we are dealing with variables that simply do not have an intuitive metric. Many variables are based on test or questionnaire sum or average scores. People use different tests/questionnaires with different metrics/scoring rules in different studies. What does it mean when, for example, subjective well-being is expected to change by 2.57 for every one-unit change in self-rated health and by 1.24 for every one-unit change in extraversion when self rated health is measured on a 0 - 10 scale and extraversion ranges between 20 and 50?

Standardized estimates can give us a better sense for the "strength" of influence/association in the presence of other predictors than unstandardized coefficients when variables have such arbitrary and not widely agreed upon metrics. The interpretation in standard deviation (SD) units is not completely useless in my opinion, especially since we operate a lot with SD units also in the context of other effect size measures such as Cohen's d. It allows us (often, not always) to see fairly quickly which variables are relatively more important as predictors of an outcome--we may not care so much about the absolute/concrete interpretation or magnitude of a standardized coefficient, but it does matter whether it is .1 or .6.

In addition, in the context of latent variable regression or path models (i.e., structural equation models), unstandardized paths between latent variables often have an even more arbitrary interpretation as there are different ways to identify/fix latent variable scales (e.g., by using a reference indicator or by standardizing the latent variable to a variance of 1). Regardless of the scaling of the latent variables, the standardized coefficients will generally be the same.

This does not mean that I recommend standardized coefficients over unstandardized coefficients. Variance dependence and (non-)comparability across studies/different populations are important issues/drawbacks of standardized coefficients. Unstandardized coefficients should always be reported as well, and they are very useful when variables have clear/intuitive/known metrics such as, for example, income in dollar, age, number of siblings (or pretty much any count), IQ scores, etc. Unstandardized coefficients are also preferable for making comparisons across groups/populations/studies that used the same variables. I would always report both unstandardized and standardized coefficients along with standard errors and, if possible, confidence intervals.

I believe there are many examples of regression or path models in psychology for which standardized coefficients were reported and that did advance our knowledge regarding which variables are more important than others in predicting an outcome.

Devils advocate.

Referencing another transaction/block in a DAG is basically providing validation service. In other words, to have your transaction confirmed, you first validate another. The reason why you would want to have more then one parent is for the network to scale and induce more trust as you pointed out. As long as you give to the network more (validate two transactions) then the network has given you (validate your single transaction) the model can be scaled.

On the other hand, I'm not sure having one parent would result in a tree like structure unless you have some constraints on which parents can be picked. That would be analogue to tip selection problem?

Destroy Image Classification by Ensemble of Pre-trained models | by Vardan Agarwal | Towards Data Science

Dear @Sam Nicholson, I'm afraid climate models do not calibrate any parameter. These models are developed considering different physical processes in terms of their equations. The number of physical processes considered, the way in which they are analytically described and also numerically implemented, depends on different factors, including the spatio-temporal discretization of the climate model (i.e. grid dimension, time step length) and the total temporal horizon to be simulated. The evolution of the state variables of climate models depends on different model forcings such as the income radiative flux affecting the modeled system through the physical processes considered by the model (e.g. vapor condensation, evaporation, moisture recycling, etc. ). Therefore, to simulate the expected paleoclimate trends, which are often revealed by different proxies, you should include the corresponding climate forcings in the model, which are supposed to generate such paleoclimate trends.

The choice of a model should be based on underlying physical explanations first.

See the manual of my software for examples:

If you do any of the models that employ regression and particularly cointegration then the dynamic stability of your variables are more important than anything else ,this will be true for time series data. If you employ cross section data there are several econometric tests that can be performed to assure soundness of the mode and standard deviation is only one criterion that may be covered by the the various tests.

Thank you for your answer

Hi, check also Fixed-effect panel threshold model using Stata, Qunyong Wang, The Stata Journal (2015) 15, Number 1, pp. 121–134

That is a good question.

Adnan Majeed, thank you for sharing. In the PDF, it's mentionned that fixed effect for a mean difference (likelihood = "normal" and link = "identity") should be working, but I noticed that "burn in" iteration are not taken into consideration (see my attached PDF). Other likelihood / link works well.

I also opened an issue on Github... I wonder if there is other way I could proceed for a fixed effect model in mean difference with that package (random effect works well) ?

You need to be mindful of what any one IC is doing for you. They can look at 3 different contexts:

(a) you select a model structure now, fit the model to the data you have now and keep using those now-fitted parameters from now on.

(b) you select a model structure now and keep that structure, but will refit the model to an expanded dataset (reducing parameter-estimation variation but not bias).

(c) you select a model structure now and keep that structure, but will continually refit the model as expanded datasets are available (eliminating parameter-estimation variation but not bias).

Dynamic panel models like Arellano bond (xtabond,xtdpdsys) are used for large N and small T. For small N and large T, you can use FE, or Zellner's seemingly unrelated regressions (SUR)-with Stata command sureg. or FGLS (xtgls).

I try to replicate your code, and I don't get "None", I just get an error when I try to do an inference with the model (see image-1). In your forward you do it:

def forward(self, xb):

xb = self.features(xb)

xb = self.avgpool(xb)

xb = torch.flatten(xb,1)

xb = self.classifier(xb)

return xb

but features, avgpool and classifier are "variables" of network, then you need to do:

def forward(self, xb):

xb = self.network.features(xb)

xb = self.network.avgpool(xb)

xb = torch.flatten(xb,1)

xb = self.network.classifier(xb)

return xb

then when I run the forward again, everything looks ok. (see Image-2)

If this not work for you, could you share your .py? I need to check the functions: to_device, evaluate, and check the ImageClassificationBase class. To replicate the error and be able to identify where it is.

Dear Gonçalo Maria ,

Please note that transforming variable(s) does NOT make the series stationary, but rather makes the distribution(s) symmetrical. Application of logarithmic transformation needs to be exercised with extreme caution regarding properties of the series, underlying theory and the implied logical/correct interpretation of the relationships between the dep variable and associated selected regressors.

Reverting to your question, the proposed solution would be to use the Autoregressive Distributed Lag (ARDL) model approach, which is suitable for datasets containing a mixture of variables with different orders of integration. Kindly read the manuscripts attached for your info.

All the best!

Daniel Marusig I agree with Anoop Kumar Srivastava

Psychological Councilling data of pateints can be analysed statistically - https://www.ncbi.nlm.nih.gov/books/NBK425420/

Thank you Remal Al-Gounmeein for sharing! I think it's interesting because I have somewhat the opposite problem that this paper addresses; many people in my field use simple correlation to relate the abundance of taxa to one another, but typically those covariances can be explained by an environmental gradient. So including covariates actually vastly decreases the number of "significant" relationships. But still it's a point well-taken because explaining that e.g. taxon1 and taxon2 don't likely interact directly even though they are positively or negatively correlated would in fact require presenting the results of both models. Thanks!

Thanks Adnan Majeed

Dear @Jesie, you are talking about a supervised model. It is known as the C4.5 algorithm developed by Quinlan. This kind of model implies trainig, pruning, and test stages. The trainig will perform a tree growing up. It will split data based on the best feature choice iteratively. It will conitnue until a) there are not more data, b) the minimum volume of data in a node is not enough for a node splitting, or c) all data into a node belong to the same target class

After training the model, a pruning stage is performed to avoid the overfitting in the model. A cross-validation technique (k-folds=10) should be used for better results. You could use the weka software for analysis of this algorithm. I hope it is useful for you. (https://www.cs.waikato.ac.nz/ml/weka/).

Why don't you try a longitudinal models? the best intro is here:

Prof. Davidian developed these models. Feel free to ask questions.

Best, D. Booth

A new source of bioclimatic variables' uncertainty has been discovered that is related to the selection of the specific month/quarter (e.g. wettest quarter in case of bio8). Please refer to this paper:

Python library pandas will be useful here, just load the CSV and use the sample for analysis. This link would be helpful

Dear Murat,

I am more than pleased!

Ashay Singh

https://www.dataversity.net/database-reverse-engineering-case-study/

In general, to explore the heterogeneity in a panel data, you need to first identify the group classifications of data in line with variables of interest.

I suggest you to visit the website of TensorFlow , we can find you the example to explained you how devise your datasets and how you can test your model by using of images tests one by one.

I hope that be Claire for you.@ Nima Karimi

Dear Glückler,

Thanks for your answer. Yes, considering the calculation of the dating error for ²¹⁰Pb, the new rplum package give me a solution.

https://cran.r-project.org/web/packages/rplum/

Regards,

Mingda

I am an applied economist; not an econometrician. So I hesitate to say that published papers in good journals are not adhering to good practice. However, having made this gesture towards humility, this is what I am about to argue. The key to determining the model specification that best conforms to the underlying process or mechanism that generates your data (the data generating mechanism) is diagnostic testing. In the case of distinguishing between a static or a dynamic model - hence, making a judgement about the underlying data generating mechanism - a simple procedure is to estimate a static panel FE model and then test the within-group residuals for serial correlation. (In Stata, this may be done by the user-written xtserial package, which implements a test devised by Jeffrey Wooldridge.) If the null of no serial correlation in the residuals is not rejected, then it is reasonable to conclude that your model does not suffer from unmodelled dynamics. In this case, estimate a static model. However, if the null is rejected, then your model has not captured the dynamics in the data. In this case, estimate a dynamic model. In effect, estimating a dynamic model is to displace the dynamics in the data from the error term (where serial correlation violates the assumptions of the estimator) into the estimated part of the model (where the dynamics are explicitly modelled and thus yield useful information - e.g. enabling long-run and short-run effects to be distinguished). If this reasoning is correct, then it is not helpful (to say the least) to report both static and dynamic models. They cannot both be well specified. Indeed, it is my observation that researchers who report both do not undertake diagnostic tests. I suspect that if diagnostic testing were to be undertaken then it would reveal the static models to be misspecified and thus - by their construction - yielding biased and inconsistent estimates . (In my experience, time-series data usually exhibit serial dependence, which needs to be taken into account in specifying econometric models). To summarise: (I) test for serial correlation; and (ii) if you find it, explain to your readers why you specify and report only a dynamic model.

By the way, the GMM is a general approach to estimation. As such, it is often applied to dynamic models but is not restricted to dynamic modelling (e.g. the GMM approach can be used to estimate static models).

I agreed with Abdullahi Ubale Usman answer. But some other techniques like non-linear analysis, cluster analysis, factor analysis, etc. may be utilized in this regard

In recent updated versions such as ANSYS 19.3, Altair hyperworks, most of the cases, all input files formats are compatible with any solver and intermediately we can move from one solver to other without much hassle.

Richard John Smith,

I think you'll find these papers interesting:

when it comes to some kind of national, government maps, you may not find such a list

but RG is huge, maybe just people will tell you that their country is like in Canada or not

in Poland, unfortunately not, we have a geodetic map of Topographic Objects Database similar to OSM

You can run Fixed Effects, Random Effects, or Pooled OLS.

Variables such as population growth and foreign aid are inappropriate

If the dependent variable is stationary at I(1) you can use ARDL Bounds test, but if the dependent variable is stationary at I(0), you can use an augmented Autoregressive Distributed Lag (ARDL) bounds test.

The GMM dynamic panel estimators are appropriate for large N and small T and generally pre-tests are not conducted. A large panel sample size is often used so that the Central Limit Theorem can be invoked for the asymptotic normality of coefficients even if the residuals are non-normal. Robust standard errors can be employed to deal with autocorrelation and heteroscedasticity. For a broader discussion and how to apply GMM methods in Stata see:

Roodman, D. (2009). How to do xtabond2: An introduction to "Difference" and "System" GMM in Stata. The Stata Journal, 9(1), 86-136. With very small T unit root testing would not typically be applied. If T is large GMM estimators can become unreliable because the number of instruments becomes large and the instrumented variables can be overfitted and so may not remove the endogenous components of the lagged dependent variable(s) as intended. When N is not large and T is moderate you may wish to use a bias corrected LSDV estimator to deal with dynamic panel bias, although these assume that all variables other than the lagged dependent variable are strictly exogenous. To apply a bias-corrected LSDV estimator to a potentially unbalanced dynamic panel in Stata see:

Bruno, G. (2005). Estimation and inference in dynamic unbalanced panel-data models with a small number of individuals. The Sata Journal, 5(4), 473-500. With moderate T you may wish to apply panel unit root tests. Second generation tests that deal with cross-sectional dependence are recommended. An example paper that uses Bruno's estimator and panel unit root tests is given in:

(1) Goda, T., Stewart, C. and Torres García, A. (2019) ‘Absolute Income Inequality and Rising House Prices’, forthcoming in Socio-Economic Review. An example application of GMM dynamic panel estimators without unit root testing is: Matousek, R., and Nguyen, T. N., and Stewart, C. (2017) ‘Note on a non-structural model using the disequilibrium approach: Evidence from Vietnamese banks’, Research in International Business and Finance, 41, pp. 125 – 135.

Holger Steinmetz Thank you so much for your consistent help. I will go on reading and studying.

Hi.,

Google Trends to improve your SEO:

Seyed Shayan Sajjadinia Many thanks for your help!

You can write me here ( message ) or to my email : Alireza.shabani@hotmial.com

Yes, this too is a dynamic model and applying Arellano-Bond (or the related GMM-type estimators) would be perfectly adequate.

Hi evan;

As far as i know, to control biases of tracking and surveys (whatever the method used to obtain presence data) datasets is to become them spatially and temporarily equal. That is, using standardizing your datasets to make these datasets comparable across scales.

On the other hand, given the different nature of our datasets, you can use the R package named "biomod2" which integrates different modelling approaches, namely GLM, Maxent etc for the same datasets separately or combined.

I strongly recommend you to check this out, indeed this modelling approach is helpful to control biases instead of using solely one method.

See this paper; https://www.sciencedirect.com/science/article/pii/S1470160X15002393

I hope I've been of help to you. However, I think that other researchers with huge backaground in ecologuical modelling could give you more useful advises.

Best regards

Jon

It differs according to which mixed GLM you are discussing

for binomial see

and the references it contains

for Poisson , see