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Pooling, eg for variance, is used when several groups or populations are assumed to have a common property (a common parameter value) and the information from all the groups or populations are used together to estimate that common property.

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I'm using the mice and miceadds packages in R to perform multiple imputation and then analyze the results. Here's what I did: I performed multiple imputation on my dataset using the mice package. For ...
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I have read that PCSE (panel corrected standard error) can model autocorrelation by adjusting standard error to account for it. would this be an ideal solution ?
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Say I am computing Hopkins statistic of clustering tendency. The statistic compares the data cloud with the cloud of points randomly and uniformly simulated in the same spatial region. Under the null ...
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I’ve been struggling with this question for a while, so any help is much appreciated! I’m trying to calculate an effect size (partial eta squared or $\eta^{2}_p$) for an ANCOVA model using pooled data ...
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I was wondering, suppose I receive a list of results (e.g., two independent variables A and B that estimate the same value), I could perform a hypothesis test to check whether the two variances are ...
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I am familiar that, for example, Hausman's test can help me pick which is better between a fixed-effects and a random-effects model. But is there a test that can help me choose between a between ...
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I am trying to estimate the prevalence of a binary variable "x" and its confidence interval after multiple imputations (using mice) and applying weights in R. I use Rubin's rules for the ...
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I want to do a narrative review (as a meta-analysis is not possible), and I want to conclude per study whether the predictor was significant yes/no. However, studies have several effect sizes per ...
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I am new to meta-analysis and currently trying to find effect of intervention on service users. Most of the studies that I found were RCTs or quasi-experimental studies with 2 groups pre-post test ...
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Formulated Question In a two-sample bootstrap procedure for testing the difference in means, why is it insufficient to subtract the group-specific mean from each bootstrapped observation (centering ...
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I am comparing $m$ methods across $d$ datasets. Through my experiments I have obtained the mean, the standard deviation, and the standard error for all methods and all datasets, hence I have the means ...
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I understand that Rubin's rule is commonly applied for pooling model results across multiple imputed datasets, where the same set of predictors and response are used, like this: ...
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Our group has had a long-standing protocol wherein multiple tissue purifications are pooled. This is because there is very little of the tissue per animal. This pool was then sampled several times and ...
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I'm using MICE to impute a small data set. I am going to use ANCOVA of type II through Anova function of R package car. However, ...
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I have a data.frame named mydata with 6 columns: status, times, t1, t2, t3, t4. However, t1, t2, t3, and t4 contain missing values in this dataset. I intend to impute these missing values using the ...
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Following Rubin's rules for multiple imputation, I've calculated pooled estimates, group means in this case, with pooled standard errors. I checked this with a bootstrap and, assuming pooled standard ...
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If several samples are taken from a distribution, say Gaussian, each sample having size n1,n2,n3,... and the SD of the underlying distribution is estimated from each of the samples, how can those ...
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I have three batches of simulations and I calculate a specific property from these (not important for the question). I want to calculate combined SEM for these three batches Batch | #samples | ...
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In my analysis, the data contains 5 imputed dependent variables. So, after analyzing all of the dependent variables separately with a regression neural network, I need to combine/pool the results. ...
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I have analyzed some secondary data that relates to Plasmodium infection at three forest sites: inside the forest, at the forest fringe, and outside the forest. The outcome variable is infection with ...
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In one of my research, we decided to "update" the procedure mid-study. In short, we initially tested a repeated measures design (1 factor with 3 levels, 5 times per condition for a total of ...
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My sample comprises of data on accounting performance of companies that had their IPOs between 2009-22. I want to examine if companies which had more foreign investor participation in their IPOs ...
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I am trying to create an intuitive example which shows why Mixed Effects Regression models perform Partial Pooling in the background. I previously tried to demonstrate that Mixed Effects Regressions ...
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My undergrad thesis has something to do with the relationship between emissions, mitigation-related official development assistance, and governance variables in chosen ASEAN countries. As such, my (...
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I am trying to come up with a statistically sensible pooled kurtosis estimator that is based on pooled cumulant estimators. Specifically, I have unbiased estimators of the second and fourth cumulant ...
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This is information I believe to be true A practical feature of hierarchical Bayesian models is that partial pooling reduces (eliminates?) the need of adjusting for multiple comparisons when ...
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I am reading the book Statistical Methods In Online A/B Testing. I have two questions: 1. Please Consider the scenario, an A/B test in which the variance of A and B groups are assumed to be same, and ...
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I have three small data sets which include scores on global cognitive assessments. Two data sets use the score on the Mini Mental State Exam, one uses the score of the Montreal Cognitive Assessment. ...
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I want to perform a meta-regression based on the mean changes from baseline values.In one of the studies, the standard deviation has not been mentioned. How can I impute the value of the missed ...
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Suppose that $X_{ik}\sim\mathcal N(0,\sigma^2)$ for $k = 1,2,\dots, n_i$ are independent and identically distributed for each $i \in\{ 1,2\}$. Note that I assume equal means ($0$) and variances ($\...
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I am running a pooled OLS regression as a benchmark model on a panel data set of online forum member activity. The aim of the model is to understand the relationship between exposure to hate speech ...
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I recognize that if the population variances are not equal, it's not really a pooled variance to start with, but I wanted to keep the question succinct. The scenario here is that I begin with a sample ...
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I spend a lot of time thinking about conditions under which various kinds of analyses perform well. To some degree I am hoping NOT to specify too many specifics here (because part of what I am ...
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Imagine that we have at disposal several batches of data from a given process and that the interval time of record of each batches can be week, months or even years. Out of these data, we would like ...
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I am running pooled regression for 67 group my dependent variable is gini disposable and independent variable is women empowerment (economic,social,political) and institutional quality ( government ...
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I have a dataset with counts of birds in two locations over time, and am interested in describing the difference in trends in bird counts between these locations. The counts are conducted by multiple ...
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After logistic regression of the cross-sectional data sets, link test _hatsq shows insignificant. However, when I pool the same two data sets, the link test using the same set of variables regression ...
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Analysts often use Rubin's rule (RR) to obtain a pooled estimate of a popular quantity from multiple (imputed) datasets. While popular statistical software (such as the R ...
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I am trying to answer a question about satisfaction and its relation with a certain variable (numeric, 1-10). However, my data contains a lot of missing values in the satisfaction outcome, therefore I ...
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I fit a cox regression using the coxph function of the survival package. Now I wanted to do the same on a multiple imputed data set (which I already have, generated in another software). I found some ...
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I want to estimate the distribution of a variable in certain subgroups of the population based on pooling of aggregate data reported in various observational studies. For simplicity, assume there is a ...
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I have groups of samples, where each group has a different number of samples and a different mean. I also know the variance of each group of samples. I would like to compute a sort of "average&...
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I have a medical dataset that has a lot of missing values. I imputed five datasets using MICE in R. I want to fit a classification machine learning model to the dataset. I want to identify the most ...
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I am running a hierarchical logistic regression analysis using multiply imputed data in R (using the mice and miceafter packages). I am unable to get the odds ratio and 95% CI per variable adjusted ...
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I'm trying to calculate an effect size index where I need to pool 2 standard deviations calculated from correlated variables (pre-post treatment scores). Using the standard formula: $$ \sigma^2_{...
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What is the difference between "repeated cross-section" and "pooled cross-section"? Pooled cross-section is defined e.g. here as "randomly sampled cross sections of ...
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I am still trying to understand the effect of any downsampling (reducing the input height and weight by 2 for example) by pooling or strided convolution? Does downsampling improve accuracy? Because in ...
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I have multiple imputed data and will be conducting an identical lightGBM model with the same input features in each of the imputed datasets. My aim is to calculate SHAP values (SHapley Additive ...
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I have 70 imputations of my original data set. I want to choose 50 of them which converged after less than 120 iterations on my CFA model: ...
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I was reading the paper of [Geoffrey Hinton: Capsule network], and I watch it's talk on Youtube about the problem of Conv Network is actually the (max) pooling layer, since we don't want to be ...
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