Empirical Studies of Transferability of Helsinki Metropolitan Area Travel Forecasting Models

1997 ◽  
Vol 1607 (1) ◽  
pp. 38-44 ◽  
Author(s):  
Nina Karasmaa ◽  
Matti Pursula
Keyword(s):  
Metropolitan Area ◽  
Model Updating ◽  
Statistical Tests ◽  
Empirical Studies ◽  
Estimation Procedure ◽  
Small Sample ◽  
Distribution Models ◽  
Travel Forecasting ◽  
The Difference ◽  
Small Sample Sizes

The temporal transferability of mode choice and trip distribution models was studied by using the data based on traffic surveys in the Helsinki, Finland, metropolitan area in 1981 and 1988. The updating procedures examined were the Bayesian updating, combined transfer estimation, transfer scaling, and joint context estimation procedures. The results of model updating indicated that finding the correct method and sample size for each case is not an unambiguous task. The best method depends on the difference in model coefficients between the initial and the final stages as well as the quality of the data. According to the statistical tests, no differences could be discerned between the models at all. However, the sample enumeration test proved that the models’ ability to predict changes in behavior can vary greatly according to the method used. On the basis of this research the transfer scaling seems to be the method best suited for simple models. In particular, the method is quite useful if the transfer bias is large. The combined transfer estimation procedure performs best when there is a great number of observations and the transfer bias is small. With small sample sizes the Bayesian approach and the joint context estimation give the best results.

Confidence intervals for a difference between lognormal means in cluster randomization trials

2014 ◽  
Vol 26 (2) ◽  
pp. 598-614 ◽  
Author(s):  
Julia Poirier ◽  
GY Zou ◽  
John Koval
Keyword(s):  
Confidence Intervals ◽  
Community Acquired Pneumonia ◽  
Small Sample ◽  
Cluster Randomization ◽  
Critical Pathway ◽  
Arithmetic Means ◽  
Multiple Parameters ◽  
The Difference ◽  
Using Data ◽  
Small Sample Sizes

Cluster randomization trials, in which intact social units are randomized to different interventions, have become popular in the last 25 years. Outcomes from these trials in many cases are positively skewed, following approximately lognormal distributions. When inference is focused on the difference between treatment arm arithmetic means, existent confidence interval procedures either make restricting assumptions or are complex to implement. We approach this problem by assuming log-transformed outcomes from each treatment arm follow a one-way random effects model. The treatment arm means are functions of multiple parameters for which separate confidence intervals are readily available, suggesting that the method of variance estimates recovery may be applied to obtain closed-form confidence intervals. A simulation study showed that this simple approach performs well in small sample sizes in terms of empirical coverage, relatively balanced tail errors, and interval widths as compared to existing methods. The methods are illustrated using data arising from a cluster randomization trial investigating a critical pathway for the treatment of community acquired pneumonia.

Sample Size and Accuracy of Estimates in Multilevel Models

Methodology ◽  
2011 ◽  
Vol 7 (3) ◽  
pp. 111-120 ◽  
Author(s):  
Omar Paccagnella
Keyword(s):  
Variance Components ◽  
Multilevel Models ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Estimation Procedure ◽  
Small Sample ◽  
Standard Errors ◽  
Sample Sizes ◽  
Monte Carlo Simulation Study ◽  
Small Sample Sizes

In a multilevel framework several researches have investigated the behavior of estimates in finite samples, particularly for continuous dependent variables. Some findings show poor precise estimates for the variance components. On the other hand, discrete response multilevel models have been investigated less widely. In this paper we analyze the influence of different factors on the accuracy of estimates and standard errors of estimates in a binary response 2-level model, through a Monte Carlo simulation study. We investigate the hypothesis of: (a) small sample sizes; (b) different intraclass correlation coefficients; (c) different numbers of quadrature points in the estimation procedure. Standard errors of estimates are studied through a noncoverage indicator. In all instances we have considered, the point estimates are unbiased (even with very small sample sizes), while the variance components are underestimated. The accuracy of the standard errors of variance estimates needs a very large number of groups.

scholarly journals Exploring rationality in schizophrenia

BJPsych Open ◽  
2015 ◽  
Vol 1 (1) ◽  
pp. 98-103 ◽  
Author(s):  
Rasmus Revsbech ◽  
Erik Lykke Mortensen ◽  
Gareth Owen ◽  
Julie Nordgaard ◽  
Lennart Jansson ◽  
...  
Keyword(s):  
Test Performance ◽  
Statistical Tests ◽  
Empirical Studies ◽  
Neuropsychological Test ◽  
Group Differences ◽  
Neuropsychological Performance ◽  
Healthy Controls ◽  
The Difference

BackgroundEmpirical studies of rationality (syllogisms) in patients with schizophrenia have obtained different results. One study found that patients reason more logically if the syllogism is presented through an unusual content.AimsTo explore syllogism-based rationality in schizophrenia.MethodThirty-eight first-admitted patients with schizophrenia and 38 healthy controls solved 29 syllogisms that varied in presentation content (ordinary v. unusual) and validity (valid v. invalid). Statistical tests were made of unadjusted and adjusted group differences in models adjusting for intelligence and neuropsychological test performance.ResultsControls outperformed patients on all syllogism types, but the difference between the two groups was only significant for valid syllogisms presented with unusual content. However, when adjusting for intelligence and neuropsychological test performance, all group differences became non-significant.ConclusionsWhen taking intelligence and neuropsychological performance into account, patients with schizophrenia and controls perform similarly on syllogism tests of rationality.

scholarly journals JMASM 52: Extremely Efficient Permutation and Bootstrap Hypothesis Tests Using R

2020 ◽  
Vol 18 (2) ◽  
pp. 2-16
Author(s):  
Christina Chatzipantsiou ◽  
Marios Dimitriadis ◽  
Manos Papadakis ◽  
Michail Tsagris
Keyword(s):  
Efficient Method ◽  
Statistical Tests ◽  
Pearson Correlation ◽  
Small Sample ◽  
Pearson Correlation Coefficient ◽  
Computationally Efficient ◽  
Sample Mean ◽  
P Values ◽  
Mean Vectors ◽  
Small Sample Sizes

Re-sampling based statistical tests are known to be computationally heavy, but reliable when small sample sizes are available. Despite their nice theoretical properties not much effort has been put to make them efficient. Computationally efficient method for calculating permutation-based p-values for the Pearson correlation coefficient and two independent samples t-test are proposed. The method is general and can be applied to other similar two sample mean or two mean vectors cases.

scholarly journals statSuma: automated selection and performance of statistical comparisons for microbiome studies

2021 ◽  
Author(s):  
Robert J. Leigh ◽  
Richard A. Murphy ◽  
Fiona Walsh
Keyword(s):  
Statistical Tests ◽  
Data Distribution ◽  
Small Sample ◽  
Statistical Procedure ◽  
T Test ◽  
Desktop Computer ◽  
And Performance ◽  
Student’S T ◽  
Small Sample Sizes ◽  
Student’S T Test

There is a reproducibility crisis in scientific studies. Some of these crises arise from incorrect application of statistical tests to data that follow inappropriate distributions, have inconsistent equivariance, or have very small sample sizes. As determining which test is most appropriate for all data in a multicategorical study (such as comparing taxa between sites in microbiome studies), we present statsSuma, an interactive Python notebook (which can be run from any desktop computer using the Google Colaboratory web service) and does not require a user to have any programming experience. This software assesses underlying data structures in a given dataset to advise what pairwise or listwise statistical procedure would be best suited for all data. As some users may be interested in further mining specific trends, statSuma performs 5 different two-tailed pairwise tests (Student's t-test, Welch's t-test, Mann-Whitney U-test, Brunner-Munzel test, and a pairwise Kruskal-Wallis H-test) and advises the best test for each comparison. This software also advises whether ANOVA or a multicategorical Kruskal-Wallis H-test is most appropriate for a given dataset and performs both procedures. A data distribution-vs-Gaussian distribution plot is produced for each taxon at each site and a variance plot between all combinations of 2 taxa at each site are produced so Gaussian tests and variance tests can be visually confirmed alongside associated statistical determinants.

Confidence intervals for the difference between two relative risks

2020 ◽  
Vol 29 (10) ◽  
pp. 3048-3058
Author(s):  
Joshua N Sampson ◽  
Mitchell H Gail
Keyword(s):  
Human Papillomavirus ◽  
Confidence Interval ◽  
Small Sample ◽  
Control Treatment ◽  
Human Papillomavirus Vaccine ◽  
Relative Risks ◽  
Vaccine Trials ◽  
The Difference ◽  
Small Sample Sizes ◽  
Papillomavirus Vaccine

We provide methods to estimate the confidence interval for the difference between two relative risks. Letting p0, p1, and p2 be the probabilities of an event in three groups (i.e. control, treatment 1, treatment 2), our methods estimate a confidence interval for r =  p1/ p0 −  p2/ p0. We highlight that our methods can handle small sample sizes, covariates, and study populations from multiple strata. We specifically developed these methods for vaccine trials to estimate the difference between two vaccine efficacies, where VE1 = 1 −  p1/ p0, VE2 = 1 −  p2/ p0 and r = VE2 − VE1. We showcase our methods by using interim data from one of these trials to suggest that one dose of the human papillomavirus vaccine may be as efficacious as two doses of the vaccine.

scholarly journals The efficiency of poststratification compared with model-assisted estimation

2017 ◽  
Vol 47 (4) ◽  
pp. 515-526 ◽  
Author(s):  
Mari Myllymäki ◽  
Terje Gobakken ◽  
Erik Næsset ◽  
Annika Kangas
Keyword(s):  
Explanatory Variable ◽  
Auxiliary Information ◽  
Regression Tree ◽  
Principal Component ◽  
Small Sample ◽  
Tree Model ◽  
Explanatory Variables ◽  
The Difference ◽  
Small Sample Sizes ◽  
Regression Tree Model

Survey sampling with model-assisted estimation has recently gained popularity in forest inventory. Another option for utilizing the auxiliary information is to use poststratification, which is a special case of model-assisted estimation with class variables as explanatory variables. In this study, we compared the efficiency of poststratification with an increasing number of strata with model-assisted estimation. We carried out a study based on a simulated population. We considered four different types of poststratifications, namely (i) stratification based on predictions of a linear model, (ii) stratification based on a regression tree model, (iii) stratification based on the first principal component of the explanatory variables, and (iv) stratification based on the regression tree model with the first principal component as the only explanatory variable. Furthermore, we examined both the traditional poststratification mean and variance estimators and the difference estimator and its variance estimator for poststratification. Within the recommended range of number of strata, the model-assisted approach was more efficient than poststratification. With a large number of strata, poststratification produced smaller standard error of estimates, but problems such as empty strata were encountered with small sample sizes. Using the first principal component directly for stratification or as an explanatory variable was the most efficient approach.

scholarly journals Testing for Equality of Parameters from Different Load-Sharing Systems

Stats ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 70-88
Author(s):  
Stefan Bedbur ◽  
Udo Kamps
Keyword(s):  
Order Statistics ◽  
Statistical Tests ◽  
Meta Analysis ◽  
Load Sharing ◽  
Small Sample ◽  
Asymptotic Distributions ◽  
Test Statistics ◽  
Sequential Order ◽  
Sequential Order Statistics ◽  
Small Sample Sizes

In reliability, sequential order statistics serve as a model for the component lifetimes of k-out-of-n systems, which are operating as long as k out of n components are operating. In contrast to modelling with order statistics, load-sharing effects and other impacts of failures on the performance of the remaining components may be taken into consideration. Inference for associated load-sharing parameters, as well as for the underlying baseline distribution, is then of particular interest. In a setup of multiple samples of sequential order statistics modelling the component lifetimes of possibly differently structured k-out-of-n systems, we provide exact statistical tests to check for common load-sharing or common baseline-distribution parameters. In the two-sample case, critical values for the corresponding test statistics are tabulated for small sample sizes, and the asymptotic distributions of the test statistics under the null hypotheses are derived. Based on a simulation study, power comparisons are addressed. The proposed tests may be applied to detect significant differences between systems or to decide whether a meta-analysis of the data may be conducted to increase the performance of subsequent inferential procedures.

scholarly journals Limits to Genomic Divergence Under Sexually Antagonistic Selection

2019 ◽  
Author(s):  
Katja R. Kasimatis ◽  
Peter L. Ralph ◽  
Patrick C. Phillips
Keyword(s):  
Genetic Model ◽  
Empirical Studies ◽  
Genetic Load ◽  
Small Sample ◽  
Sampling Variance ◽  
Antagonistic Selection ◽  
Genome Wide ◽  
Sexually Antagonistic Selection ◽  
Small Sample Sizes ◽  
The Relationship

AbstractSince the autosomal genome is shared between the sexes, sex-specific fitness optima present an evolutionary challenge. While sexually antagonistic selection might favor different alleles within females and males, segregation randomly reassorts alleles at autosomal loci between sexes each generation. This process of homogenization during transmission thus prevents between-sex allelic divergence generated by sexually antagonistic selection from accumulating across multiple generations. However, recent empirical studies have reported high male-female FST statistics. Here, we use a population genetic model to evaluate whether these observations could plausibly be produced by sexually antagonistic selection. To do this, we use both a single-locus model with nonrandom mate choice, and individual-based simulations to study the relationship between strength of selection, degree of between-sex divergence, and the associated genetic load. We show that selection must be exceptionally strong to create measurable divergence between the sexes and that the decrease in population fitness due to this process is correspondingly high. Individual-based simulations with selection genome-wide recapitulate these patterns and indicate that small sample sizes and sampling variance can easily generate substantial male-female divergence. We therefore conclude that caution should be taken when interpreting autosomal allelic differentiation between the sexes.

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