This book reviews methods for handling missing data, manipulated data, multiple confounders, predictions beyond observation, uncertainty of diagnostic tests, and the problems of outliers.
This book reviews methods for handling missing data, manipulated data, multiple confounders, predictions beyond observation, uncertainty of diagnostic...
The first part of this title contained all statistical tests that are relevant for starters on SPSS, and included standard parametric and non-parametric tests for continuous and binary variables, regression methods, trend tests, and reliability and validity assessments of diagnostic tests. The current part 2 of this title reviews multistep methods, multivariate models, assessments of missing data, performance of diagnostic tests, meta-regression, Poisson regression, confounding and interaction, and survival analyses using log tests and segmented time-dependent Cox regression. Methods for...
The first part of this title contained all statistical tests that are relevant for starters on SPSS, and included standard parametric and non-param...
This handy guide will help clinicians with computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It includes step by step data analyses in SPSS.
This handy guide will help clinicians with computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It in...
The core principles of statistical analysis are too easily forgotten in today's world of powerful computers and time-saving algorithms. This step-by-step primer takes researchers who lack the confidence to conduct their own analyses right back to basics, allowing them to scrutinize their own data through a series of rapidly executed reckonings on a simple pocket calculator. A range of easily navigable tutorials facilitate the reader's assimilation of the techniques, while a separate chapter on next generation Flash prepares them for future developments in the field. This practical volume...
The core principles of statistical analysis are too easily forgotten in today's world of powerful computers and time-saving algorithms. This step-b...
Machine learning is concerned with the analysis of large data and multiple variables. It is also often more sensitive than traditional statistical methods to analyze small data. The first and second volumes reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, fuzzy modeling, various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, association rule learning, anomaly detection, and correspondence analysis. This third volume addresses more advanced methods and includes...
Machine learning is concerned with the analysis of large data and multiple variables. It is also often more sensitive than traditional statistical met...
Machine learning and big data is hot. It is, however, virtually unused in clinical trials. This is so, because randomization is applied to even out multiple variables
Modern medical computer files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required
This is the first publication of clinical trials that have been systematically analyzed with machine learning. In addition, all of the machine learning analyses were tested against traditional analyses. Step by step statistics for self-assessments are...
Machine learning and big data is hot. It is, however, virtually unused in clinical trials. This is so, because randomization is applied to even out...
IBM (international business machines) has published in its SPSS statistical software 2022 update a very important novel regression method entitled Kernel Ridge Regression (KRR). It is an extension of the currently available regression methods, and is suitable for pattern recognition in high dimensional data, particularly, when alternative methods fail. Its theoretical advantages are plenty and include the
kernel trick for reduced arithmetic complexity,
estimation of uncertainty by Gaussians unlike histograms,
corrected data-overfit by ridge...
IBM (international business machines) has published in its SPSS statistical software 2022 update a very important novel regression method entitled ...
IBM (international business machines) has published in its SPSS statistical software 2022 update a very important novel regression method entitled Kernel Ridge Regression (KRR). It is an extension of the currently available regression methods, and is suitable for pattern recognition in high dimensional data, particularly, when alternative methods fail. Its theoretical advantages are plenty and include the
kernel trick for reduced arithmetic complexity,
estimation of uncertainty by Gaussians unlike histograms,
corrected data-overfit by ridge...
IBM (international business machines) has published in its SPSS statistical software 2022 update a very important novel regression method entitled ...
An important novel menu for Survival Analysis entitled Accelerated Failure Time (AFT) models has been published by IBM (international Businesss Machines) in its SPSS statistical software update of 2023. Unlike the traditional Cox regressions that work with hazards, which are the ratio of deaths and non-deaths in a sample, it works with risk of death, which is the proportion of deaths in the same sample. The latter approach may provide better sensitivity of testing, but has been seldom applied, because with computers risks are tricky and hazards because they are odds are fine....
An important novel menu for Survival Analysis entitled Accelerated Failure Time (AFT) models has been published by IBM (international Businesss Mac...
