Provides a coherent and comprehensive account of the theory and practice of real-time human disease outbreak detection, explicitly recognizing the revolution in practices of infection control and ...public health surveillance. * Reviews the current mathematical, statistical, and computer science systems for early detection of disease outbreaks * Provides extensive coverage of existing surveillance data * Discusses experimental methods for data measurement and evaluation * Addresses engineering and practical implementation of effective early detection systems * Includes real case studies
Introduction to Statistical Analysis of Laboratory Data presents a detailed discussion of important statistical concepts and methods of data presentation and analysis * Provides detailed discussions ...on statistical applications including a comprehensive package of statistical tools that are specific to the laboratory experiment process * Introduces terminology used in many applications such as the interpretation of assay design and validation as well as "fit for purpose" procedures including real world examples * Includes a rigorous review of statistical quality control procedures in laboratory methodologies and influences on capabilities * Presents methodologies used in the areas such as method comparison procedures, limit and bias detection, outlier analysis and detecting sources of variation * Analysis of robustness and ruggedness including multivariate influences on response are introduced to account for controllable/uncontrollable laboratory conditions
Multicollinearity represents a high degree of linear intercorrelation between explanatory variables in a multiple regression model and leads to incorrect results of regression analyses. Diagnostic ...tools of multicollinearity include the variance inflation factor (VIF), condition index and condition number, and variance decomposition proportion (VDP). The multicollinearity can be expressed by the coefficient of determination (Rh2) of a multiple regression model with one explanatory variable (Xh) as the model's response variable and the others (Xi i ≠ h) as its explanatory variables. The variance (σh2) of the regression coefficients constituting the final regression model are proportional to the VIF. Hence, an increase in Rh2 (strong multicollinearity) increases σh2. The larger σh2 produces unreliable probability values and confidence intervals of the regression coefficients. The square root of the ratio of the maximum eigenvalue to each eigenvalue from the correlation matrix of standardized explanatory variables is referred to as the condition index. The condition number is the maximum condition index. Multicollinearity is present when the VIF is higher than 5 to 10 or the condition indices are higher than 10 to 30. However, they cannot indicate multicollinear explanatory variables. VDPs obtained from the eigenvectors can identify the multicollinear variables by showing the extent of the inflation of σh2 according to each condition index. When two or more VDPs, which correspond to a common condition index higher than 10 to 30, are higher than 0.8 to 0.9, their associated explanatory variables are multicollinear. Excluding multicollinear explanatory variables leads to statistically stable multiple regression models.
Statistical mechanics relies on the maximization of entropy in a system at thermal equilibrium. However, an isolated quantum many-body system initialized in a pure state remains pure during ...Schrödinger evolution, and in this sense it has static, zero entropy. We experimentally studied the emergence of statistical mechanics in a quantum state and observed the fundamental role of quantum entanglement in facilitating this emergence. Microscopy of an evolving quantum system indicates that the full quantum state remains pure, whereas thermalization occurs on a local scale. We directly measured entanglement entropy, which assumes the role of the thermal entropy in thermalization. The entanglement creates local entropy that validates the use of statistical physics for local observables. Our measurements are consistent with the eigenstate thermalization hypothesis.
Institutions of higher education are operating in an increasingly complex and competitive environment. This paper identifies contemporary challenges facing institutions of higher education worldwide ...and explores the potential of Big Data in addressing these challenges. The paper then outlines a number of opportunities and challenges associated with the implementation of Big Data in the context of higher education. The paper concludes by outlining future directions relating to the development and implementation of an institutional project on Big Data.
Statistical hypothesis testing compares the significance probability value and the significance level value to determine whether or not to reject the null hypothesis. This concludes "significant or ...not significant." However, since this process is a process of statistical hypothesis testing, the conclusion of "statistically significant or not statistically significant" is more appropriate than the conclusion of "significant or not significant." Also, in many studies, the significance level is set to 0.05 to compare with the significance probability value,
-value. If the
-value is less than 0.05, it is judged as "significant," and if the
-value is greater than 0.05, it is judged as "not significant." However, since the significance probability is a value set by the researcher according to the circumstances of each study, it does not necessarily have to be 0.05. In a statistical hypothesis test, the conclusion depends on the setting of the significance level value, so the researcher must carefully set the significance level value. In this study, the stages of statistical hypothesis testing were examined in detail, and the exact conclusions accordingly and the contents that should be considered carefully when interpreting them were mentioned with emphasis on statistical hypothesis testing and significance level. In 11 original articles published in the
in 2022, the interpretation of hypothesis testing and the contents of the described conclusions were reviewed from the perspective of statistical hypothesis testing and significance level, and the content that I would like to be supplemented was mentioned.
Propensity score analysis (PSA) is a prominent method to alleviate selection bias in observational studies, but missing data in covariates is prevalent and must be dealt with during propensity score ...estimation. Through Monte Carlo simulations, this study evaluates the use of imputation methods based on multiple random forests algorithms to handle missing data in covariates: multivariate imputation by chained equations-random forest (Caliber), proximity imputation (PI), and missForest. The results indicated that PI and missForest outperformed other methods with respect to bias of average treatment effect regardless of sample size and missing mechanisms. A demonstration of these five methods with PSA to evaluate the effect of participation in center-based care on children’s reading ability is provided using data from the Early Childhood Longitudinal Study, Kindergarten Class of 2010–2011. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: journal abstract)
The use of ambulatory assessments (AAs) as an approach to gather self-reported questionnaires or self-collected biochemical data is constantly increasing to investigate the experiences, states, and ...behaviors of individuals and their interaction with external situational factors during everyday life. It is often implicitly assumed that data from different sampling protocols can be used interchangeably, despite them assessing processes over different timescales in different intervals and at different occasions, which depending on the variables under study may result in fundamentally different dynamics. There are multiple temporal parameters to consider and while there is an abundance of sampling protocols that are applied regularly, to date, there is only limited empirical background on the influence different approaches may have on the data and findings. In this review, we aim to give an overview of commonly used types of AA in psychology, psychiatry, and biobehavioral research with a breakdown by temporal design parameters. Additionally, we discuss potential advantages and pitfalls associated with the various approaches. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
This article considers identification, estimation, and model fit issues for models with contemporaneous and reciprocal effects. It explores how well the models work in practice using Monte Carlo ...studies as well as real-data examples. Furthermore, by using models that allow contemporaneous and reciprocal effects, the paper raises a fundamental question about current practice for cross-lagged panel modeling using models such as cross-lagged panel model (CLPM) or random intercept cross-lagged panel model (RI-CLPM): Can cross-lagged panel modeling be relied on to establish cross-lagged effects? The article concludes that the answer is no, a finding that has important ramifications for current practice. It is suggested that analysts should use additional models to probe the temporalities of the CLPM and RI-CLPM effects to see if these could be considered contemporaneous rather than lagged. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
PDF
