Products related to Missing:
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Handbook of Missing Data Methodology
Missing data affect nearly every discipline by complicating the statistical analysis of collected data.But since the 1990s, there have been important developments in the statistical methodology for handling missing data.Written by renowned statisticians in this area, Handbook of Missing Data Methodology presents many methodological advances and the latest applications of missing data methods in empirical research. Divided into six parts, the handbook begins by establishing notation and terminology.It reviews the general taxonomy of missing data mechanisms and their implications for analysis and offers a historical perspective on early methods for handling missing data.The following three parts cover various inference paradigms when data are missing, including likelihood and Bayesian methods; semi-parametric methods, with particular emphasis on inverse probability weighting; and multiple imputation methods.The next part of the book focuses on a range of approaches that assess the sensitivity of inferences to alternative, routinely non-verifiable assumptions about the missing data process.The final part discusses special topics, such as missing data in clinical trials and sample surveys as well as approaches to model diagnostics in the missing data setting.In each part, an introduction provides useful background material and an overview to set the stage for subsequent chapters.Covering both established and emerging methodologies for missing data, this book sets the scene for future research.It provides the framework for readers to delve into research and practical applications of missing data methods.
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Applied Missing Data Analysis, Second Edition
The most user-friendly and authoritative resource on missing data has been completely revised to make room for the latest developments that make handling missing data more effective.The second edition includes new methods based on factored regressions, newer model-based imputation strategies, and innovations in Bayesian analysis.State-of-the-art technical literature on missing data is translated into accessible guidelines for applied researchers and graduate students.The second edition takes an even, three-pronged approach to maximum likelihood estimation (MLE), Bayesian estimation as an alternative to MLE, and multiple imputation.Consistently organized chapters explain the rationale and procedural details for each technique and illustrate the analyses with engaging worked-through examples on such topics as young adult smoking, employee turnover, and chronic pain.The companion website (www.appliedmissingdata.com) includes data sets and analysis examples from the book, up-to-date software information, and other resources. New to This Edition *Expanded coverage of Bayesian estimation, including a new chapter on incomplete categorical variables. *New chapters on factored regressions, model-based imputation strategies, multilevel missing data-handling methods, missing not at random analyses, and other timely topics. *Presents cutting-edge methods developed since the 2010 first edition; includes dozens of new data analysis examples. *Most of the book is entirely new.
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Missing
On an early autumn day like any other Miles Avery drives his wife, Jacqueline, to the train station. Jacqueline gets out of the car, picks up her overnight bag, and turns towards the platforms. This is the last anyone sees of her. When Miles calls the police three weeks later, they find no evidence of Jacqueline boarding a train, or even entering the station. Does Miles know more than he’s letting on? And could his missing wife somehow be linked to the tragic disappearance of their son years before?As dark secrets from the past merge with those of the present, Miles must confront his biggest fears if he’s to uncover the truth.Praise for Susan Lewis'One of the best around' Independent on Sunday'A guaranteed tear-jerker that will keep you at the edge of your seat' OK!'Spellbinding' Daily Mail
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missing
Dryad Press’s re-launch of missing underlines the value of a collection that, in Finuala Dowling’s words, “covers the archetypal journey from sickness and neardeath to transformation and hope … — though rich in deftly crafted images, the poems are profoundly inviting, readable, memorable”.
Price: 14.00 £ | Shipping*: 3.99 £
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What is data protection and security?
Data protection and security refer to the measures and practices put in place to safeguard data from unauthorized access, use, or disclosure. This includes protecting data from cyber threats such as hacking, malware, and phishing attacks, as well as ensuring compliance with privacy regulations and laws. Data protection and security also involve implementing encryption, access controls, and regular data backups to prevent data loss and maintain the integrity of sensitive information. Overall, data protection and security are essential for maintaining the confidentiality, integrity, and availability of data.
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What is more important, data protection or privacy?
Both data protection and privacy are important, but they serve different purposes. Data protection focuses on safeguarding the information collected and stored by organizations, ensuring it is secure from unauthorized access or breaches. Privacy, on the other hand, is about giving individuals control over their personal information and how it is used. Both are essential for maintaining trust and security in the digital age, and organizations should prioritize both data protection and privacy to ensure the safety and rights of their users.
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Which reseller hosting services offer privacy and data protection?
Many reseller hosting services offer privacy and data protection as part of their packages. Some popular options include SiteGround, Bluehost, and A2 Hosting. These providers offer features such as SSL certificates, regular backups, and secure data centers to ensure the privacy and protection of customer data. It's important to carefully review the privacy and security features offered by each reseller hosting service to ensure they meet your specific needs.
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What are the missing data in physics?
In physics, missing data refers to information that is not available or not measured in a particular experiment or study. This could include variables that were not recorded, measurements that were not taken, or data that was lost or corrupted. Missing data can lead to gaps in our understanding of a phenomenon and can affect the accuracy and reliability of our conclusions. Researchers use various techniques to address missing data, such as imputation methods or sensitivity analyses, to account for the potential impact of missing information on their results.
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Flexible Imputation of Missing Data, Second Edition
Missing data pose challenges to real-life data analysis.Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice.Multiple imputation replaces each missing value by multiple plausible values.The variability between these replacements reflects our ignorance of the true (but missing) value.Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals.Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem. This is the second edition of a popular book on multiple imputation, focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author.This new edition incorporates the recent developments in this fast-moving field. This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms.The book sharpens the reader’s intuition on how to think about missing data, and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data.
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Bayesian Nonparametrics for Causal Inference and Missing Data
Bayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay with causal inference and missing data.This book emphasizes the importance of making untestable assumptions to identify estimands of interest, such as missing at random assumption for missing data and unconfoundedness for causal inference in observational studies.Unlike parametric methods, the BNP approach can account for possible violations of assumptions and minimize concerns about model misspecification.The overall strategy is to first specify BNP models for observed data and then to specify additional uncheckable assumptions to identify estimands of interest. The book is divided into three parts. Part I develops the key concepts in causal inference and missing data and reviews relevant concepts in Bayesian inference.Part II introduces the fundamental BNP tools required to address causal inference and missing data problems.Part III shows how the BNP approach can be applied in a variety of case studies.The datasets in the case studies come from electronic health records data, survey data, cohort studies, and randomized clinical trials. Features• Thorough discussion of both BNP and its interplay with causal inference and missing data• How to use BNP and g-computation for causal inference and non-ignorable missingness• How to derive and calibrate sensitivity parameters to assess sensitivity to deviations from uncheckable causal and/or missingness assumptions• Detailed case studies illustrating the application of BNP methods to causal inference and missing data• R code and/or packages to implement BNP in causal inference and missing data problemsThe book is primarily aimed at researchers and graduate students from statistics and biostatistics.It will also serve as a useful practical reference for mathematically sophisticated epidemiologists and medical researchers.
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Missing Link
Stop-motion animation featuring the voice talents of Hugh Jackman, Zach Galifianakis and Zoe Saldana.In search of a legendary creature, said to be the missing evolutionary link between man and beast, Sir Lionel Frost (Jackman) discovers the giant, talking Mr. Link (Galifianakis), the last of his kind, who enlists the help of the monster investigator to find his distant cousins.The duo joins forces with adventurer Adelina Fortnight (Saldana) and set off on an adventure across the globe to find Mr. Link's distant relatives and potentially make the discovery of the century.
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Missing Kissinger
'Etgar Keret's short stories are fierce, funny, full of energy and insight, and at the same time they are often deep, tragic and very moving' - Amos OzAt a children's tea party, a magician tries to pull a rabbit out of a hat, but takes out only its head; a young man has a mother and girlfriend who each demand that he gives them the other one's heart; while a Nobel Laureate asks an orphan to perform a very strange task. In Etgar Keret's blackly comic stories the unexpected can, and usually does, happen.They are clever, quick, sometimes violent and often intensely poignant.They are, in short, brilliant.
Price: 9.99 £ | Shipping*: 3.99 £
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After resetting the PC, data is missing.
After resetting the PC, data may be missing due to the reset process restoring the computer to its original factory settings. This can result in the deletion of personal files, documents, and other data that was not backed up or saved elsewhere. It is important to always back up important data before performing a reset on a PC to avoid losing any important information. Additionally, it is recommended to use data recovery tools or services to attempt to retrieve any missing data after a reset.
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What is the question about data protection and data security?
The question about data protection and data security revolves around how organizations can safeguard sensitive information from unauthorized access, breaches, and cyber threats. It also involves ensuring compliance with data protection regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Additionally, the question may address best practices for data encryption, access controls, regular security audits, and incident response plans to mitigate risks and protect data integrity.
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What is the difference between data protection and data security?
Data protection refers to the policies and procedures put in place to ensure that personal data is handled in compliance with privacy laws and regulations. This includes obtaining consent for data collection, limiting access to personal information, and ensuring data is accurate and up to date. On the other hand, data security focuses on protecting data from unauthorized access, use, or destruction. This includes measures such as encryption, firewalls, and access controls to prevent data breaches and cyber attacks. In summary, data protection is about the legal and ethical handling of personal data, while data security is about the technical measures used to protect that data from unauthorized access or misuse.
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What is missing as a basis for social security?
A basis for social security is missing in terms of universal access and coverage. Many people around the world do not have access to social security programs, leaving them vulnerable to economic insecurity, poverty, and lack of basic necessities. Additionally, there is a lack of adequate funding and resources for social security programs, which hinders their effectiveness in providing support to those in need. Finally, there is a need for greater coordination and collaboration between governments, organizations, and communities to ensure that social security programs are comprehensive and inclusive for all individuals.
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