Checchin Andrea*
Robust statistical methods have gained significant attention in recent years due to their ability to provide reliable results in the presence of outliers and model deviations. This review aims to explore various robust techniques, their theoretical foundations, and practical applications across different domains. By categorizing methods into parametric, non-parametric, and semi-parametric approaches, we will analyze their strengths and limitations. The review concludes with recommendations for future research directions in robust statistics. In statistical analysis, traditional methods often rely on assumptions of normality and homoscedasticity. However, real-world data frequently violate these assumptions, leading to unreliable results. Robust statistical methods have emerged as essential tools to handle these violations effectively. This article reviews various robust techniques, their applications, and their performance relative to classical methods.
Maertens Kim*
Binomial regression, a specialized form of regression analysis, is essential for modeling binary outcomes, where the response variable has two possible outcomes. This review article provides a comprehensive exploration of binomial regression, detailing its theoretical underpinnings, practical applications, model fitting techniques, and interpretation of results. By synthesizing existing literature and presenting case studies, this guide aims to serve as a valuable resource for researchers across various fields, including biostatistics, social sciences, and econometrics. In many fields of research, particularly in the health sciences, social sciences, and econometrics, researchers encounter binary response variables. For instance, outcomes such as success/failure, yes/no, and present/absent are prevalent. Traditional linear regression is inadequate for such data due to its assumptions about the distribution of the residuals and the nature of the response variable. Binomial regression addresses these limitations by employing a logistic link function, allowing researchers to model probabilities directly
Zhou Wang*
Hypothesis testing is a cornerstone of statistical inference in modern research across various disciplines. This article reviews the principles and practices of hypothesis testing, examining its historical evolution, foundational concepts, methodological approaches, and contemporary challenges. By exploring the nuances of null and alternative hypotheses, significance levels, p-values, confidence intervals, and the impact of statistical power, this review aims to provide a comprehensive understanding of hypothesis testing in the context of scientific inquiry. Furthermore, the implications of misinterpretations and the ongoing debates regarding statistical practices are discussed, emphasizing the need for robust methodologies and transparency in research.
Hadvina Zubair*
Microarray technology has revolutionized the field of genomics by enabling the simultaneous analysis of thousands of genes in a single experiment. This review explores the principles, methodologies, applications and future directions of microarray studies, highlighting their significance in understanding gene expression patterns, disease mechanisms, and therapeutic interventions. By synthesizing recent advancements and addressing challenges in data analysis and interpretation, this article provides a comprehensive overview of how microarray studies are unlocking the secrets of gene expression. Gene expression profiling is crucial for understanding cellular functions and the underlying mechanisms of diseases. Traditional methods for studying gene expression, such as Northern blotting and Reverse Transcription-Polymerase Chain Reaction (RT-PCR), are limited by their capacity to analyze only a handful of genes at a time. Microarray technology addresses this limitation by allowing researchers to measure the expression levels of thousands of genes simultaneously, thus offering a broader and more comprehensive view of gene activity within a biological sample. The invention of microarrays in the late 1990s marked a significant turning point in molecular biology. Since then, the technology has evolved significantly, driven by advances in both the design of arrays and the sophistication of data analysis techniques. This review article aims to provide a detailed overview of microarray studies, discussing their principles, methodologies, applications, challenges, and future prospects.
Gusenbauer Wang*
The advent of the digital age has led to an exponential increase in the volume, velocity, and variety of data generated daily. This phenomenon, often referred to as "big data," presents both opportunities and challenges for organizations. In this review article, we explore the landscape of big data analytics, focusing on effective techniques for managing and extracting insights from large datasets. We will examine the technologies underpinning big data analytics, the methodologies employed, and the implications for businesses in various sectors. The concept of big data refers to datasets that are so large or complex that traditional data processing software is inadequate to handle them. This data deluge stems from numerous sources, including social media, IoT devices, online transactions, and more. According to a report by IBM, 2.5 quintillion bytes of data are created every day, and this volume continues to grow. As organizations seek to harness the power of big data, understanding effective analytics techniques becomes crucial for deriving actionable insights. This review discusses the fundamental aspects of big data analytics, including key technologies, methodologies, and real- world applications. By navigating the complexities of big data, organizations can gain a competitive advantage, enhance decision-making processes, and foster innovation.
Helina Dreon*
In the rapidly evolving landscape of data science, "Statistical Methods for the 21st Century: Innovations and Applications" emerges as a vital resource for researchers, practitioners, and students alike. This comprehensive review explores the key themes, methodologies, and applications presented in the book, while evaluating its contribution to the field of statistics in contemporary research. The book is structured into several sections that address both foundational statistical principles and innovative methodologies. Each chapter is written by experts in the field, ensuring that the content is not only rigorous but also relevant to current trends in data analysis. The initial chapters provide a refresher on traditional statistical methods, such as hypothesis testing, regression analysis, and Bayesian statistics. These concepts are contextualized within modern applications, demonstrating their continued relevance. The authors emphasize the importance of understanding these foundations as they serve as the bedrock for more advanced techniques. One of the most compelling aspects of the book is its focus on innovation. The authors introduce a range of new methodologies that have emerged in response to the challenges posed by big data and complex datasets. Techniques such as machine learning, ensemble methods, and advanced Bayesian approaches are discussed in detail. Each chapter explores the theoretical underpinnings of these methods, alongside practical applications in various fields, including health, finance, and social sciences
Jalin Karima*
Survival analysis is a branch of statistics that deals with the analysis of time-to-event data. It has applications across various fields, including medicine, engineering, social sciences, and economics. Time-to-event data, often referred to as survival data, involves the time until a specific event occurs, such as death, failure, or relapse. The unique characteristics of this type of data necessitate specialized techniques and models that can appropriately handle censoring and the non-normality of the distribution of survival times. At the core of survival analysis is time-to-event data, which measures the duration until an event of interest occurs. For example, in clinical trials, researchers may examine the time until a patient experiences an adverse event or reaches a specific health milestone. Censoring occurs when the event of interest has not been observed for some subjects during the study period. This can happen for various reasons, such as loss to follow-up or the study ending before the event occurs. Censoring is a critical concept in survival analysis because traditional statistical methods that assume complete data can lead to biased results.
Brassard Zhao*
The advent of biometric technologies has revolutionized the ways in which personal identification and authentication are conducted. From fingerprint scanning to facial recognition and iris detection, biometrics offers a level of security that traditional methods, such as passwords and PINs, often cannot match. However, the storage and use of biometric data raise significant ethical concerns, particularly regarding privacy, consent, and data security. This article explores the ethical implications of biometric data storage, emphasizing the need to balance security with the preservation of individual privacy rights.
Journal of Biometrics & Biostatistics received 3496 citations as per Google Scholar report
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