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Innovations in human-centered biomedical informatics are often developed with the eventual goal of real-world translation. While biomedical research questions are usually answered in terms of how a method performs in a particular context, we argue that it is equally important to consider and formally evaluate the ethical implications of informatics solutions. Several new research paradigms have arisen as a result of the consideration of ethical issues, including but not limited for privacy-preserving computation and fair machine learning. In the spirit of the Pacific Symposium on Biocomputing, we discuss broad and fundamental principles of ethical biomedical informatics in terms of Olelo Noeau, or Hawaiian proverbs and poetical sayings that capture Hawaiian values. While we emphasize issues related to privacy and fairness in particular, there are a multitude of facets to ethical biomedical informatics that can benefit from a critical analysis grounded in ethics.

Point estimation can be used in both predictive and descriptive data mining tasks. Three classical point estimation methods — the method of moments, maximum likelihood estimation, and the Expectation-Maximization algorithm — are discussed in this chapter, followed by a review of measurements of estimation performance. This chapter intends to introduce basic concepts and methods of point estimation. These concepts and methods are the basis for more advanced estimation techniques.

The following sections are included:

• INTRODUCTION

• POINT ESTIMATION
  • Point Estimate, Estimator, and Estimation
  • Four Important Properties of Estimators
  • Mean-Squared Error for Choosing Point Estimator

• INTERVAL ESTIMATION

• INTERVAL ESTIMATES FOR μ WHEN IS KNOWN
  • Confidence Intervals in Terms of 20 Samples
  • Sandbags We Can Have Real Confidence In: 95 Percent and 99 Percent Confidence Intervals
  • 95 Percent Confidence Interval for the Sandbag Sample with a Smaller Sample Size
  • 95 Percent Confidence Interval for the Mean External Audit Fees for 32 Diverse Companies

• CONFIDENCE INTERVALS FOR μ WHEN IS UNKNOWN
  • 95 Percent Confidence Interval for the Average Weight of Football Players
  • 90 Percent Confidence Interval for the Average Weight of Football Players
  • Estimate for Waiting Time at a Bank
  • 95 Percent Confidence Interval for the True Mean Incremental Profit of “Successful” Trade Promotion

• CONFIDENCE INTERVALS FOR THE POPULATION PROPORTION
  • 95 Percent Confidence Interval for Voting Proportion
  • 95 Percent Confidence Interval for Commodity Preference Proportion
  • 95 Percent Confidence Interval for the Proportion of Working Adults Who Use Computer Equipment

• CONFIDENCE INTERVALS FOR THE VARIANCE
  • Confidence Intervals for

• AN OVERVIEW OF STATISTICAL QUALITY CONTROL⁵
  • The Sample Size of an Inspection
  • Acceptance Sampling and Its Alternative Plans
  • Process Control

• CONTROL CHARTS FOR QUALITY CONTROL
  • -Chart
  • -Chart and S-Chart

• FURTHER APPLICATIONS
  • Using Interval Estimates to Evaluate Donors and Donations Models
  • Shoppers’ Attitudes Toward Shoplifting and Shoplifting Prevention Devices

• Summary

• Appendix 10A Control Chart Approach for Cash Management¹⁶

• Appendix 10B Using MINITAB to Generate Control Charts

• Questions and Problems

One of the most important objectives of statistical inference is to estimate unknown model parameters based on an observed data. In this chapter, we will introduce some fundamental estimation methods for distributional model parameters. In particular, the maximum likelihood estimation and the method of moments estimation/generalized method of moments estimation are discussed, and their asymptotic properties investigated. Then we will discuss the methods for evaluating parameter estimators using the mean squared error criterion. The Lagrange multiplier method and the Cramer-Rao lower bound are used to derive the best unbiased estimators.

This paper examines the performance of a new probability model developed in terms of the surface area and velocity of an aero-structure, and presents some relevant statistical inferences. Several examples will compare the robustness of three kinds of point estimators developed for the lift coefficient. Four different types of confidence intervals for the lift coefficient are also developed, and it is shown that the shortest length confidence interval is an UMA confidence interval. Using the probability model, the correlation coefficient between lift and velocity, and the relevant regression slope are computed. The inferences and the results of this paper can be used particularly in the designing process of an aero-structure, especially when there is some uncertainty about its surface area; this provides some flexibilities for aerodynamicists in determining some of the unknown design characteristics.

Artificial Intelligence (AI) algorithms showcase the potential to steer a paradigm shift in clinical medicine, especially medical imaging. Concerns associated with model generalizability and biases necessitate rigorous external validation of AI algorithms prior to their adoption into clinical workflows. To address the barriers associated with patient privacy, intellectual property, and diverse model requirements, we introduce ClinValAI, a framework for establishing robust cloud-based infrastructures to clinically validate AI algorithms in medical imaging. By featuring dedicated workflows for data ingestion, algorithm scoring, and output processing, we propose an easily customizable method to assess AI models and investigate biases. Our novel orchestration mechanism facilitates utilizing the complete potential of the cloud computing environment. ClinValAI’s input auditing and standardization mechanisms ensure that inputs consistent with model prerequisites are provided to the algorithm for a streamlined validation. The scoring workflow comprises multiple steps to facilitate consistent inferencing and systematic troubleshooting. The output processing workflow helps identify and analyze samples with missing results and aggregates final outputs for downstream analysis. We demonstrate the usability of our work by evaluating a state-of-the-art breast cancer risk prediction algorithm on a large and diverse dataset of 2D screening mammograms. We perform comprehensive statistical analysis to study model calibration and evaluate performance on important factors, including breast density, age, and race, to identify latent biases. ClinValAI provides a holistic framework to validate medical imaging models and has the potential to advance the development of generalizable AI models in clinical medicine and promote health equity.