In 2023, the number of missing person files in the United States equaled 563,389 cases, an increase from 2021 which had the lowest number of missing person files in the U.S. since 1990.

In 2024, there were 301,623 cases filed by the National Crime Information Center (NCIC) where the race of the reported missing person was white. In the same year, 17,097 people whose race was unknown were also reported missing in the United States. What is the NCIC? The National Crime Information Center (NCIC) is a digital database that stores crime data for the United States, so criminal justice agencies can access it. As a part of the FBI, it helps criminal justice professionals find criminals, missing people, stolen property, and terrorists. The NCIC database is broken down into 21 files. Seven files belong to stolen property and items, and 14 belong to persons, including the National Sex Offender Register, Missing Person, and Identify Theft. It works alongside federal, tribal, state, and local agencies. The NCIC’s goal is to maintain a centralized information system between local branches and offices, so information is easily accessible nationwide. Missing people in the United States A person is considered missing when they have disappeared and their location is unknown. A person who is considered missing might have left voluntarily, but that is not always the case. The number of the NCIC unidentified person files in the United States has fluctuated since 1990, and in 2022, there were slightly more NCIC missing person files for males as compared to females. Fortunately, the number of NCIC missing person files has been mostly decreasing since 1998.

Missing Persons Statistics........................

The Missing Person Information Clearinghouse was established July 1, 1985, within the Department of Public Safety providing a program for compiling, coordinating and disseminating information in relation to missing persons and unidentified body/persons. Housed within the Division of Criminal Investigation, the clearinghouse assists in helping to locate missing persons through public awareness and cooperation, and in educating law enforcement officers and the general public about missing person issues.

While the fear of being kidnapped may persist for one’s entire life, in 2022 the number of missing persons under the age of 21 was much higher than those 21 and over, with 206,371 females under 21 reported missing, and 64,956 females over the age of 21 reported missing.

Why people go missing

There are many reasons why people go missing; some are kidnapped, some purposefully go missing - in order to escape abuse, for example - and some, usually children, are runaways. What persists in the imagination when thinking of missing persons, however, are kidnapping victims, usually due to extensive media coverage of child kidnappings by the media.

Demographics of missing persons

While the number of missing persons in the United States fluctuates, in 2021, this number was at its lowest since 1990. Additionally, while it has been observed that there is more media coverage in the United States of white missing persons, almost half of the missing persons cases in 2022 were of minorities.

Number of homicide victims, by Indigenous identity (total, by Indigenous identity; Indigenous identity; First Nations (North American Indian); Métis; Inuk (Inuit); Indigenous person, Indigenous group unknown; non-Indigenous identity; unknown Indigenous identity) and missing person status (total, by missing person status; missing; not missing; missing person status unknown), Canada, 2015 to 2024.

This study reexamined and recoded missing data in the Uniform Crime Reports (UCR) for the years 1977 to 2000 for all police agencies in the United States. The principal investigator conducted a data cleaning of 20,067 Originating Agency Identifiers (ORIs) contained within the Offenses-Known UCR data from 1977 to 2000. Data cleaning involved performing agency name checks and creating new numerical codes for different types of missing data including missing data codes that identify whether a record was aggregated to a particular month, whether no data were reported (true missing), if more than one index crime was missing, if a particular index crime (motor vehicle theft, larceny, burglary, assault, robbery, rape, murder) was missing, researcher assigned missing value codes according to the "rule of 20", outlier values, whether an ORI was covered by another agency, and whether an agency did not exist during a particular time period.

NamUs is the only national repository for missing, unidentified, and unclaimed persons cases. The program provides a singular resource hub for law enforcement, medical examiners, coroners, and investigating professionals. It is the only national database for missing, unidentified, and unclaimed persons that allows limited access to the public, empowering family members to take a more proactive role in the search for their missing loved ones.

Missing data is an inevitable aspect of every empirical research. Researchers developed several techniques to handle missing data to avoid information loss and biases. Over the past 50 years, these methods have become more and more efficient and also more complex. Building on previous review studies, this paper aims to analyze what kind of missing data handling methods are used among various scientific disciplines. For the analysis, we used nearly 50.000 scientific articles that were published between 1999 and 2016. JSTOR provided the data in text format. Furthermore, we utilized a text-mining approach to extract the necessary information from our corpus. Our results show that the usage of advanced missing data handling methods such as Multiple Imputation or Full Information Maximum Likelihood estimation is steadily growing in the examination period. Additionally, simpler methods, like listwise and pairwise deletion, are still in widespread use.

An adequate imputation of missing data would significantly preserve the statistical power and avoid erroneous conclusions. In the era of big data, machine learning is a great tool to infer the missing values. The root means square error (RMSE) and the proportion of falsely classified entries (PFC) are two standard statistics to evaluate imputation accuracy. However, the Cox proportional hazards model using various types requires deliberate study, and the validity under different missing mechanisms is unknown. In this research, we propose supervised and unsupervised imputations and examine four machine learning-based imputation strategies. We conducted a simulation study under various scenarios with several parameters, such as sample size, missing rate, and different missing mechanisms. The results revealed the type-I errors according to different imputation techniques in the survival data. The simulation results show that the non-parametric “missForest” based on the unsupervised imputation is the only robust method without inflated type-I errors under all missing mechanisms. In contrast, other methods are not valid to test when the missing pattern is informative. Statistical analysis, which is improperly conducted, with missing data may lead to erroneous conclusions. This research provides a clear guideline for a valid survival analysis using the Cox proportional hazard model with machine learning-based imputations.

The dataset contains the state-wise number of persons reported missing in a particular year, the total number of persons missing including those from previous years, the number of persons recovered/traced and those unrecovered/untraced. The dataset also contains the percentage recovery of missing persons which is calculated as the percentage share of total number of persons traced over the total number of persons missing. NCRB started providing detailed data on missing & traced persons including children from 2016 onwards following the Supreme Court’s direction in a Writ Petition. It should also be noted that the data published by NCRB is restricted to those cases where FIRs have been registered by the police in respective States/UTs.

Note: Figures for projected_mid_year_population are sourced from the Report of the Technical Group on Population Projections for India and States 2011-2036

Under Section 8 of the Missing Persons Act, 2018, police services are required to report annually on their use of urgent demands for records under the Act and the Ministry of the Solicitor General is required to make the OPP’s annual report data publicly available. The data includes: * year in which the urgent demands were reported * category of records * description of records accessed under each category * total number of times each category of records was demanded * total number of missing persons investigations which had urgent demands for records * total number of urgent demands for records made by OPP in a year.

In 2023 alone, ****** persons have been reported as missing in Mexico, a figure that implies about ** people going missing every day. The volume of these incidents soared after Felipe Calderon's government declared the war on drugs in December 2006, an event that marked a surged in violence throughout the Latin American country.

The monitoring of surface-water quality followed by water-quality modeling and analysis is essential for generating effective strategies in water resource management. However, water-quality studies are limited by the lack of complete and reliable data sets on surface-water-quality variables. These deficiencies are particularly noticeable in developing countries.

This work focuses on surface-water-quality data from Santa Lucía Chico river (Uruguay), a mixed lotic and lentic river system. Data collected at six monitoring stations are publicly available at https://www.dinama.gub.uy/oan/datos-abiertos/calidad-agua/. The high temporal and spatial variability that characterizes water-quality variables and the high rate of missing values (between 50% and 70%) raises significant challenges.

To deal with missing values, we applied several statistical and machine-learning imputation methods. The competing algorithms implemented belonged to both univariate and multivariate imputation methods (inverse distance weighting (IDW), Random Forest Regressor (RFR), Ridge (R), Bayesian Ridge (BR), AdaBoost (AB), Huber Regressor (HR), Support Vector Regressor (SVR), and K-nearest neighbors Regressor (KNNR)).

IDW outperformed the others, achieving a very good performance (NSE greater than 0.8) in most cases.

In this dataset, we include the original and imputed values for the following variables:

Water temperature (Tw)

Dissolved oxygen (DO)

Electrical conductivity (EC)

pH

Turbidity (Turb)

Nitrite (NO2-)

Nitrate (NO3-)

Total Nitrogen (TN)

Each variable is identified as [STATION] VARIABLE FULL NAME (VARIABLE SHORT NAME) [UNIT METRIC].

More details about the study area, the original datasets, and the methodology adopted can be found in our paper https://www.mdpi.com/2071-1050/13/11/6318.

If you use this dataset in your work, please cite our paper: Rodríguez, R.; Pastorini, M.; Etcheverry, L.; Chreties, C.; Fossati, M.; Castro, A.; Gorgoglione, A. Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach. Sustainability 2021, 13, 6318. https://doi.org/10.3390/su13116318

The number of missing persons cases and the number of cases solved within Nantou County.

Stata do-files and data to support tutorial "Sensitivity Analysis for Not-at-Random Missing Data in Trial-Based Cost-Effectiveness Analysis" (Leurent, B. et al. PharmacoEconomics (2018) 36: 889).Do-files should be similar to the code provided in the article's supplementary material.Dataset based on 10 Top Tips trial, but modified to preserve confidentiality. Results will differ from those published.

This document provides a clear and practical guide to understanding missing data mechanisms, including Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR). Through real-world scenarios and examples, it explains how different types of missingness impact data analysis and decision-making. It also outlines common strategies for handling missing data, including deletion techniques and imputation methods such as mean imputation, regression, and stochastic modeling.Designed for researchers, analysts, and students working with real-world datasets, this guide helps ensure statistical validity, reduce bias, and improve the overall quality of analysis in fields like public health, behavioral science, social research, and machine learning.

Ministry of Home Affairs, Government of India has defined missing child as 'a person below eighteen years of age, whose whereabouts are not known to the parents, legal guardians and any other persons who may be legally entrusted with the custody of the child, whatever may be the circumstances/causes of disappearance”. The dataset contains the state wise and gender-wise number of children reported missing in a particular year, total number of persons missing including those from previous years, number of persons recovered/traced and those unrecovered/untraced. The dataset also contains the percentage recovery of missing persons which is calculated as the percentage share of total number of persons traced over the total number of persons missing. NCRB started providing detailed data on missing & traced persons including children from 2016 onwards following the Supreme Court’s direction in a Writ Petition. It should also be noted that the data published by NCRB is restricted to those cases where FIRs have been registered by the police in respective States/UTs.

Example data sets and computer code for the book chapter titled "Missing Data in the Analysis of Multilevel and Dependent Data" submitted for publication in the second edition of "Dependent Data in Social Science Research" (Stemmler et al., 2015). This repository includes the computer code (".R") and the data sets from both example analyses (Examples 1 and 2). The data sets are available in two file formats (binary ".rda" for use in R; plain-text ".dat").

The data sets contain simulated data from 23,376 (Example 1) and 23,072 (Example 2) individuals from 2,000 groups on four variables:

ID = group identifier (1-2000) x = numeric (Level 1) y = numeric (Level 1) w = binary (Level 2)

In all data sets, missing values are coded as "NA".

Financial overview and grant giving statistics of Missing Children Global Network