Background/Objective: Accurate biological profile estimation from skeletal remains is fundamental to forensic identification. While the humerus demonstrates considerable sexual dimorphism, population-specific validation data for Thai populations remain limited. This study aimed to develop and independently validate population-specific equations for sex and stature estimation from humeral measurements in Northeastern Thai populations. Methods: This cross-sectional study examined 300 adult humeri (150 male, 150 female) from the Khon Kaen University skeletal collection. Four osteometric measurements (maximum length, midshaft circumference, epicondylar breadth, superior–inferior head diameter) and weight were recorded. The sample was randomly divided into development (n = 200) and validation (n = 100) datasets. Logistic regression for sex estimation and linear regression for stature estimation were developed using stepwise selection. Results: Sex classification achieved 93.5% accuracy in development and 93.0% in independent validation. The optimal model incorporated midshaft circumference, superior–inferior head diameter, and weight, with an area under the curve of 0.977 (95% CI: 0.953–1.000), sensitivity 90.0%, specificity 96.0%, and Cohen’s kappa 0.86. Stature estimation demonstrated a correlation coefficient of 0.81 with a mean absolute error of 4.36 cm (2.74% of the mean stature). Independent validation confirmed minimal performance deterioration for both models. Conclusions: These independently validated, population-specific equations provide accurate and reliable methods for biological profile estimation in Northeastern Thai forensic contexts. The rigorous validation framework supports confident operational application and provides a methodological model for developing regional forensic standards.

Background: Sex estimation from skeletal remains is important for forensic identification, but many methodologies focus on complete elements despite high fragmentation rates in operational contexts. The aim of this study was to develop and validate discriminant function equations for sex estimation between complete and fragmented Thai femora. Materials and Methods: A total of 560 adult femora (280 males and 280 females) were used for measurements of eight osteometric variables. Then, discriminant function analysis was applied to complete femora and anatomically isolated segments, including proximal, diaphyseal, and distal, with leave-one-out cross-validation. Results: All measurements showed significant sexual dimorphism, with percentage differences ranging from 6.56% to 42.27%. Complete femur stepwise analysis achieved 90.47% accuracy by using four optimally selected variables, performing comparably to eight-variable models. Isolated segment accuracies varied substantially: proximal segments achieved 89.64% accuracy, differing by only 0.83 percentage points from complete performance; distal segments demonstrated 86.25% accuracy from bicondylar width alone; and diaphyseal segments achieved 80.88%. Combined segment approaches demonstrated synergistic effects approaching complete femur performance. Conclusions: These population-specific equations provided validated methodologies for sex estimation from complete and fragmentary Thai femora. Anatomical region selection still maintained high classification accuracy despite skeletal incompleteness, in line with the fragmentary conditions commonly encountered in forensic and archeological contexts.

Background/Objective: Sex estimation from cranial remains is essential in forensic biological profile reconstruction, yet systematic comparisons of statistical classifiers under formally verified assumption conditions remain limited in Thai populations. This study aimed to develop and validate sex estimation models from craniofacial measurements in a Northeastern Thai skeletal sample using DFA, BLR, and SVM, and to examine whether distributional assumption violations influence classifier performance and generalizability under holdout validation. Methods: Eight linear craniometric parameters connecting five osteometric landmarks were measured in 300 adult skeletal specimens (150 males, 150 females) from the Unit of Human Bone Warehouse for Research, Khon Kaen University. Specimens were allocated into a training dataset (n = 200) and a holdout dataset (n = 100). Five multivariate model configurations were developed under direct-entry and stepwise parameter-entry strategies. Formal assumption diagnostics included Shapiro–Wilk normality testing, Levene’s test, Box’s M test, and variance inflation factor analysis. Results: All parameters demonstrated excellent interobserver reliability and significant sex-based differences (p < 0.01), with males recording larger mean values throughout. Apparent multivariate accuracy ranged from 78.0% to 85.5%. Holdout validation revealed consistent performance decline across all five configurations, with accuracy reductions of 3.0 to 9.5 percentage points and AUC reductions of 0.042 to 0.079. The stepwise DFA model showed the smallest overall accuracy decline, although this apparent stability reflected an uneven sex-specific trade-off, with a specificity decline offset by a sensitivity gain, while SVM and DFA direct-entry models showed the largest decline. Conclusions: Craniofacial measurements provide moderate discriminatory capacity for sex estimation in Northeastern Thai skeletal remains. Although univariate assumption violations were observed, DFA’s performance remained comparable to BLR and SVM under the present large, balanced sample; multivariate analysis or distributional assumption-free methods offer practical alternatives where violations are present. Apparent performance alone is insufficient to establish forensic reliability, and holdout validation should be regarded as a necessary component of sex estimation model development.