Detecting response styles by using dual scaling of successive categories

A response style denotes a certain mapping of latent preferences to a rating scale that is common among a certain group of individuals. For example, individuals from the same country may assign high ratings to the majority of objects regardless of the specific preferences for the objects. The existence of response styles causes problems in international and cross-cultural research as it makes it hard to compare findings. Moreover, even within homogeneous samples, response styles make it difficult to expose the underlying preference structure. Detecting the existence and influence of a response style is typically a difficult issue as the underlying preferences are not directly observable. Hence, we can never be sure if the observed ratings are the result of a response style or an adequate representation of the preferences. In this paper, we consider the use of dual scaling as a tool to detect the existence of a response style. By means of a simulation study, we assess the performance of the proposed method

Predicting inspection outcomes and evaluating port state control targeting using random forests

This study uses global inspection data of 790k inspections and 1.5 million deficiencies (2013 to 2021) which is complemented by 500k incidents and ship particulars of 132k unique vessels. The results show that over 70% of ships that had very serious and serious incidents (2020 to 2021) were not inspected and only 2.5% were detained. The global averages of percentage of inspections without deficiencies is around 50% with high variability across the port state control (PSC) regimes (2013 to 2021). Since there is ample room for improvement to target risky vessels for inspection, it is not recommended to continue with the status quo of the industry by using detention alone as proxy to target future risk. Instead, the study develops 13 prediction models for detention and deficiency types using ML methods by evaluating over 400 risk factors. The results vary across the endpoint of interest but overall, the normal random forests variants outperform the other variants. The top 5 most influential covariates towards prediction are found to be the size of the vessel (GRT), age, previous number of deficiencies within 365 days prior to the inspection, the year of existence of the beneficial owner and safety manager company. These prediction models can be combined with incident type models to enhance targeting of risky vessels and reduce future incidents compared to the current status quo of 70% false negative events

Predicting detention and deficiencies using random forests

The aim of this exploration study is to predict detention and twelve deficiency types which can be used to enhance port state control targeting as well as domain awareness for coastal administrations. A total of 234 combinations of random forest variants are explored evaluating over 400 covariates. The study uses a comprehensive and unique, global inspection dataset of over 200k inspections and 400k deficiencies (2014 to 2019) and out of sample data from 2020 to 2021 for evaluation. The results show that based on the used data, normal random forests outperform other variants and overall detention has the highest decile lift with 3 or higher compared to random selection. This is followed by the deficiency groups safety of navigation, certificates and qualification and the Maritime Labor Convention. Deficiencies related to newer areas such as MARPOL Annex VI, ballast water treatment and anti-fouling are more difficult to predict and are also more difficult to detect compared to other areas where detection often depend on the training and background of inspectors. Future work will evaluate further model variants and evaluate inspection policies by filtering out high risk vessels that were missed

Generalized canonical correlation analysis with missing values

Generalized canonical correlation analysis is a versatile technique that allows the joint analysis of several sets of data matrices. The generalized canonical correlation analysis solution can be obtained through an eigenequation and distributional assumptions are not required. When dealing with multiple set data, the situation frequently occurs that some values are missing. In this paper, two new methods for dealing with missing values in generalized canonical correlation analysis are introduced. The first approach, which does not require iterations, is a generalization of the Test Equating method available for principal component analysis. In the second approach, missing values are imputed in such a way that the generalized canonical correlation analysis objective function does not increase in subsequent steps. Convergence is achieved when the value of the objective function remains constant. By means of a simulation study, we assess the performance of the new methods. We compare the results with those of two available methods; the missing-data passive method, introduced in Gifi's homogeneity analysis framework, and the GENCOM algorithm developed by Green and Carroll. An application using world bank data is used to illustrate the proposed methods

Generalized canonical correlation analysis of matrices with different row and column orders

A Method is offered that makes it possible to apply generalized canonical correlations analysis (CANCOR) to two or more matrices of different row and column order. The new method optimizes the generalized canonical correlation analysis objective by considering only the observed values. This is achieved by employing selection matrices. We present and discuss fit measures to assess the quality of the solutions. In a simulation study we assess the performance of our new method and compare it to an existing procedure called GENCOM, proposed by Green and Carroll. We find that our new method outperforms the GENCOM algorithm both with respect to model fit and recovery of the true structure. Moreover, as our new method does not require any type of iteration it is easier to implement and requires less computation. We illustrate the method by means of an example concerning the relative positions of the political parties in the Netherlands based on provincial data.Generalized canonical correlation analysis, perceptual mapping

Generalized canonical correlation analysis with missing values

Two new methods for dealing with missing values in generalized canonical correlation analysis are introduced. The first approach, which does not require iterations, is a generalization of the Test Equating method available for principal component analysis. In the second approach, missing values are imputed in such a way that the generalized canonical correlation analysis objective function does not increase in subsequent steps. Convergence is achieved when the value of the objective function remains constant. By means of a simulation study, we assess the performance of the new methods. We compare the results with those of two available methods; the missing-data passive method, introduced Gifi's homogeneity analysis framework, and the GENCOM algorithm developed by Green and Carroll

CAR: A MATLAB Package to Compute Correspondence Analysis with Rotations

Correspondence analysis (CA) is a popular method that can be used to analyse relationships between categorical variables. Like principal component analysis, CA solutions can be rotated both orthogonally and obliquely to simple structure without affecting the total amount of explained inertia. We describe a MATLAB package for computing CA. The package includes orthogonal and oblique rotation of axes. It is designed not only for advanced users of MATLAB but also for beginners. Analysis can be done using a user-friendly interface, or by using command lines. We illustrate the use of CAR with one example.