title: Maximal Associated Regression: A nonlinear extension to Least Angle Regression

authors: Abeysekera, Sanush K.

abstract: This paper proposes Maximal Associated Regression (MAR), a novel algorithm that performs forward stage-wise regression by applying nonlinear transformations to fit predictor covariates. For each predictor, MAR selects between a linear or additive fit as determined by the dataset. The proposed algorithm is an adaptation of Least Angle Regression (LARS) and retains its efficiency in building sparse models. Constrained penalized splines are used to generate smooth nonlinear transformations for the additive fits. A monotonically constrained extension of MAR (MARm) is also introduced in this paper to fit isotonic regression problems. The proposed algorithms are validated on both synthetic and real datasets. The performances of MAR and MARm are compared against LARS, Generalized Linear Models (GLM), and Generalized Additive Models (GAM) under the Gaussian assumption with a unity link function. Results indicate that MAR-type algorithms achieve a superior subset selection accuracy, generating sparser models that generalize well to new data. MAR is also able to generate models for sample deficient datasets. Thus, MAR is proposed as a valuable tool for subset selection and data exploration, especially when a priori knowledge of the dataset is unavailable.

text: Maximal Associated Regression: A nonlinear extension to Least Angle Regression This paper proposes Maximal Associated Regression (MAR), a novel algorithm that performs forward stage-wise regression by applying nonlinear transformations to fit predictor covariates. For each predictor, MAR selects between a linear or additive fit as determined by the dataset. The proposed algorithm is an adaptation of Least Angle Regression (LARS) and retains its efficiency in building sparse models. Constrained penalized splines are used to generate smooth nonlinear transformations for the additive fits. A monotonically constrained extension of MAR (MARm) is also introduced in this paper to fit isotonic regression problems. The proposed algorithms are validated on both synthetic and real datasets. The performances of MAR and MARm are compared against LARS, Generalized Linear Models (GLM), and Generalized Additive Models (GAM) under the Gaussian assumption with a unity link function. Results indicate that MAR-type algorithms achieve a superior subset selection accuracy, generating sparser models that generalize well to new data. MAR is also able to generate models for sample deficient datasets. Thus, MAR is proposed as a valuable tool for subset selection and data exploration, especially when a priori knowledge of the dataset is unavailable.

year: 2021

title: 'Crimes Against International Peace' and International Law [Book review]

authors: Boister, Neil

abstract: This article reviews Kirsten Sellars' book, “Crimes against peace” and international law.

text: 'Crimes Against International Peace' and International Law [Book review] This article reviews Kirsten Sellars' book, “Crimes against peace” and international law.

year: 2014

title: What works for midwives supporting māmā through emotional distress?

authors: Roia, Tiaan Mereana Manurangi

abstract: Pregnancy and the postnatal period are particularly vulnerable times for mothers’ mental health, with an increased risk of women experiencing depression and anxiety during this time. Therefore, understanding the level and type of service midwives provide to māmā who experience emotional distress is vital to know what midwives need in order to provide the right care to their clients.
The aim of this study was to find out what tools and resources community-based midwives find most helpful to support the needs of māmā. Seven semi-structured interviews were conducted, including four Māori midwives, who were able to provide insight on their experiences caring for mothers with mental health needs. Overall, the interviews reflected a strong sense of care and empathy from midwives during their time caring for mothers. Midwives appeared to rely on their interpersonal skills and intuition about emotional distress, and emphasised the importance of connecting with people and utilising support from their communities. Whilst midwives were interested and engaged with providing appropriate care to mothers, they were unbeknown to some of the clinical resources that could be available to their clients. The complexity of referrals and access to mental health services was central to the midwives’ frustration around caring for mothers, as well as a lack of education relating to kaupapa Māori, cultural competency, and mental health processes.
Recommendations based on this study are to provide midwives with education and training on topics like kaupapa Māori, on social and mental health resources and processes for referral to increase their ability to support and care for mothers who experience emotional distress. Improvements to the pathways for referrals and access to mental health services is essential. Future research should focus on understanding why midwives aren’t using a range of online apps and tools, and increase awareness of the available modes of support available.

text: What works for midwives supporting māmā through emotional distress? Pregnancy and the postnatal period are particularly vulnerable times for mothers’ mental health, with an increased risk of women experiencing depression and anxiety during this time. Therefore, understanding the level and type of service midwives provide to māmā who experience emotional distress is vital to know what midwives need in order to provide the right care to their clients.
The aim of this study was to find out what tools and resources community-based midwives find most helpful to support the needs of māmā. Seven semi-structured interviews were conducted, including four Māori midwives, who were able to provide insight on their experiences caring for mothers with mental health needs. Overall, the interviews reflected a strong sense of care and empathy from midwives during their time caring for mothers. Midwives appeared to rely on their interpersonal skills and intuition about emotional distress, and emphasised the importance of connecting with people and utilising support from their communities. Whilst midwives were interested and engaged with providing appropriate care to mothers, they were unbeknown to some of the clinical resources that could be available to their clients. The complexity of referrals and access to mental health services was central to the midwives’ frustration around caring for mothers, as well as a lack of education relating to kaupapa Māori, cultural competency, and mental health processes.
Recommendations based on this study are to provide midwives with education and training on topics like kaupapa Māori, on social and mental health resources and processes for referral to increase their ability to support and care for mothers who experience emotional distress. Improvements to the pathways for referrals and access to mental health services is essential. Future research should focus on understanding why midwives aren’t using a range of online apps and tools, and increase awareness of the available modes of support available.

year: 2023