title: Strengthening responsive and reciprocal relationships in a whānau tangata centre: A summary

authors: Clarkin-Phillips, Jeanette

subjects: New Zealand

abstract: Although the kindergarten teaching team members were excited at being a part of the parent support and development initiative, they were also aware that they would face challenges that would affect their practice. Their commitment to ensuring effective teaching and learning at Taitoko Kindergarten led them to seek evidence-based teaching practices, inside the vision of the parent support and development initiative. Their involvement with this TLRI research project has helped to provide this.
Through action research, the research team has addressed the teachers’ questions associated with (a) the strengthening of relationships (how this has been done so far, and how might it be furthered); (b) changes in practice (what is possible, what appears to be effective); (c) diversity (how a range of “funds of knowledge” (González, Moll, & Amanti, 2005) can be shared and included in curriculum); and (d) documentation (how this can engage families and learners).

text: Strengthening responsive and reciprocal relationships in a whānau tangata centre: A summary Although the kindergarten teaching team members were excited at being a part of the parent support and development initiative, they were also aware that they would face challenges that would affect their practice. Their commitment to ensuring effective teaching and learning at Taitoko Kindergarten led them to seek evidence-based teaching practices, inside the vision of the parent support and development initiative. Their involvement with this TLRI research project has helped to provide this.
Through action research, the research team has addressed the teachers’ questions associated with (a) the strengthening of relationships (how this has been done so far, and how might it be furthered); (b) changes in practice (what is possible, what appears to be effective); (c) diversity (how a range of “funds of knowledge” (González, Moll, & Amanti, 2005) can be shared and included in curriculum); and (d) documentation (how this can engage families and learners).

year: 2009

title: Using finite mixtures to robustify statistical models

authors: Rohan, Maheswaran

subjects: Robust Statistics

abstract: This thesis is concerned with robust estimation of the parameters of statistical models. Although robust estimation is a very good idea, it has some shortcomings when seen from the statistical modelling point of view. For example, there are no easily applicable principles for creating robust estimates in new situations. In this thesis, we are trying to introduce a unified method to obtain the robustified statistics for various situations such as the linear model and the generalized linear model. We wish to modify the maximum likelihood estimation procedure, which is very sensitive to the outliers. In order to reduce the effect on these estimates by outliers, we add an additional component, which would be of no interest but would contain all outliers, to the regular component forming a finite mixture. In fact, we use the finite mixture model to obtain a ``robustified'' estimate for a model parameter $\theta$, where the finite mixture form is being used as a mathematical tool to have a tractable form of analysis rather than being used as a serious model for the data. We employ the EM algorithm to obtain the our proposed robustified estimates for the parameters. Our estimates are compared with some other estimates defined in the robust statistics literature. This thesis examines the robustness of the proposed estimates using the concept of influence function. The estimates are defined iteratively, so that the implicit differentiation method of Jorgensen is used to obtain the influence functions of the estimates. We give example plots of these influence functions which are bounded. In this thesis, we give mathematical results for all cases and we use well known real data sets to investigate our method. The statistical software R is used for all investigation. Finally, we hope that this method may give a unified approach for making parameter estimation in statistical models more robust.

text: Using finite mixtures to robustify statistical models This thesis is concerned with robust estimation of the parameters of statistical models. Although robust estimation is a very good idea, it has some shortcomings when seen from the statistical modelling point of view. For example, there are no easily applicable principles for creating robust estimates in new situations. In this thesis, we are trying to introduce a unified method to obtain the robustified statistics for various situations such as the linear model and the generalized linear model. We wish to modify the maximum likelihood estimation procedure, which is very sensitive to the outliers. In order to reduce the effect on these estimates by outliers, we add an additional component, which would be of no interest but would contain all outliers, to the regular component forming a finite mixture. In fact, we use the finite mixture model to obtain a ``robustified'' estimate for a model parameter $\theta$, where the finite mixture form is being used as a mathematical tool to have a tractable form of analysis rather than being used as a serious model for the data. We employ the EM algorithm to obtain the our proposed robustified estimates for the parameters. Our estimates are compared with some other estimates defined in the robust statistics literature. This thesis examines the robustness of the proposed estimates using the concept of influence function. The estimates are defined iteratively, so that the implicit differentiation method of Jorgensen is used to obtain the influence functions of the estimates. We give example plots of these influence functions which are bounded. In this thesis, we give mathematical results for all cases and we use well known real data sets to investigate our method. The statistical software R is used for all investigation. Finally, we hope that this method may give a unified approach for making parameter estimation in statistical models more robust.

year: 2011

title: Ngā maihi o Te Whare Hukahuka, ngā maihi o Te Whare o Ruamano: Te whare kōrero o ngā maihi o Tuteao

authors: Hona, Hakahaka Charlie Barlow

subjects: Tuteao

abstract: Ko Pūtauaki te maunga
Ko Rangitāiki te awa
Ko Hekerangi te papa whenua
Ko Tuteao te marae
Ko Ngā Maihi te hapū
Ko Ngāti Awa te iwi
Ko Mataatua te waka
Tihe mauri-ora
Ko tōku kāwai rangatira tēnei. E mōhio ai te tangata ko wai ahau; nō hea ahau, nā wai hoki ahau. Ā, nā whai anō taku urupounamu, ko wai a Ngā Maihi o Tuteao? E mea ana ngā pākeke, he iwi tawhito a Ngā Maihi i heke mai i ngā tātai whakapapa o Tangaroa, o Pōtiki, o Mahutapoanui, o Toroa, o Tuteao. Hēoi, kua kore e rangona ōna kōrero i runga marae ātea. Kua kore e wānangahia i roto i te poho o te whare o Tuteao. Kua ngū ngā poupou o te whare kōrero. Ka mutu, i roto i ngā tau tata nei kua riro tītapu ngā puna o te kī, ngā whītiki o te kupu, o te hapū; kua kore e rangona ngā kōrero o Ngā Maihi i runga marae ātea. Ko te haumate o te reo i runga o Tuteao tētahi take i pēnei ai. Nō reira, kai te imu te ruhi, kai te imu te ngēngē. E noho pōhara ana a Ngā Maihi ki ngā tāonga o tōna whare kōrero. Hei te tau 2027, ka whakanuia te rautau o te whare tīpuna a Tuteao, ā i tēnei tau tonu 2022, e whai whakaaro ana te hapū ki taua kaupapa otirā ki te hanga whare tīpuna hou; he whare kaitā e uru ai te tokomaha. Nō reira, e whakaaro ana hoki ki ngā kōrero ka whakairohia ki ngā pakitara o te whare. I tēnei tau hoki kua mana te whakaako i ngā kōrero hītori o te Māori i roto i ngā kura, nō reira he aha rā ngā kōrero e wātea ana ki ngā tamariki o ngā kura e pā ana ki a Ngā Maihi? He maramara, he kongakonga, he putunga kōrero e noho marara ana i ngā wāhi tūmatanui. He uaua te kitea o ētahi atu, kei ngā wāhi tūmataiti, pēnei i te Kōti Whenua Māori, i ngā tuhinga ā-whānau, kei ngā pukapuka wānanga o te marae. Nō reira, he uaua te kite atu i tōna whānuitanga, i tōna hohonutanga. Tīmata ake i ngā kohinga kōrero o te hapū tonu o Ngā Maihi, i ōna kaumātua, i ōna wānanga, i ōna whānau. I tua atu i wā te whare pukapuka, kei ngā wāhi tūmataiti ētahi putunga kōrero hei ō mo te whare kōrero o Ngā Maihi o Tuteao. Ka mutu, kei ngā waiata, ngā haka, ngā mōteatea. Kai ngā kōrero hoki o iwi kē, e pā ana ki wō mātou tīpuna. I reira i tōtō mai i te kōrero.

text: Ngā maihi o Te Whare Hukahuka, ngā maihi o Te Whare o Ruamano: Te whare kōrero o ngā maihi o Tuteao Ko Pūtauaki te maunga
Ko Rangitāiki te awa
Ko Hekerangi te papa whenua
Ko Tuteao te marae
Ko Ngā Maihi te hapū
Ko Ngāti Awa te iwi
Ko Mataatua te waka
Tihe mauri-ora
Ko tōku kāwai rangatira tēnei. E mōhio ai te tangata ko wai ahau; nō hea ahau, nā wai hoki ahau. Ā, nā whai anō taku urupounamu, ko wai a Ngā Maihi o Tuteao? E mea ana ngā pākeke, he iwi tawhito a Ngā Maihi i heke mai i ngā tātai whakapapa o Tangaroa, o Pōtiki, o Mahutapoanui, o Toroa, o Tuteao. Hēoi, kua kore e rangona ōna kōrero i runga marae ātea. Kua kore e wānangahia i roto i te poho o te whare o Tuteao. Kua ngū ngā poupou o te whare kōrero. Ka mutu, i roto i ngā tau tata nei kua riro tītapu ngā puna o te kī, ngā whītiki o te kupu, o te hapū; kua kore e rangona ngā kōrero o Ngā Maihi i runga marae ātea. Ko te haumate o te reo i runga o Tuteao tētahi take i pēnei ai. Nō reira, kai te imu te ruhi, kai te imu te ngēngē. E noho pōhara ana a Ngā Maihi ki ngā tāonga o tōna whare kōrero. Hei te tau 2027, ka whakanuia te rautau o te whare tīpuna a Tuteao, ā i tēnei tau tonu 2022, e whai whakaaro ana te hapū ki taua kaupapa otirā ki te hanga whare tīpuna hou; he whare kaitā e uru ai te tokomaha. Nō reira, e whakaaro ana hoki ki ngā kōrero ka whakairohia ki ngā pakitara o te whare. I tēnei tau hoki kua mana te whakaako i ngā kōrero hītori o te Māori i roto i ngā kura, nō reira he aha rā ngā kōrero e wātea ana ki ngā tamariki o ngā kura e pā ana ki a Ngā Maihi? He maramara, he kongakonga, he putunga kōrero e noho marara ana i ngā wāhi tūmatanui. He uaua te kitea o ētahi atu, kei ngā wāhi tūmataiti, pēnei i te Kōti Whenua Māori, i ngā tuhinga ā-whānau, kei ngā pukapuka wānanga o te marae. Nō reira, he uaua te kite atu i tōna whānuitanga, i tōna hohonutanga. Tīmata ake i ngā kohinga kōrero o te hapū tonu o Ngā Maihi, i ōna kaumātua, i ōna wānanga, i ōna whānau. I tua atu i wā te whare pukapuka, kei ngā wāhi tūmataiti ētahi putunga kōrero hei ō mo te whare kōrero o Ngā Maihi o Tuteao. Ka mutu, kei ngā waiata, ngā haka, ngā mōteatea. Kai ngā kōrero hoki o iwi kē, e pā ana ki wō mātou tīpuna. I reira i tōtō mai i te kōrero.

year: 2022