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Kenji Togashi - One of the best experts on this subject based on the ideXlab platform.
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Development of a structural growth curve model that considers the Causal Effect of initial phenotypes
Genetics Selection Evolution, 2019Co-Authors: Akio Onogi, Atsushi Ogino, Ayako Sato, Kazuhito Kurogi, Takanori Yasumori, Kenji TogashiAbstract:AbstractBackgroundGrowth curves have been widely used in genetic analyses to gain insights into the growth characteristics of both animals and plants. However, several questions remain unanswered, including how the initial phenotypes affect growth and what is the duration of any such impact. For beef cattle production in Japan, calves are procured from farms that specialize in reproduction and then moved to other farms where they are fattened to achieve their market/purchase value. However, the Causal Effect of growth, while calves are on the reproductive farms, on their growth during fattening remains unclear. To investigate this, we developed a model that combines a structural equation with a growth curve model. The Causal Effect was modeled with B-splines, which allows inference of the Effect as a curve. We fitted the proposed structural growth curve model to repeated measures of body weight from a Japanese beef cattle population (n = 3831) to estimate the curve of the Causal Effect of the calves’ initial weight on their trajectory of growth when they are on fattening farms.ResultsMaternal and reproduction farm Effects explained 26% of the phenotypic variance of initial weight at fattening farms. The structural growth curve model was fitted to remove the Effects of these factors in growth curve analysis at fattening farms. The estimated curve of Causal Effects remained at approximately 0.8 for 200 d after the calves entered the fattening farms, which means that 64% of the phenotypic variance was explained by the initial weight. Then, the Effect decreased linearly and disappeared approximately 620 d after entering the fattening farms, which corresponded to an average age of 871.5 d.ConclusionsThe proposed model is expected to provide more accurate estimates of genetic values for growth patterns because the confounding Causal factors such as maternal and reproduction farm Effects are removed. Moreover, examination of the inferred curve of the Causal Effect enabled us to estimate the Effect of a calf’s initial weight at arbitrary times during growth, which could provide suitable information for decision-making when shifting the time of slaughter, building models for genetic evaluation, and selecting calves for market.
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Development of a structural growth curve model that considers the Causal Effect of initial phenotypes
Genetics selection evolution : GSE, 2019Co-Authors: Akio Onogi, Atsushi Ogino, Ayako Sato, Kazuhito Kurogi, Takanori Yasumori, Kenji TogashiAbstract:Growth curves have been widely used in genetic analyses to gain insights into the growth characteristics of both animals and plants. However, several questions remain unanswered, including how the initial phenotypes affect growth and what is the duration of any such impact. For beef cattle production in Japan, calves are procured from farms that specialize in reproduction and then moved to other farms where they are fattened to achieve their market/purchase value. However, the Causal Effect of growth, while calves are on the reproductive farms, on their growth during fattening remains unclear. To investigate this, we developed a model that combines a structural equation with a growth curve model. The Causal Effect was modeled with B-splines, which allows inference of the Effect as a curve. We fitted the proposed structural growth curve model to repeated measures of body weight from a Japanese beef cattle population (n = 3831) to estimate the curve of the Causal Effect of the calves’ initial weight on their trajectory of growth when they are on fattening farms. Maternal and reproduction farm Effects explained 26% of the phenotypic variance of initial weight at fattening farms. The structural growth curve model was fitted to remove the Effects of these factors in growth curve analysis at fattening farms. The estimated curve of Causal Effects remained at approximately 0.8 for 200 d after the calves entered the fattening farms, which means that 64% of the phenotypic variance was explained by the initial weight. Then, the Effect decreased linearly and disappeared approximately 620 d after entering the fattening farms, which corresponded to an average age of 871.5 d. The proposed model is expected to provide more accurate estimates of genetic values for growth patterns because the confounding Causal factors such as maternal and reproduction farm Effects are removed. Moreover, examination of the inferred curve of the Causal Effect enabled us to estimate the Effect of a calf’s initial weight at arbitrary times during growth, which could provide suitable information for decision-making when shifting the time of slaughter, building models for genetic evaluation, and selecting calves for market.
Akio Onogi - One of the best experts on this subject based on the ideXlab platform.
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Development of a structural growth curve model that considers the Causal Effect of initial phenotypes
Genetics Selection Evolution, 2019Co-Authors: Akio Onogi, Atsushi Ogino, Ayako Sato, Kazuhito Kurogi, Takanori Yasumori, Kenji TogashiAbstract:AbstractBackgroundGrowth curves have been widely used in genetic analyses to gain insights into the growth characteristics of both animals and plants. However, several questions remain unanswered, including how the initial phenotypes affect growth and what is the duration of any such impact. For beef cattle production in Japan, calves are procured from farms that specialize in reproduction and then moved to other farms where they are fattened to achieve their market/purchase value. However, the Causal Effect of growth, while calves are on the reproductive farms, on their growth during fattening remains unclear. To investigate this, we developed a model that combines a structural equation with a growth curve model. The Causal Effect was modeled with B-splines, which allows inference of the Effect as a curve. We fitted the proposed structural growth curve model to repeated measures of body weight from a Japanese beef cattle population (n = 3831) to estimate the curve of the Causal Effect of the calves’ initial weight on their trajectory of growth when they are on fattening farms.ResultsMaternal and reproduction farm Effects explained 26% of the phenotypic variance of initial weight at fattening farms. The structural growth curve model was fitted to remove the Effects of these factors in growth curve analysis at fattening farms. The estimated curve of Causal Effects remained at approximately 0.8 for 200 d after the calves entered the fattening farms, which means that 64% of the phenotypic variance was explained by the initial weight. Then, the Effect decreased linearly and disappeared approximately 620 d after entering the fattening farms, which corresponded to an average age of 871.5 d.ConclusionsThe proposed model is expected to provide more accurate estimates of genetic values for growth patterns because the confounding Causal factors such as maternal and reproduction farm Effects are removed. Moreover, examination of the inferred curve of the Causal Effect enabled us to estimate the Effect of a calf’s initial weight at arbitrary times during growth, which could provide suitable information for decision-making when shifting the time of slaughter, building models for genetic evaluation, and selecting calves for market.
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Development of a structural growth curve model that considers the Causal Effect of initial phenotypes
Genetics selection evolution : GSE, 2019Co-Authors: Akio Onogi, Atsushi Ogino, Ayako Sato, Kazuhito Kurogi, Takanori Yasumori, Kenji TogashiAbstract:Growth curves have been widely used in genetic analyses to gain insights into the growth characteristics of both animals and plants. However, several questions remain unanswered, including how the initial phenotypes affect growth and what is the duration of any such impact. For beef cattle production in Japan, calves are procured from farms that specialize in reproduction and then moved to other farms where they are fattened to achieve their market/purchase value. However, the Causal Effect of growth, while calves are on the reproductive farms, on their growth during fattening remains unclear. To investigate this, we developed a model that combines a structural equation with a growth curve model. The Causal Effect was modeled with B-splines, which allows inference of the Effect as a curve. We fitted the proposed structural growth curve model to repeated measures of body weight from a Japanese beef cattle population (n = 3831) to estimate the curve of the Causal Effect of the calves’ initial weight on their trajectory of growth when they are on fattening farms. Maternal and reproduction farm Effects explained 26% of the phenotypic variance of initial weight at fattening farms. The structural growth curve model was fitted to remove the Effects of these factors in growth curve analysis at fattening farms. The estimated curve of Causal Effects remained at approximately 0.8 for 200 d after the calves entered the fattening farms, which means that 64% of the phenotypic variance was explained by the initial weight. Then, the Effect decreased linearly and disappeared approximately 620 d after entering the fattening farms, which corresponded to an average age of 871.5 d. The proposed model is expected to provide more accurate estimates of genetic values for growth patterns because the confounding Causal factors such as maternal and reproduction farm Effects are removed. Moreover, examination of the inferred curve of the Causal Effect enabled us to estimate the Effect of a calf’s initial weight at arbitrary times during growth, which could provide suitable information for decision-making when shifting the time of slaughter, building models for genetic evaluation, and selecting calves for market.
K Sudhir - One of the best experts on this subject based on the ideXlab platform.
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the Causal Effect of service satisfaction on customer loyalty
Management Science, 2021Co-Authors: Guofang Huang, K SudhirAbstract:We propose an instrumental-variable (IV) approach to estimate the Causal Effect of service satisfaction on customer loyalty by exploiting a common source of randomness in the assignment of service ...
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the Causal Effect of service satisfaction on customer loyalty
2019Co-Authors: Guofang Huang, K SudhirAbstract:We propose an instrumental-variable (IV) approach to estimate the Causal Effect of service satisfaction on customer loyalty, by exploiting a common source of randomness in the assignment of service employees to customers in service queues. Our approach can be applied at no incremental cost by using routine repeated cross-sectional customer survey data collected by firms. The IV approach addresses multiple sources of biases that pose challenges in estimating the Causal Effect using cross-sectional data: (i) the upward bias from common-method variance due to the joint measurement of service satisfaction and loyalty intent in surveys; (ii) the attenuation bias caused by measurement errors in service satisfaction; and (iii) the omitted-variable bias that may be in either direction. In contrast to the common concern about the upward common-method bias in the estimates using cross-sectional survey data, we find that ordinary-least-squares (OLS) substantially underestimates the casual Effect, suggesting that the downward bias due to measurement errors and/or omitted variables is dominant. The underestimation is even more significant with a behavioral measure of loyalty--where there is no common methods bias. This downward bias leads to significant underestimation of the positive profit impact from improving service satisfaction and can lead to under-investment by firms in service satisfaction. Finally, we find that the Causal Effect of service satisfaction on loyalty is greater for more difficult types of services.
Ayako Sato - One of the best experts on this subject based on the ideXlab platform.
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Development of a structural growth curve model that considers the Causal Effect of initial phenotypes
Genetics Selection Evolution, 2019Co-Authors: Akio Onogi, Atsushi Ogino, Ayako Sato, Kazuhito Kurogi, Takanori Yasumori, Kenji TogashiAbstract:AbstractBackgroundGrowth curves have been widely used in genetic analyses to gain insights into the growth characteristics of both animals and plants. However, several questions remain unanswered, including how the initial phenotypes affect growth and what is the duration of any such impact. For beef cattle production in Japan, calves are procured from farms that specialize in reproduction and then moved to other farms where they are fattened to achieve their market/purchase value. However, the Causal Effect of growth, while calves are on the reproductive farms, on their growth during fattening remains unclear. To investigate this, we developed a model that combines a structural equation with a growth curve model. The Causal Effect was modeled with B-splines, which allows inference of the Effect as a curve. We fitted the proposed structural growth curve model to repeated measures of body weight from a Japanese beef cattle population (n = 3831) to estimate the curve of the Causal Effect of the calves’ initial weight on their trajectory of growth when they are on fattening farms.ResultsMaternal and reproduction farm Effects explained 26% of the phenotypic variance of initial weight at fattening farms. The structural growth curve model was fitted to remove the Effects of these factors in growth curve analysis at fattening farms. The estimated curve of Causal Effects remained at approximately 0.8 for 200 d after the calves entered the fattening farms, which means that 64% of the phenotypic variance was explained by the initial weight. Then, the Effect decreased linearly and disappeared approximately 620 d after entering the fattening farms, which corresponded to an average age of 871.5 d.ConclusionsThe proposed model is expected to provide more accurate estimates of genetic values for growth patterns because the confounding Causal factors such as maternal and reproduction farm Effects are removed. Moreover, examination of the inferred curve of the Causal Effect enabled us to estimate the Effect of a calf’s initial weight at arbitrary times during growth, which could provide suitable information for decision-making when shifting the time of slaughter, building models for genetic evaluation, and selecting calves for market.
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Development of a structural growth curve model that considers the Causal Effect of initial phenotypes
Genetics selection evolution : GSE, 2019Co-Authors: Akio Onogi, Atsushi Ogino, Ayako Sato, Kazuhito Kurogi, Takanori Yasumori, Kenji TogashiAbstract:Growth curves have been widely used in genetic analyses to gain insights into the growth characteristics of both animals and plants. However, several questions remain unanswered, including how the initial phenotypes affect growth and what is the duration of any such impact. For beef cattle production in Japan, calves are procured from farms that specialize in reproduction and then moved to other farms where they are fattened to achieve their market/purchase value. However, the Causal Effect of growth, while calves are on the reproductive farms, on their growth during fattening remains unclear. To investigate this, we developed a model that combines a structural equation with a growth curve model. The Causal Effect was modeled with B-splines, which allows inference of the Effect as a curve. We fitted the proposed structural growth curve model to repeated measures of body weight from a Japanese beef cattle population (n = 3831) to estimate the curve of the Causal Effect of the calves’ initial weight on their trajectory of growth when they are on fattening farms. Maternal and reproduction farm Effects explained 26% of the phenotypic variance of initial weight at fattening farms. The structural growth curve model was fitted to remove the Effects of these factors in growth curve analysis at fattening farms. The estimated curve of Causal Effects remained at approximately 0.8 for 200 d after the calves entered the fattening farms, which means that 64% of the phenotypic variance was explained by the initial weight. Then, the Effect decreased linearly and disappeared approximately 620 d after entering the fattening farms, which corresponded to an average age of 871.5 d. The proposed model is expected to provide more accurate estimates of genetic values for growth patterns because the confounding Causal factors such as maternal and reproduction farm Effects are removed. Moreover, examination of the inferred curve of the Causal Effect enabled us to estimate the Effect of a calf’s initial weight at arbitrary times during growth, which could provide suitable information for decision-making when shifting the time of slaughter, building models for genetic evaluation, and selecting calves for market.
Atsushi Ogino - One of the best experts on this subject based on the ideXlab platform.
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Development of a structural growth curve model that considers the Causal Effect of initial phenotypes
Genetics Selection Evolution, 2019Co-Authors: Akio Onogi, Atsushi Ogino, Ayako Sato, Kazuhito Kurogi, Takanori Yasumori, Kenji TogashiAbstract:AbstractBackgroundGrowth curves have been widely used in genetic analyses to gain insights into the growth characteristics of both animals and plants. However, several questions remain unanswered, including how the initial phenotypes affect growth and what is the duration of any such impact. For beef cattle production in Japan, calves are procured from farms that specialize in reproduction and then moved to other farms where they are fattened to achieve their market/purchase value. However, the Causal Effect of growth, while calves are on the reproductive farms, on their growth during fattening remains unclear. To investigate this, we developed a model that combines a structural equation with a growth curve model. The Causal Effect was modeled with B-splines, which allows inference of the Effect as a curve. We fitted the proposed structural growth curve model to repeated measures of body weight from a Japanese beef cattle population (n = 3831) to estimate the curve of the Causal Effect of the calves’ initial weight on their trajectory of growth when they are on fattening farms.ResultsMaternal and reproduction farm Effects explained 26% of the phenotypic variance of initial weight at fattening farms. The structural growth curve model was fitted to remove the Effects of these factors in growth curve analysis at fattening farms. The estimated curve of Causal Effects remained at approximately 0.8 for 200 d after the calves entered the fattening farms, which means that 64% of the phenotypic variance was explained by the initial weight. Then, the Effect decreased linearly and disappeared approximately 620 d after entering the fattening farms, which corresponded to an average age of 871.5 d.ConclusionsThe proposed model is expected to provide more accurate estimates of genetic values for growth patterns because the confounding Causal factors such as maternal and reproduction farm Effects are removed. Moreover, examination of the inferred curve of the Causal Effect enabled us to estimate the Effect of a calf’s initial weight at arbitrary times during growth, which could provide suitable information for decision-making when shifting the time of slaughter, building models for genetic evaluation, and selecting calves for market.
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Development of a structural growth curve model that considers the Causal Effect of initial phenotypes
Genetics selection evolution : GSE, 2019Co-Authors: Akio Onogi, Atsushi Ogino, Ayako Sato, Kazuhito Kurogi, Takanori Yasumori, Kenji TogashiAbstract:Growth curves have been widely used in genetic analyses to gain insights into the growth characteristics of both animals and plants. However, several questions remain unanswered, including how the initial phenotypes affect growth and what is the duration of any such impact. For beef cattle production in Japan, calves are procured from farms that specialize in reproduction and then moved to other farms where they are fattened to achieve their market/purchase value. However, the Causal Effect of growth, while calves are on the reproductive farms, on their growth during fattening remains unclear. To investigate this, we developed a model that combines a structural equation with a growth curve model. The Causal Effect was modeled with B-splines, which allows inference of the Effect as a curve. We fitted the proposed structural growth curve model to repeated measures of body weight from a Japanese beef cattle population (n = 3831) to estimate the curve of the Causal Effect of the calves’ initial weight on their trajectory of growth when they are on fattening farms. Maternal and reproduction farm Effects explained 26% of the phenotypic variance of initial weight at fattening farms. The structural growth curve model was fitted to remove the Effects of these factors in growth curve analysis at fattening farms. The estimated curve of Causal Effects remained at approximately 0.8 for 200 d after the calves entered the fattening farms, which means that 64% of the phenotypic variance was explained by the initial weight. Then, the Effect decreased linearly and disappeared approximately 620 d after entering the fattening farms, which corresponded to an average age of 871.5 d. The proposed model is expected to provide more accurate estimates of genetic values for growth patterns because the confounding Causal factors such as maternal and reproduction farm Effects are removed. Moreover, examination of the inferred curve of the Causal Effect enabled us to estimate the Effect of a calf’s initial weight at arbitrary times during growth, which could provide suitable information for decision-making when shifting the time of slaughter, building models for genetic evaluation, and selecting calves for market.