Judul: Mixed effect models for high –dimensional longitudinal data with latent variables
Abstrak: A set of data that consist of repeated measurements on a large number of outcomes and covariates are known as high-dimensional longitudinal data. The high-dimensional longitudinal data may consist of several effects of interest that cannot be directly measured, known as latent factors or latent variables. During the last decade, the multivariate responses or high-dimensional in longitudinal data have been a big issue. The analysis of high-dimensional longitudinal data is complicated due to its complex correlation structures between outcomes. To understand changes over time of outcome variables, having correlations in individuals with explanatory variables is not enough. Hence, complex correlation structures between outcomes need to be considered. One approach that is commonly used to overcome high-dimensional longitudinal data is simultaneous equation modeling. A well-known simultaneous equation modeling method is the structural equation model (SEM). This approach has several appealing modeling abilities and can be used for high-dimensional longitudinal data. Under the SEM framework, the continuous time SEM is developed to avoid some issues associated with autoregressive and cross-lagged problems in SEM. Another simultaneous equation modeling method is by combining factor analysis and multivariate analysis methods to overcome high dimensional longitudinal data, namely latent factor linear mixed model (LFLMM). The factor analysis is used to reduce the high-dimensional outcomes, and the multivariate linear mixed model is used to study the longitudinal trends of several latent factors. One example of high-dimensional longitudinal data is the General Election Study. This study is carried out repeatedly to observe tendencies towards political attitudes and behavior over time in Belgium. The data contain political information, knowledge, perceptions, and preferences of a political party and the level of participation in politics. One of the most interesting things to study from the data is to analyze the change of political attitudes and behavior of respondents over time. Also, the relationship of changes in these outcomes is important to analyze. The General Election Study in Belgium was designed to include a representative sample of the target population under the Belgian electorate, so accurate estimates about the population could be made. This sampling design was created by the Institute for Social and Political Opinion (ISPO) and the Inter-university Center for Political Opinion Research (PIOP). The Belgian data set contained three subsamples, the Flemish (Dutch-speaking), the Walloon (French-speaking), and the Brussels Capital Region (Dutch and French-speaking). Several studies conducted on the Belgium data are carried out to understand the relationships between the latent variables Individualism (I), Ethnocentrism (E), and Authoritarianism (A) in Flanders. Cross-sectional or a longitudinal studies have also been carried out. In such cases, it is critical to capture the trend of the latent variables over time and, more importantly, whether there is any association or relationships between the development of nationalism (N), ethnocentrism (E), individualism (I), and authoritarianism (A) in Belgium. An empirical analysis of CT-SEM has been done to present the interdependencies among the four latent variables mentioned above on the basis of the General Election Studies for Belgium in 1991, 1995, and 1999 (Interuniversitair steunpunt politieke-opinieonderzoek, 1991, 1995, 1999). Although the four variables have been the subject of
Keyword: Belgium, CT-SEM, high-dimensional longitudinal data, LFLMM, Supplemented EM Algorithm

Judul: Analysis and Solving Outliers to Longitdinal Data
Abstrak: Suatu studi longitudinal dicirikan dengan percobaan yang pengukurannya dilakukan secara berulang antar waktu pada setiap individu. Pada studi ini dimungkinkan untuk mempelajari perubahan respon antar waktu beserta faktor- faktor yang mempengaruhi perubahan tersebut, baik pada level populasi maupun level individu. Metode yang dapat digunakan untuk analisis data longitudinal berbasis model linier adalah model linier campuran (GLMM). Metode pendugaan pada model linier campuran didasarkan pada asumsi bahwa pengaruh spesifik subyek dan galat intra-subyek menyebar normal. Namun, dalam berbagai kasus tidak jarang ditemui hal yang menyebabkan tidak terpenuhinya asumsi tersebut. Salah satu penyebab tidak terpenuhinya asumsi k arena ada pencilan pada data amatan. Pengamatan pencilan mengakibatkan kenormalan dari sebaran data menjadi terganggu, akibatnya matriks peragam akan kehilangan efisiensinya dan sifat penduga menjadi bias dan tidak konsisten (Yohai 2006 ). Pada data longitudinal terdapat dua jenis pencilan, yaitu pencilan pada galat intra-subyek dan pencilan pada pengaruh spesifik subjek
Keyword: longitudinal data, linear mixed models, outliers, robust estimation.

Judul: Karakterisasi Nira Kelapa Sawit (Elaesis guineensis, Jacq) yang Disadap Melalui Bunga Jantan dan Pohon Tumbang
Abstrak: Nira merupakan salah satu produk yang dihasilkan oleh pohon palma dan banyak digunakan dalam pembuatan gula merah dan minuman penyegar. Untuk meningkatkan hasil produksi nira, perlu ditingkatkan jumlah pohon palma penghasil nira atau mencari alternatif pohon palma lain sebagai sumber nira. Kelapa sawit (Elaeis guineensis, Jacq. ) merupakan salah satu diantaranya. Penelitian ini bertujuan untuk mencari metoda penyadapan nira yang paling baik dan melakukan karakterisasi nira. Penyadapan dilakukan melalui bunga jantan yang seludangnya belum membuka, melalui titik tumbuh pohon tumbang yang tidak mengalami pelayuan dan melalui titik tumbuh pchon tumbang yang mengalami pelayuan selama 2-3 minggu. Karakterisasi nira yang dilakukan meliputi pengukuran kadar air, kadar abu, total gula, total padatan terlarut, pH, to~al asam dan identifikasi gula. Sedangkan karakterisasi pada bagianbagian bunga jantan meliputi pengukuran kadar air, kadar abu, total gula, pH, total asam, kadar pati dan serat kasar.
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