Analisa Perbandingan Kinerja Keuangan Bank Mandiri (Persero) Tbk, Bank Central Asia (Persero) Tbk, Dan Bank Cimb Niaga (Persero) Tbk.

Bank merupakan lembaga keuangan dengan fungsi pokok menyimpan dana dari masyarakat dalam bentuk tabungan dan menyalurkan kembali kepada masyarakat dalam bentuk kredit untuk meningkatkan taraf hidup rakyat banyak.Laporan laba rugi, neraca, ekuitas pemilik, dan laporan arus kas adalah laporan dasar yang perlu dianalisis untuk kepentingan Perusahaan itu sendiri, dari laporan dasar departemen keuangan dapat membuat analisis keuangan yang lain dengan menggunakan data untuk menentukan rasio kinerja keuangan. Penelitian ini bertujuan untuk memberikan bukti empiris tentang perbandingan kinerja keuangan Bank Mandiri, Bank Central Asia, Bank Cimb Niaga pada periode 2012-2014 dengan menggunakan rasio keuangan (LDR, DER, ROA, ROE, NPM) dengan alat analisis yang digunakan dalam penelitian ini yaitu Independent Sample T test, dari rasio 5 variabel yang digunakan menunjukan bahwa hipotesis ditolak karena terdapat perbedaan namun tidak signifikan antara kinerja keuangan Bank Mandiri, Bank Central Asia, dan Bank Cimb Niaga.Sebaiknya Bank Mandiri,Bank BCA,Bank CIMB Niaga meningkatkan kinerja keuangan agar dapat menarik perhatian para investor sehingga dapat mempertahankan predikatnya sebagai bank yang memiliki asset terbesat di Indonesia. Kata kunci: perbandingan kinerja bank, rasio keuanga

Large Dimensional Independent Component Analysis: Statistical Optimality and Computational Tractability

In this paper, we investigate the optimal statistical performance and the impact of computational constraints for independent component analysis (ICA). Our goal is twofold. On the one hand, we characterize the precise role of dimensionality on sample complexity and statistical accuracy, and how computational consideration may affect them. In particular, we show that the optimal sample complexity is linear in dimensionality, and interestingly, the commonly used sample kurtosis-based approaches are necessarily suboptimal. However, the optimal sample complexity becomes quadratic, up to a logarithmic factor, in the dimension if we restrict ourselves to estimates that can be computed with low-degree polynomial algorithms. On the other hand, we develop computationally tractable estimates that attain both the optimal sample complexity and minimax optimal rates of convergence. We study the asymptotic properties of the proposed estimates and establish their asymptotic normality that can be readily used for statistical inferences. Our method is fairly easy to implement and numerical experiments are presented to further demonstrate its practical merits

Tensor Methods in High Dimensional Data Analysis: Opportunities and Challenges

Large amount of multidimensional data represented by multiway arrays or tensors are prevalent in modern applications across various fields such as chemometrics, genomics, physics, psychology, and signal processing. The structural complexity of such data provides vast new opportunities for modeling and analysis, but efficiently extracting information content from them, both statistically and computationally, presents unique and fundamental challenges. Addressing these challenges requires an interdisciplinary approach that brings together tools and insights from statistics, optimization and numerical linear algebra among other fields. Despite these hurdles, significant progress has been made in the last decade. This review seeks to examine some of the key advancements and identify common threads among them, under eight different statistical settings