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http://dbpedia.org/ontology/abstract Least-squares spectral analysis (LSSA) is … Least-squares spectral analysis (LSSA) is a method of estimating a frequency spectrum, based on a least squares fit of sinusoids to data samples, similar to Fourier analysis. Fourier analysis, the most used spectral method in science, generally boosts long-periodic noise in long gapped records; LSSA mitigates such problems. Unlike with Fourier analysis, data need not be equally spaced to use LSSA. LSSA is also known as the Vaníček method or the Gauss-Vaniček method after Petr Vaníček, and as the Lomb method or the Lomb–Scargle periodogram, based on the contributions of Nicholas R. Lomb and, independently, Jeffrey D. Scargle.mb and, independently, Jeffrey D. Scargle. , Lombs Periodogram, eller Lomb-Scargle Peri … Lombs Periodogram, eller Lomb-Scargle Periodogram, är en metod för att skatta frekvensspektrum för data som inte är samplade med jämnt intervall . Antag att data är , tillgängliga vid tidpunkter för . Spektrumet vid kan då skattas genom att beräkna där definieras med hjälp av . Scargle visade att den här skattningen är statistiskt ekvivalent med ett vanligt periodogram vid jämnt samplad data . Lomb visade i sin tur att skattningen är ekvivalent med en minsta-kvadrat skattningen vid varje frekvens , med andra ord ges samma resultat genom att först beräkna , , samt och spektrumet kan sedan skattas genom att beräkna , där .an sedan skattas genom att beräkna , där . , 最小平方頻譜分析法（英語：Least-squares spectral analys … 最小平方頻譜分析法（英語：Least-squares spectral analysis）是一種利用最小平方法尋找適配於資料點之最佳正弦曲線，以估算頻譜的方法。其數學原理與科學界中最常用的傅立葉分析相似。一般而言，傅立葉分析會將間隔較長之訊號的長周期雜訊放大，而最小平方頻譜分析法則解決了這個問題。 最小平方頻譜分析法也稱為凡尼切克法（Vaníček method）、隆布法（Lomb method）或隆布—史卡構法（Lomb–Scargle method），分別取名自對其有所貢獻的、尼可拉斯·隆布（Nicholas R. Lomb）以及傑佛瑞·史卡構（Jeffrey D. Scargle）。此外，麥可·科恩伯格（Michael Korenberg）、史考特·陳（Scott Chen）以及等人也曾開發出與之關係密切的其它方法。rg）、史考特·陳（Scott Chen）以及等人也曾開發出與之關係密切的其它方法。
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rdfs:comment Lombs Periodogram, eller Lomb-Scargle Peri … Lombs Periodogram, eller Lomb-Scargle Periodogram, är en metod för att skatta frekvensspektrum för data som inte är samplade med jämnt intervall . Antag att data är , tillgängliga vid tidpunkter för . Spektrumet vid kan då skattas genom att beräkna där definieras med hjälp av . Scargle visade att den här skattningen är statistiskt ekvivalent med ett vanligt periodogram vid jämnt samplad data . Lomb visade i sin tur att skattningen är ekvivalent med en minsta-kvadrat skattningen vid varje frekvens , med andra ord ges samma resultat genom att först beräkna , , samt , där .t genom att först beräkna , , samt , där . , 最小平方頻譜分析法（英語：Least-squares spectral analys … 最小平方頻譜分析法（英語：Least-squares spectral analysis）是一種利用最小平方法尋找適配於資料點之最佳正弦曲線，以估算頻譜的方法。其數學原理與科學界中最常用的傅立葉分析相似。一般而言，傅立葉分析會將間隔較長之訊號的長周期雜訊放大，而最小平方頻譜分析法則解決了這個問題。 最小平方頻譜分析法也稱為凡尼切克法（Vaníček method）、隆布法（Lomb method）或隆布—史卡構法（Lomb–Scargle method），分別取名自對其有所貢獻的、尼可拉斯·隆布（Nicholas R. Lomb）以及傑佛瑞·史卡構（Jeffrey D. Scargle）。此外，麥可·科恩伯格（Michael Korenberg）、史考特·陳（Scott Chen）以及等人也曾開發出與之關係密切的其它方法。rg）、史考特·陳（Scott Chen）以及等人也曾開發出與之關係密切的其它方法。 , Least-squares spectral analysis (LSSA) is … Least-squares spectral analysis (LSSA) is a method of estimating a frequency spectrum, based on a least squares fit of sinusoids to data samples, similar to Fourier analysis. Fourier analysis, the most used spectral method in science, generally boosts long-periodic noise in long gapped records; LSSA mitigates such problems. Unlike with Fourier analysis, data need not be equally spaced to use LSSA.ta need not be equally spaced to use LSSA.
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